handbook of robotics chapter 19: force and tactile sensors...

Handbook of Robotics

Chapter 19: Force and Tactile Sensors

Chs. 5,8,15 POINTERS Chs. 28,29,30,32,33,34,35

DRAFT 11/5/07

Mark R. CutkoskyDepartment of Mechanical Engineering

Stanford UniversityStanford, CA 94305-2232, USA

Robert D. HoweDivision of Engineering and Applied Sciences

Harvard UniversityCambridge, MA 02138, USA

William R. ProvancherDepartment of Mechanical Engineering

University of UtahSalt Lake City, UT 84112-9208, USA

November 5, 2007

Contents

20 Force and Tactile Sensors 120.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120.2 Sensor Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

20.2.1 Proprioceptive and proximity sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220.2.2 Other contact sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420.2.3 Kinematic sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520.2.4 Force and load sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520.2.5 Dynamic tactile sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520.2.6 Array sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

20.3 Tactile Information Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1020.3.1 Tactile Information Flow: Means and ends of tactile sensing . . . . . . . . . . . . . . . . . . . 1020.3.2 Solid mechanics and deconvolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1220.3.3 Curvature and shape information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1420.3.4 Object and surface identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1520.3.5 Active sensing strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1520.3.6 Dynamic sensing and event detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1620.3.7 Integration of thermal and other sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

20.4 Integration Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1620.5 Conclusions and Future Developments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

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Chapter 20

Force and Tactile Sensors

This chapter provides an overview of force and tac-tile sensing, with the primary emphasis placed on tactilesensing. We begin by presenting some basic considera-tions in choosing a tactile sensor and then review of awide variety of sensor types, including proximity, kine-matic, force, dynamic, contact, skin deflection, thermaland pressure sensors. We also review various transduc-tion methods, appropriate for each general sensor type.We consider the information that these various types ofsensors provide in terms of whether they are most usefulfor manipulation, surface exploration or being responsiveto contacts from external agents.

Concerning the interpretation of tactile information,we describe the general problems and present two shortillustrative examples. The first involves intrinsic tac-tile sensing, i.e., estimating contact locations and forcesfrom force sensors. The second involves contact pressuresensing, i.e., estimating surface normal and shear stress

Force and Tactile Sensors (Mark R. Cutkosky + Robert D. Howe + William R. Provancher)

OUTLINE DRAFT 1/6/06 RH

1 Introduction (assigned to Mark – putting place holders in for content under each subsection)

1.1 Big picture and context Tactile sensing has been a component of robotics for roughly as long as vision. However, in comparison to vision, for which great strides have been made in terms of hardware and software and which is now widely used in industrial and mobile robot applications, tactile sensing always seems to be “a few years away” from widespread utility. Therefore, before reviewing the technologies and approaches available it is worthwhile to consider some basic questions:

• How important is tactile sensing? • What is it useful for? • Why does it remain comparatively undeveloped?

In Nature, tactile sensing is evidently an essential survival tool. Even the simplest creatures are endowed with large numbers of mechanoreceptors for exploring and responding to various stimuli. In humans, tactile sensing is indispensable for manipulation, exploration and response. A couple of quick thought exercises illustrate the point: When our fingers are numbed by cold we become clumsy, so that simple manipulation tasks, like unbuckling a boot, are an exercise in frustration. Our muscles, snug in our coat sleeves, are only slightly affected but our cutaneous mechanoreceptors

are anesthetized. For exploration, we rapidly assimilate tactile information about material and surface properties (e.g., hardness, thermal conductivity, friction, roughness) to help us identify objects. We may have difficulty distinguishing leather from pleather™ by sight, but not by touch! The importance of tactile response, whether to a gentle touch or an impact, is seen in the damage that patients with peripheral neuropathy (e.g., as a complication of diabetes) accidentally do to themselves. Contemplating alternative terminology for “RESPONSE” in Table I. Considering adjectives like reactive, encountered response, cognoscente, or some form of communication

Manipulation: Grasp force control; contact locations and kinematics; stability assessment.

Exploration: Surface texture, friction and hardness; thermal properties; local features.

Response: Detection and reaction to contacts from external agents.

Figure 20.1: Uses of tactile sensing in robotics

distributions from an array of sensors in an elastic skin.We conclude with a brief discussion of the challenges

that remain to be solved in packaging and manufacturingdamage-tolerant tactile sensors.

20.1 Introduction

Tactile sensing has been a component of robotics forroughly as long as vision. However, in comparison tovision, for which great strides have been made in termsof hardware and software and which is now widelyused in industrial and mobile robot applications, tactilesensing always seems to be a few years away fromwidespread utility. Therefore, before reviewing thetechnologies and approaches available it is worthwhileto consider some basic questions:

• How important is tactile sensing?• What is it useful for?• Why does it remain comparatively undeveloped?

In Nature, tactile sensing is an essential survival tool.Even the simplest creatures are endowed with large num-bers of mechanoreceptors for exploring and respondingto various stimuli. In humans, tactile sensing is indis-pensable for three distinct kinds of activities: manip-ulation, exploration and response. The importance oftactile sensing for manipulation is most evident for finemotor tasks. When we are chilled, tasks like buttoninga shirt can become an exercise in frustration. The prob-lem is primarily a lack of sensing; our muscles, snug inour coat sleeves, are only slightly affected but our cuta-neous mechanoreceptors are anesthetized and we becomeclumsy. For exploration, we continually assimilate tactileinformation about materials and surface properties (e.g.,hardness, thermal conductivity, friction, roughness) tohelp us identify objects. We may have difficulty dis-

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CHAPTER 20. FORCE AND TACTILE SENSORS 2

tinguishing real leather from synthetic leather by sight,but not by touch. Finally, the importance of tactile re-sponse, whether to a gentle touch or an impact, is seenin the damage that patients with peripheral neuropathy(e.g., as a complication of diabetes) accidentally do tothemselves.

As Figure 20.1 indicates, the same functional cate-gories apply to robots. However, in comparison to an-imals, with thousands of mechanoreceptors per squarecentimeter of skin, even the most sophisticated robotsare impoverished. One reason for the slow developmentof tactile sensing technology as compared to vision isthat there is no tactile analog to the CCD or CMOSoptical array. Instead, tactile sensors elicit informationthrough physical interaction. They must be incorpo-rated into skin surfaces with compliance, for conforminglocally to surfaces, and with adequate friction for han-dling objects securely. The sensors and skin must alsobe robust enough to survive repeated impacts and abra-sions. And unlike the image plane in a camera, tactilesensors must be distributed over the robot appendages,with particularly high concentrations in areas such as thefingertips. The wiring of tactile sensors is consequentlyanother formidable challenge.

Nonetheless, considerable progress in tactile sensor de-sign and deployment has been made over the last twentyyears. decades. In the following sections we review themain functional classes of tactile sensors and discuss theirrelative strengths and limitations. Looking ahead, newfabrication techniques offer the possibility of artificialskin materials with integrated sensors and local process-ing for interpreting sensor signals and communicatingover a common buss to reduce wiring.

There is an extensive literature describing touch sens-ing research. Recent general reviews include [1] and [2]and these cite several older reviews from the 1980s and1990s.

20.2 Sensor Types

This section outlines five main types of sensors: pro-prioceptive, kinematic, force, dynamic tactile, and arraytactile sensors. A basic review of the first three of theseis provided along with contact sensors that provide ther-mal or material composition data. However, greater em-phasis placed on tactile sensors that provide mechanore-ception. When considering tactile sensors, it is useful tobegin by considering the fundamental physical quantitiesthat can only be sensed through contact with the envi-

ronment. The most important quantities measured withtouch sensors are shape and force. Each of these maybe measured either as average quantity for some part ofthe robot or as a spatially resolved, distributed quantityacross a contact area. In this chapter we follow the con-vention of studies of the human sense of touch and usethe term “touch sensing” to refer to the combination ofthese two modes. Devices that measure an average orresultant quantity are sometimes referred to as “inter-nal” or “intrinsic” sensors. The basis for these sensorsis force sensing, which precedes the discussion of tactilearray sensors.

20.2.1 Proprioceptive and proximitysensing

Proprioceptive sensing refers to sensors that provide in-formation about the net force or motion of an appendage,analogous to receptors that provide information in hu-mans about tendon tensions or joint movements. Gen-erally speaking the primary source for spatial proprio-ceptive information on a robot is provided by joint angleand force-torque sensors. Since joint angle sensors suchas potentiometers, encoders, and resolvers are well estab-lished technologies, they do not warrant discussion here.Instead, a brief review of proximity sensing via whiskersand antennae as well as non-contact proximity sensing isprovided. Force-torque sensors are discussed in greaterdetail in section 20.2.4.

Whisker and antenna sensors

Whisker or antenna sensors are in essence a hybrid of pro-prioceptive and tactile information. This form of sensingwas first explored in the early 90’s. For example, Russell[25] developed a whisker sensor with base angle sensorand tip contact sensor that was attached to a robot armto explore its environment. Another example by Kaneko,et al. [26] is one of the earliest examples of active an-tenna sensing. Kaneko, et al. affixed a rigid spring steelantenna to a 1-DOF rotating axis used to sweep the an-tenna side-to-side similar to the method an insect wouldemploy. The sweeping motion, in combination with ajoint angle sensor and torque sensor, was used to as-sess encountered contacts. Clements and Rahn [27] tooka similar approach to Kaneko, but added an extra de-gree of freedom to the sweeping pattern of their whisker.Clements and Rahn used a motor driven gimbal to drivetheir spring steel whisker in 2-DOF to explore objects.Cowan, et al. [28] used a multi-segmented piezoresistive


Table 20.1: Tactile sensor modalities and common transduction types.Sensor Sensor TypeModality and Attributes Advantages DisadvantagesNormal Pressure

Piezoresistive Array (e.g., [3, 4, 5, 6, 7, 8])• Array of piezoresistive junctions • Simple signal conditioning • Temperature sensitive

• Embedded in a elastomeric skin • Simple design • Frail

• Cast or screen printed • Suitable for mass produc-tion

• Signal drift and hysteresis

Capacitive Array (e.g., [9, 10, 11, 12, 13])• Array of capacitive junctions • Good sensitivity • Complex circuitry

• Row and column electrodes sepa-rated by elastomeric dielectric

• Moderate hysteresis, de-pending on construction

Piezoresistive MEMS Array (e.g., [14, 15])• Silicon micro-machined arraywith doped silicon strain gaugedflexures

• Suitable for mass produc-tion

• Frail

Optical (e.g., [16])• Combined tracking of opticalmarkers with a constitutive model

• No interconnects to break • Requires PC for comput-ing applied forces

Skin deformationOptical (e.g., [17])• Fluid-filled elastomeric membrane • Compliant membrane • Complex computations

• Tracking of optical markers in-scribed on membrane coupled withenergy minimization algorithm

• No electrical interconnectsto be damaged

• Hard to customize sensor

Magnetic (e.g., [18])• Array of hall effect sensors • Complex computations

• Hard to customize sensor

Resistive Tomography (e.g., [19])• Array of conductive rubber tracesas electrodes

• Robust construction • Ill-posed inverse problems

Piezoresistive (Curvature) (e.g., [20])• Employs an array of strain gauges • Directly measure curva-

ture• Frailty of electrical inter-connects

• Hysteresis

Dynamic Tactile SensingPiezoelectric (stress rate) (e.g., [21, 22, 23])• PVDF embedded in elastomericskin

• High bandwidth • Frailty of electrical junc-tions

Skin Acceleration (e.g., [24, 23])• Commercial accelerometer affixedto robot skin

• Simple • No spatially distributedcontent

• Sensed vibrations tend tobe dominated by structuralresonant frequency


antenna to aid a bio-inspired insect hexapod robot in awall-following control task.

For many animals, whiskers or antennae provide an ex-tremely accurate combination of contact sensing and pro-prioceptive information. For example, cockroaches cansteer themselves along curved walls at 20 body-lengthsper second using only the position and rate informationthat they obtain from their antennae. Other insects,such as arthropods use numerous small hair sensors onthe exoskeleton to localize contacts.

Proximity

While proximity sensing does not strictly fall under thecategory of tactile sensing, a number of researchers haveemployed various proximity sensors for the application ofcollision detection between a robot arm and the environ-ment and thus, we briefly review these technologies here.Three primary sensor technologies which include capaci-flective, IR optical, and ultrasonic sensors have been usedin this application. Vranish et al. developed an earlycapaciflective sensor for collision avoidance between theenvironment and a grounded robot arm [29]. Examplesof distributed IR emitter/detector pairs utilized withinartificial skin for the purposes of proximity sensing havebeen presented by Lumelsky’s research group [30, 31].A more recent design using optical fibers is reported in[32]. Other researchers have developed robot skins thatinclude both distributed ultrasonic and IR optical sen-sors for the purposes of collision avoidance [33]. Wegerifand Rosinski provide a comparison of the performance ofall 3 of these proximity sensing technologies [34]. For amore detailed review of this variety of sensors, see chap-ter 21 on sonar sensing and chapter 22 on range sensors.

20.2.2 Other contact sensors

There are a variety of other contact based sensors thatare capable of discerning object properties such as elec-tromagnetic characteristics, density (via ultrasound), orchemical composition (cf. animals, senses of taste andsmell). While this is beyond the scope of the cur-rent chapter, chapter 61, on biologically inspired robots,briefly discusses biologically inspired chemical sensors re-lated to smell and taste. For completeness, thermal sen-sors and material composition sensors are also brieflydiscussed below.

Thermal sensors

Thermal sensing can be used to determine the materialcomposition of an object as well as to measure surfacetemperatures. Since most objects in the environment areat about the same (“room”) temperature, a temperaturesensor that contains a heat source can detect the rateat which heat is absorbed by an object. This providesinformation about the heat capacity of the object andthe thermal conductivity of the material from which it ismade, making it easy, for example, to distinguish metalsfrom plastics.

Buttazzo, Dario, and Bajcsy [35] note that the piezo-electric polymer used in their tactile sensing system isalso strongly pyroelectric, and use a superficial layer as athermal sensor. Other sensors use thermistors as trans-ducers, with Siegel, Garabieta, and Hollerbach [36] re-porting a 4 x 4 array and Russell [37] a 2 x 10 array.Some systems purposely provide an internal tempera-ture reference and use the temperature differential fromthe environment as a means of finding contacts [38, 39].However, objects with a temperature the same as thereference will not be detected. Most of these sensorshave a relatively thick outer skin covering the heat sen-sitive elements, thus protecting delicate components andproviding a conformal surface at the expense of slowerresponse time.

A more recent example of thermal sensing can be foundin the work of Engel et al. who present a flexible tactilesensor design that includes integrated gold film heatersand RTDs (Resistance Temperature Device) on a poly-mer micro-machined substrate [40]. While there is a highlevel of integration presented by Engel et al., these sens-ing elements still remain frail, hence trade-offs concern-ing construction, performance, and protection of sensingelements in these systems remains an ongoing challenge.

Material composition sensors

There has been a little work on sensors for material com-position. In analogy with the human senses of taste andsmell, liquid– and vapor–phase chemical sensors couldpotentially determine the chemical composition of a sur-face (Bergveld 1986 [41], Nakamoto et al. 1991 [42]). An-other sensing modality which provides information aboutmaterial properties is electromagnetic field sensing, us-ing devices such as eddy current or Hall effect probesto measure ferromagnetism or conductivity (Auld andBahr 1986 [43], Clergeot et al. 1986 [44]).


20.2.3 Kinematic sensors

Although they are not generally thought of as tactilesensors, sensors that detect the position of a limb canprovide the robot with geometric information for ma-nipulation and exploration, particularly when the limbalso includes sensors that register contact events. Ex-amples of such sensors include the ubiquitous joint angleencoders found in virtually all robots as well as poten-tiometers, resolvers and other joint angle measuring de-vices. For limbs that do not undergo large rotations onecan also embed flexible structures such as elements com-posed of piezoresistive ink, e.g., used by Cowan et al.[28] as discussed above in section 20.2.1. Examples ofcombining information about joint angles with contactstatus sensors for manipulation include Kaneko [45] onthe “posture changeability” of fingers.

20.2.4 Force and load sensing

Actuator effort sensors

For some actuators such as electric servo motors, a mea-sure of the actuator effort can be obtained directly bymeasuring the motor current (typically using a sensingresistor in series with the motor and measuring voltagedrop across the sense resistor). However, because mo-tors are typically connected to robot limbs via gearboxeswith output/input efficiencies of 60 percent or less, it isusually much more accurate to measure the torque at theoutput of the gearbox. Solutions to this problem includeshaft torque load cells, (typically using strain gages) andmechanical structures at the robot joints whose deflec-tions can be measured using electromagnetic or opticalsensors . For cable or tendon-driven arms and hands it isuseful to measure the cable tension – both for purposesof compensating for friction in the drive-train and as away of measuring the loads upon the appendage [46].When fingers or arms make contact with objects in theenvironment, cable tension sensing becomes an alterna-tive to endpoint load sensing for measuring componentsof the contact forces. Of course, only those componentsthat produce significant torques can be measured withany accuracy. Section 15.4.2, Multifinger Hands:Sensors,contains more details concerning cable tension measure-ment.

Force sensors

When actuator effort sensors are not sufficient to mea-sure the forces exerted by or on a robot appendage, dis-

crete force sensors are typically utilized. These sensorsare found most often at the base joint or wrist of a robot,but could be distributed throughout the links of a robot.

In principle, any type of multi-axis load cell couldbe used for manipulator force–torque sensing. How-ever, the need for small, lightweight units with good sta-tic response eliminates many commercial sensors. Thedesign of force sensors for mounting above the grip-per at the wrist has received the most attention (e.g.Pugh 1986 [47], Webster 1988 [48]), but fingertip sen-sors for dextrous hands have also been devised. Oftenthese sensors are based on strain gauges mounted ona metal flexure (e.g. Salisbury 1984 [49], Brock andChiu 1985) [50]), which can be fairly stiff and robust.Sinden and Boie [51] propose a planar six axis force–torque sensor based on capacitive measurements with anelastomer dielectric. Design considerations for force sen-sors include stiffness, hysteresis, calibration, amplifica-tion, robustness, and mounting. Dario et al. presentan integrated fingertip for robotic hands integrated FSR(Force Sensing Resistor) pressure array, piezoceramicbimorph dynamic sensor, and force-torque sensor [21].More recently Edin et al. [52] have developed a minia-ture multi-axis fingertip force sensor, see Figure 20.2. Forapplications where immunity to electromagnetic noise isdesirable, Park [53] present a design for a robot finger-tip with embedded fiber optic Bragg gratings, used asoptical strain gages. Bicchi [54] and Uchiyama, Bayo,and Palma-Villalon [55] consider the optimal design ofmulti-axis force sensors in general.

It is interesting to note that more than just force infor-mation can be gained by the use of fingertip load sensors.Information from the force sensors can be combined withknowledge of fingertip geometry to estimate contact lo-cation, as implied in Figure 20.3. This method of contactsensing is referred to as “intrinsic tactile sensing,” andwas first presented by Bicchi et al. [56]. A compari-son between intrinsic and extrinsic contact sensing (i.e.,using distributed contact sensors) is presented by Son etal. [11]. This is also discussed in further detail in section20.3.1

20.2.5 Dynamic tactile sensors

Early special purpose slip sensors based on displacementdetected the motion of a moving element such as a rolleror needle in the gripper surface (e.g. Ueda et al. [57]). Amore recent approach uses a thermal sensor and a heatsource: when the grasped object begins to slip, the previ-ously warmed surface under the sensor moves away, caus-


Figure 20.2: Miniature fingertip force-torque sensor fora prosthetic hand (used by permission) [52].

TACTILE ARRAY SENSOR

FINGER TIP FORCE-TORQUE SENSOR

JOINT ANGLE SENSORS

DYNAMIC TACTILE SENSOR

JOINT ANGLE/TORQUE SENSORS

TACTILE ARRAY SENSOR

FINGERTIPFORCE/TORQUE SENSOR

DYNAMICTACTILESENSOR Point of

Contact

Figure 20.3: Robot hand with fingertip force and tactilesensing. Information from the force sensors can be com-bined with knowledge of fingertip geometry to estimatecontact location, referred to as “intrinsic tactile sensing.”

ing a drop in surface temperature beneath the sensor. Anon-contact optical approach uses correlation to revealmotion of the object surface (Matsuda et al. [58]). Anumber of researchers have suggested using conventionalarrays for slip detection, but the array resolution mustbe good and the scanning rate high to detect the motionof object features soon enough to prevent dropping thegrasped object.

In a systematic investigation of the feasibility of usingvibration to detect slip, Rebman and Kallhammer [59]used single elements from an array sensor to detect nor-mal vibrations at the contact surface. Dario and DeRossi[60] and Cutkosky and Howe [61] note that piezoelec-tric polymer transducers located near the contact sur-

face are very sensitive to vibrations and may be used forslip detection. Howe and Cutkosky [24] show that us-ing a small accelerometer to sense minute vibrations ofa compliant sensor skin is an effective means of detect-ing slip at its earliest stages. For hard objects held inmetal grippers, acoustic emissions may reveal incipientslip (Dornfeld and Handy [62], Cuttino, Huey, and Tay-lor [63]). Morrell [64] and Tremblay [65] investigated theuse of slip sensors in grasp force control.

Buttazzo, Dario and Bajcsy [35] have built a texture-sensing “fingernail” as part of their anthropomorphictactile sensing system. A piezoelectric element at thebase of the rigid plastic nail produces a large signal as itis dragged over a textured surface. The stress rate sensor(Cutkosky and Howe [61, 22] and Son et al. [66]) skin ac-celeration sensor (Howe and Cutkosky [24, 22]), and theinduced vibration sensor (Patterson and Nevill [67]) alldescribed above in the context of shape or slip sensing,also respond to the small vibrations produced by slid-ing over fine surface textures. More recent adaptationsof these sensors include piezoceramic bimorph dynamicsensors, with integrated FSR pressure array, and force-torque sensor [21]. Yamada et al. [68], who have devel-oped a piezoelectric artificial skin, are able to distinguishbetween rolling and slip. Waldron, et al. [23] describea tactile sensor that integrates skin acceleration with apiezoelectric array for the application of teledermitology.Ellis has also investigated the use of a very high resolu-tion tactile array data for discriminating surface textures[69]. Omata and Terunuma present a sensor that mea-sures changes in compliance by detecting changes in theresonant frequency of an active piezoelectric element [70].

20.2.6 Array sensors

Array sensors can be subdivided into two primary cate-gories: those that measure pressure and those that mea-sure the deflection of the sensor skin. Tactile pressurearrays are by far more common. Pressure arrays tendto be relatively stiff and utilize a variety of transductionmethods along with solid mechanics to calculate contactpressure distribution. Skin deformations/deflections inpressure arrays are on order of 1-2 mm. On the otherhand, skin deflection sensors are constructed in a man-ner that permits gross sensor skin deformation duringcontacts, which can be advantageous for grasp stability(see chapters 27 and 28 on grasping and manipulation).

Hundreds of designs for tactile array sensors have ap-peared in the literature in the last fifteen years, and afew of them are suitable for use with dextrous hands. In


terms of transducers, the fundamental requirement is tounambiguously recover either the shape or pressure dis-tribution across the contact from beneath a compliantelastomer layer. This can be done by directly sensingshape (e.g. Grahn and Astle [71], Nowlin [72]) or bysensing multiple components of subsurface strain (e.g.Novak [73], Yao et al. [74]). Solid mechanics models (seeSection 20.3.2) can then be used to determine the de-sired quantities. While the approach used in the modelsdeveloped to date is quite successful, they must be ex-tended to three dimensions and to incorporate multiplecomponents of strain. Since these models are complex,real-time execution must be addressed.

Contact location sensors

There have been a number of tactile sensors that solelyprovide contact location. Discrete switches placed on theouter chassis of mobile robots were quite common beforethe proliferation of ultrasonic sensors. Other researchershave presented work using discrete switch arrays. Somesuch sensors, utilize a membrane switch design, found inkeyboards. A visually transparent example of such asdesign is presented by Arai et al. for use in combinationwith a touch screen display [75]. Alternately, Griffin [76]shows a membrane switch inspired contact switch arraydesigned by W. Provancher that consists of flexible cir-cuits separated by thin dielectric strips. Contacts areregistered when pressure deflects one flexible circuit intothe other, as shown in Figure 20.4(a). More recent workpresented by Edin et al. [52] shows a nice extension onthis idea, with a two dimensional switch array embeddedin a prosthetic hand, see Figure 20.4(b). Some opticaltactile sensors (e.g. [77]) have also been used primarilyas contact location sensors. However, because they canalso measure the magnitude of the contact force they arecovered under tactile arrays.

Pressure sensing arrays

Capacitive pressure sensing arraysTactile pressure arrays are one of the oldest and mostcommon tactile sensor types. Some of the earliest workin this area was done by Fearing [78] and Fearing andBinford [9]. These researchers produced capacitive tac-tile pressure arrays within a robot fingertip, suitable fordexterous manipulation. The sensor array consisted ofoverlapping row and column electrodes separated by anelastomeric dielectric, forming an array of capacitors. Achange in capacitance results from compressing the di-electric between row-column plates at a particular in-

(a)

Touch Sensor Schematic

Plan View

Narrow Plate Elements(electrical conductors)

End View Ground Conductor

DielectricNarrow Plate Element

Dielectric Strips

Dielectric Strips

Flex-circuit substrate

Dielectric

Sensor Construction Deformed Sensor

ElectricalContact

ContactForce

Ground Conductor

End View

Assembled RobotFingertip Sensor

Sensor withflex-circuit peeled back

(b)

Figure 20.4: Contact switch arrays fabricated from flex-ible printed circuits. (a) A simple 16 x 1 switch arrayused on the fingertip of a dexterous robot hand (drawnby W. Provancher). (b) Contact switch array embeddedin the skin of a prosthetic hand (used by permission)[52].

tersection. The equation for capacitance, based on thephysical parameters, is expressed as C≈ (ε * A)/d, whereε is the permittivity of the dielectric between the platesof the capacitor, A is the area of the plates, and d is thespacing between them. Compressing the dielectric be-tween the capacitive plates thus reduces the plate spac-ing, d, thus providing a linear response with displace-ment.

Through appropriate switching circuitry, a particularregion of a sensor array can be isolated at a particularrow/column intersection. Pressure is calculated bymeans of solid mechanics as outlined by Fearing [79].Examples of similar capacitive tactile arrays can befound in [10, 11] and commercially at [12]. Morerecently, Shinoda’s group has investigated conductivefabric to form pairs of conforming, stacked capacitorsmore appropriate for covering larger areas than a robothand [13]. The fabric electrodes in the stacked capacitivesensor elements are alternately separated by stiff or softurethane foam, permitting simultaneous estimation of


contact pressure and area within a single sensor element.

Piezoresistive pressure sensing arraysA variety of researchers have produced tactile sensorarrays that are piezoresistive in nature. These sensorsgenerally either utilize a conductive rubber that is bulkmolded or a piezoresistive ink that is generally patternedvia screen printing or stamping. Each of these employ aconductive additive (typically carbon black) to create itsconductive/piezoresistive behavior. However, because ofthe fragility and hysteresis that these sensor morpholo-gies exhibit, some researchers have also developed fabric-based piezoresistive sensors.

Russell [80] presents one of the first molded conductiverubber tactile sensor arrays composed of conductive rub-ber column and row electrodes with piezoresistive junc-tions. However, this sensor exhibited significant driftand hysteresis, which became the topic of later researchto try to minimize these effects through proper selectionof molding material [3]. These issues were never com-pletely solved due to the hysteretic nature of elastomers,but this sensing approach remains attractive due to itsease of manufacturing. Hence it has continued to find ap-plications in the appendages of humanoid robots whereextreme accuracy is not required [81].

A number of researchers and companies have devel-oped tactile sensors that utilize conductive (piezoresis-tive) ink, generally referred to as FSRs (Force SensitiveResistors). It is by far the most common, simplest andmost readily available means to incorporate tactile sens-ing via off the shelf discrete sensors (see Tekscan Flexi-force FSRs). However, to make highly integrated, densesensor arrays, custom fabrication is necessary. Examplesof such sensors are presented by Papakostas et al. [4] andDario et al. [21]. To take this approach one step further,Someya [5] has produced robotic skin that employs pat-terned organic semiconductors for local amplification ofthe piezoresistive sensor array, printed on flexible poly-imide film. However, despite being fabricated on a flexi-ble substrate, these sensor arrays are still susceptible tobending fatigue.

Piezoresistive fabrics have been developed to addressfatigue and fragility issues found in tactile arrays. Ex-amples of these sensors are presented by De Rossi et al.[7], Tognetti et al. [8], and Shimojo et al. [6]. Thesesensors tend to be larger (i.e., have lower spacial reso-lution) and are utilized in applications such as the armsor legs of humanoid robots. Because this technology hasthe potential to replace ordinary cloth, it is a promis-ing technology for applications in wearable computing

or even smart clothing.One final design that does not fall under the above

fabrication categories is a sensor designed by Kageyamaet al. that utilizes a piezoresistive conductive gelpressure array along with a multi-level contact switcharray via variable contact resistance within sensor layersthat they developed for use in humanoid robots [82].

MEMS pressure sensing arraysMEMS technology is quite attractive for producinghighly integrated packaging for tactile sensing and as-sociated interconnects and electronics. Early deviceswere produced in silicon through standard silicon micro-machining techniques, which include Kane, et al. whoproduced a silicon micro-machine CMOS-compatibletactile array capable of measure shear and normal force[14]. These sensors functioned well on the laboratorydesktop, but were too frail to survive impact or harshenvironments. More recently, researchers have appliedMEMS techniques to produce sensor arrays appropriatefor embedding within an elastomeric skin with flexiblesubstrates. This has the advantage of improved durabil-ity because of the protective skin. Engel et al. haveproduced a tactile sensor with combined temperatureand cantilever elements grown on a polyimide backing[40]. Polymer micro-machining of the polyimide was per-formed to give the polyimide substrate preferential com-pliance. An interesting variation on this design is pre-sented by Noda and Shimoyama [83]. These researchersalso produced cantilever shear stress elements on their ar-ray sensor, however, they do this in a planar fabricationprocess to fabricate cantilevers with Ni-Cr piezoresistorsthat are subsequently released from the fabrication sub-strate and made to stand up off the substrate like hairsby applying a magnetic field and subsequently fixing thecantilevered beams by covering them with Parylene-C(used for sensing shear) [83]. Returning to the idea ofusing silicon load cells for tactile sensing, Valdastri etal. [15] have developed a miniature MEMS silicon-basedload cell, that resembles a joystick, that is appropriate forembedding within an elastomeric skin (see Figure 20.5).These sensors could be distributed beneath the skin sur-face to detect complex stress states in the elastomericskin.

Skin deflection sensing

Brocket [84] was one of the first to propose the idea ofusing a deformable membrane robot fingertips. As notedby Shimoga and Goldenberg [85], there are several ad-


(a)

3 Copyright © 1996 by ASME

Figure 1 (A) SENSOR STRUCTURE; (B) A SEM PICTURE CLOSE UP

ON THE SENSOR TETHERS AND CENTRAL PILLAR; (C) SEM PICTURE OF SENSOR BACK SIDE SHOWING ONE PIEZORESISTOR

CONNECTED BY AL METALS TO TWO ROUND NI/AU PADS.

Packaging of the microsensor. The packaging of the microsensor consisted in two main parts, i.e. the contact of the sensor to flexible connections and the implementation of a mechanical interface between the sensor and the outer world, that protects the sensor and through which external actions can be detected by the sensor itself.

Flexible connections were obtained from LF9150R Pyralux (DuPont, Wilmington, DE, USA) consisting of a 127 µm thick Kapton© sheet with a 35µm layer of copper (305g/m2) on one side. Cu was patterned by photolithography using a MicroPosit S1813 photoresist (Shipley, USA) and 100µm Cu metals could be obtained.

The current layout of the sensor contacts includes nine Ni/Au round bump pads (Ø 200µm) shown in Fig. 1(c). They were designed with dimensions compatible with the polymer flip-chip bonding technique that was previously employed to bond the sensor to a carrier substrate [23]. In this work, the sensor contacts were bonded directly to corresponding copper pads on the Kapton© substrate with a different technique.

A proper bonding technique between the sensor was identified in order to establish mechanically and electrically reliable contacts. The 5552 R Z-Axis Adhesive Film (3M, St. Paul MN, USA) [28] was exploited for mounting the sensor on the flexible circuit. This component is a heat-bonded, electrically anisotropic conductive film consisting of an adhesive matrix randomly loaded with conductive particles. These particles allow electrical connection through the film (in the Z axis), but are spaced far enough from each other to avoid electrical connections in the XY plane. The main advantage in using such adhesive film is that serial bonding of each pad could be avoided. A microassembly station was set up which allowed precise alignment of the sensor on the flexible circuit, and precise controlling of the curing parameters, i.e. pressure (20-40kg/cm²) and temperature (170°C). Reliable and strong mechanical bondings (sensor detachment from the flexible circuit was unsuccessful without breaking the sensor bulk), together with good electrical contacts (contact resistances below 1Ω), were achieved.

After mounting the sensor on the flexible circuit, as described above, the next step towards the tactile sensor prototype was the encapsulation in a soft and compliant material.

Figure 2 PACKAGING COMPONENTS OF THE SOFT COMPLIANT TACTILE MICROSENSOR (SCTM) PROTOTYPE IN (A) A 2D

SECTION AND (B) AN EXPLODED DISPLAY; (C) PICTURE OF THE PACKAGED PROTOTYPE; (D) PICTURE OF THREE KINDS OF

PROTOTYPES HAVING A FIXED t1= 1MM AND t2 RANGING FROM 1MM TO 3MM FROM THE FRONT TO THE BACK PROTOTYPE.

While striving to identify the proper packaging material,

that can be considered to simulate the compliance and friction properties of the natural human finger, the chosen material should also fulfil important technological and mechanical type of requirements. It must have the capability of ensuring protection to the silicon structure, while not impairing sensor function and not increasing mechanical filtering of the external applied load. In addition, the curing parameters of the chosen material must be compatible with the physical and technological characteristics of the silicon device.

The selected materials for the microsensor packaging were silicone and polyurethane. First trials were performed using PDMS silicone elastomer [Sylgard 184, Dow Corning, USA]. Nevertheless, silicone typically has very low adhesion to non-silicone materials thus to silicon. Also, during the curing phase shrinkage of the material occurs. These effects lead to an empty space left in between the silicon sensor surface and the cured silicone, introducing unwanted hysteresis. Thus silicone was discarded as encapsulating material.

The material used for the tactile sensor packaging was then the two components soft polyurethane Poly 74-45 (PolyTek, USA). From a technological point of view, polyurethane adheres well to the MEMS device and by penetrating into the empty cavities of the microsensor. It can highly increase the loading range of the device, as reported for a particular packaging and biomedical application in [29]. Importantly, it has been recently demonstrated [30] that the proposed type of polyurethane material has conformance characteristics similar to the human fingertip and could be considered for the artificial emulation of its epidermis/dermis layer.

As described in the next section, the shape and dimensions of the polyurethane packaging was designed in order to allow

(A) (B) (C)

t2

t1

Top layer

Bottom layer

Silicon sensor

Flexible circuit

(A) (B)

(D) (C)

(b)

3 Copyright © 1996 by ASME

Figure 1 (A) SENSOR STRUCTURE; (B) A SEM PICTURE CLOSE UP

ON THE SENSOR TETHERS AND CENTRAL PILLAR; (C) SEM PICTURE OF SENSOR BACK SIDE SHOWING ONE PIEZORESISTOR

CONNECTED BY AL METALS TO TWO ROUND NI/AU PADS.

Packaging of the microsensor. The packaging of the microsensor consisted in two main parts, i.e. the contact of the sensor to flexible connections and the implementation of a mechanical interface between the sensor and the outer world, that protects the sensor and through which external actions can be detected by the sensor itself.

Flexible connections were obtained from LF9150R Pyralux (DuPont, Wilmington, DE, USA) consisting of a 127 µm thick Kapton© sheet with a 35µm layer of copper (305g/m2) on one side. Cu was patterned by photolithography using a MicroPosit S1813 photoresist (Shipley, USA) and 100µm Cu metals could be obtained.

The current layout of the sensor contacts includes nine Ni/Au round bump pads (Ø 200µm) shown in Fig. 1(c). They were designed with dimensions compatible with the polymer flip-chip bonding technique that was previously employed to bond the sensor to a carrier substrate [23]. In this work, the sensor contacts were bonded directly to corresponding copper pads on the Kapton© substrate with a different technique.

A proper bonding technique between the sensor was identified in order to establish mechanically and electrically reliable contacts. The 5552 R Z-Axis Adhesive Film (3M, St. Paul MN, USA) [28] was exploited for mounting the sensor on the flexible circuit. This component is a heat-bonded, electrically anisotropic conductive film consisting of an adhesive matrix randomly loaded with conductive particles. These particles allow electrical connection through the film (in the Z axis), but are spaced far enough from each other to avoid electrical connections in the XY plane. The main advantage in using such adhesive film is that serial bonding of each pad could be avoided. A microassembly station was set up which allowed precise alignment of the sensor on the flexible circuit, and precise controlling of the curing parameters, i.e. pressure (20-40kg/cm²) and temperature (170°C). Reliable and strong mechanical bondings (sensor detachment from the flexible circuit was unsuccessful without breaking the sensor bulk), together with good electrical contacts (contact resistances below 1Ω), were achieved.

After mounting the sensor on the flexible circuit, as described above, the next step towards the tactile sensor prototype was the encapsulation in a soft and compliant material.

Figure 2 PACKAGING COMPONENTS OF THE SOFT COMPLIANT TACTILE MICROSENSOR (SCTM) PROTOTYPE IN (A) A 2D

SECTION AND (B) AN EXPLODED DISPLAY; (C) PICTURE OF THE PACKAGED PROTOTYPE; (D) PICTURE OF THREE KINDS OF

PROTOTYPES HAVING A FIXED t1= 1MM AND t2 RANGING FROM 1MM TO 3MM FROM THE FRONT TO THE BACK PROTOTYPE.

While striving to identify the proper packaging material,

that can be considered to simulate the compliance and friction properties of the natural human finger, the chosen material should also fulfil important technological and mechanical type of requirements. It must have the capability of ensuring protection to the silicon structure, while not impairing sensor function and not increasing mechanical filtering of the external applied load. In addition, the curing parameters of the chosen material must be compatible with the physical and technological characteristics of the silicon device.

The selected materials for the microsensor packaging were silicone and polyurethane. First trials were performed using PDMS silicone elastomer [Sylgard 184, Dow Corning, USA]. Nevertheless, silicone typically has very low adhesion to non-silicone materials thus to silicon. Also, during the curing phase shrinkage of the material occurs. These effects lead to an empty space left in between the silicon sensor surface and the cured silicone, introducing unwanted hysteresis. Thus silicone was discarded as encapsulating material.

The material used for the tactile sensor packaging was then the two components soft polyurethane Poly 74-45 (PolyTek, USA). From a technological point of view, polyurethane adheres well to the MEMS device and by penetrating into the empty cavities of the microsensor. It can highly increase the loading range of the device, as reported for a particular packaging and biomedical application in [29]. Importantly, it has been recently demonstrated [30] that the proposed type of polyurethane material has conformance characteristics similar to the human fingertip and could be considered for the artificial emulation of its epidermis/dermis layer.

As described in the next section, the shape and dimensions of the polyurethane packaging was designed in order to allow

(A) (B) (C)

t2

t1

Top layer

Bottom layer

Silicon sensor

Flexible circuit

(A) (B)

(D) (C)

Figure 20.5: (a) SEM micrographs of a MEMS 3-axistactile force sensor. (b) MEMS force sensor wire-bondedto a flex-circuit and embedded within a silicone rubberskin [15].

vantages to using deformable fingertips over more rigidrobot fingertips, which include: 1) improved grasp stabil-ity, 2) reduced shock, and 3) reduced fatigue for embed-ded sensor elements. Early work on deformable finger-tips includes Russell’s work to create a compliant siliconerubber robot finger [80]. His sensor finger used a rigidbacking and polyurethane foam to provide a restoringforce for the sensor skin. An array of conductive rubberstrain gauge elements and their associated interconnectswere cut to the correct shape and bonded to the backsideof the silicone rubber skin. The conductive rubber wassimply silicone rubber, chosen with minimal mechanicalhysteresis, mixed with graphite. The electrical intercon-nects of a given row tapped into the conductive rubberstrain gauge at even intervals along its length, thus sub-dividing it into several individual strain measurements,forming a potential divider. Russell shows results forboth an 8 x 1 line array and an 8 x 5 array.

Nowlin used Bayesian algorithms to improve data in-terpretation of a deformable tactile sensor that usedmagnetic field sensing [72]. A 4 x 4 array of magnets

were supported above paired hall-effect sensors off a rigidbase by individual fluid filled balloons. Hall-effect sen-sors measure the strength of the local magnetic field andshould therefore increase with closer proximity to themagnets; however, this is complicated by the proximityof neighboring magnets in the sensor array. Hence, noisydata from the hall-effect sensors were combined usingBayesian algorithms to predict membrane deformationfor a deformable fingertip [72].

Later Russell and Parkinson [19] developed an im-pedance tomographic tactile sensor, capable of measureskin deformation over an 8 x 5 array. This sensor wasconstructed with of neoprene rubber and filled with dis-tilled water. Row and column electrodes were made fromcopper and conductive rubber for the rigid substrate andneoprene skin, respectively. Like the capacitive tactilesensors described above, this sensor utilized multiplex-ing electronics to reduce the number of electrical inter-connects. Square waveform driver electronics are used toestimate the resistance of a column of water formed be-tween row and column elements providing a signal thatis proportional to the current skin height.

After suggesting the idea of using deformable tactilesensors, Ferrier and Brocket [17] implemented a tactilesensor which used optical tracking in combination withmodels of the sensors skin deformation to predict sensorfingertip skin deformations. The fingertip sensor consistsof a tiny CCD camera focused on a 7 x 7 array of dotsthat are marked on the inside of the gel-filled siliconefingertip membrane. An algorithm is used to construct a13 x 13 grid over the array of dots. This algorithm usesa combination of the position that is sensed by the CCDcamera, which provides the location along a line radiallyoutward from the focal point, in combination with a me-chanical model used to solve for the radial distance fromthe camera focal point, based on energy minimization.

Provancher and Cutkosky present another design thatuses piezoresistive strain gauges to directly measure fin-gertip membrane curvature [20]. The sensor is con-structed on a polyimide substrate with commercial straingauge strips (used to investigate strain gradients) bondedback-to-back to isolate bending strains, which are pro-portional to curvature. This curvature information isimportant for motion planning in dexterous manipula-tion. The authors present a mathematical model whichuses a set of basis functions and least squares minimiza-tion to calculate a curvature-space curve fit that is in-sensitive to sensor noise and capable of reconstruction ofthe deformed membrane shape. Results from a 11 x 1line array sensor prototype are presented in 20.3.2.


Other tactile array sensors

An early example of an optical tactile sensor was pre-sented by Maekawa, et al. [77], who used a single opticalposition sensing device (PSD) or a CCD camera array todetect the position of scattered light off of a hemispher-ical optical wave guide fingertip with a silicone rubbercover. Light is scattered at the location of contact and,based on a simple model, multiple points of contact canbe estimated. With a textured skin, the magnitude of theforce can also be estimated, as the contact area growsin proportion to the pressure. However, an issue withfingertips that use a compliant skin covering a hard sub-strate is that adhesion between the two materials resultsin hysteresis. In addition, when the fingertip is draggedover a surface, the friction can produce a shift in theestimated contact position.

Another interesting tactile sensor uses vision to trackan array of spherical markers embedded in a transparentelastomer to infer the stress state of the skin materialdue to applied forces [16]. This sensor is currently beingcommercialized under the tradename GelForce.

A number of sensors have been developed that monitorchanges in acoustic energy reflected off of a skin surfaceor due to the distortion of an acoustic subsurface cavity.Shinoda et al. [86] present a sensor that looks at thechange in reflected acoustic energy from an acoustic res-onator chamber near the surface of the skin and its appli-cation for instantaneous friction measurement [87]. Andoet al. [88] present a more sophisticated ultrasound sen-sor that achieves 6-DOF displacement sensing via pairedplate elements that utilize 4 ultrasound transducers perplate.

Several researchers have developed sensors with multi-ple sensing modalities. Examples of sensors that combinetactile and thermal sensing are presented by Siegel et al.[89] and Castelli [90]. Shimizu et al. [91] present an sen-sor for measuring force and hardness of an object. Thissensor actively uses pneumatic pressure to drive sensingelements into an object’s surface.

Another interesting sensor includes a combination oftactile sensing and ultrasound imaging [92], in which Me-thil et al. have developed a haptic system for performingbreast exams that utilizes an optical waveguide tactilesensor.

20.3 Tactile Information Process-ing

20.3.1 Tactile Information Flow: Meansand ends of tactile sensing

In discussing the processing of tactile information wereturn first to the three main uses depicted in Fig-ure 20.1. For manipulation, we require, foremost, in-formation about contact locations and forces so that wecan grasp objects securely and impart desired forces andmotions to them. For exploration, we are concerned withobtaining and integrating information about the object,including the local geometry, hardness, friction, texture,thermal conductivity, etc. For response, we are con-cerned especially with the detection of events, such ascontacts produced by an external agent, and in assessingtheir types and magnitudes. The uses of information areoften coupled. For example, we manipulate objects inorder to explore them and we use the information ob-tained through object exploration to improve our abilityto control forces and motions in manipulation. Recog-nizing contact events is also important for manipulationand exploration, as it is for response.

Figure 20.6 summarizes the general flow of informa-tion from each type of sensor reviewed in the previoussection through primary sensed quantities to informationprovided for manipulation, exploration and response. Auseful thought exercise is to consider exactly what infor-mation we use to perform a task such as turning a penend-over-end between the fingers. We can easily performthis task with our eyes closed. What information are weusing? We need to track the position and orientationof the pen and to monitor the forces that we impose onit to maintain stable manipulation. In other words, weneed to know the configuration of our grasp, the loca-tions and movements of contacts over the surfaces of ourfingers, the magnitudes of grasp forces and the contactconditions with respect to friction limits, etc. The samerequirements apply for robots and are provided by theinformation flow in Figure 20.6.

At the upper left corner of the figure, joint angles, com-bined with the forward kinematic model of the manipula-tor and knowledge of external link geometries, establishthe positions and orientations of coordinate frames em-bedded in the fingertips. This information is needed tointegrate local information about object shape, surfacenormal orientation, etc. so that the overall geometry andpose of the object can be determined.

Actuator effort sensors provide information about the


joint angle

actuator

effort

force/torque

dynamic

array

thermal, etc.

kinematics

solid

mechanics

dynamic

filtering

contact geometry

surface stresses

grasp configuration

contact forces

contact impulse, vibration

geometry

feature

extraction

surface fitting

event

recognition

contact type

grasp forces

contact motion

object shape

orientation

manipulation phase

object pose

estimation

surface properties (e.g thermal)

object identification

Figure 20.6: Force and tactile sensor information flow and signal processing.

resultant forces, using the Jacobian transpose: JT f = τwhere f is an n×1 vector of external forces and momentstaken with respect to a coordinate frame embedded inthe appendage. JT is the Jacobian transpose, mappingexternal forces and moments to joint torques and τ isan m × 1 vector of joint torques for a serial kinematicchain with m degrees of freedom. We require that thekth column of JT have elements that are relatively largecompared to the overall condition number of J in orderto provide an accurate measurement of the kth elementof f . Eberman and Salisbury [93] show that it is pos-sible to measure contact force and location using onlyjoint torque measurements if the manipulator has cleandynamics.

Alternatively, we can use a multi-axis force/torquesensor in the fingers, as indicated in Figure 20.3, or robotwrist to obtain contact forces. This approach has the ad-vantage of providing dynamic force signals with a highersignal/noise ratio because they are not masked by theinertias of the robot arm or fingers and their transmis-sions. If the geometry of the fingertip is known, one canuse “intrinsic tactile sensing” [49] [94] to compute thecontact location as well as the contact force by examin-ing ratios of resultant forces and torques at the sensor.

When the contacts are small compared to the finger-tips, so that a point-contact approximation applies, andthe fingertips are convex shapes, the contact locationis easily computed. Figure 20.7 shows a contact force,f , contacting the fingertip surface at a location r. Aforce/torque sensor such as that in Figure 20.2 measuresthe moment, τ = r × f with respect to the origin. Ifwe consider the lever arm, h, perpendicular to the lineof action of f , then h/h = f/f × τ/τ , where h = τ/f isthe magnitude of h. We can then write that r = h−αfwhere α is a constant obtained by solving for the inter-section of the line of action and the fingertip surface. Fora convex fingertip, there will be two such points, of whichonly one corresponds to a positive (inward) contact force.

Going a couple of steps further, from the contact loca-tion one can deduce the local contact normal and contactkinematic type from a small number of force measure-ments. Bicchi presents algorithms for extending thesemethods to soft fingers [94]. Brock and Chiu [50] de-scribe the use of force sensors for the perception of objectshape using this approach, and for measuring the massand center of mass of a grasped object.

For precision tasks involving small objects or smallforces and motions, cutaneous sensors provide the most


r

f

h

x

y

z

contact force

on fingertip

surface

Figure 20.7: Intrinsic tactile sensing: a contact producesa unique line of action and moment about the origin of acoordinate system in the fingertip. The contact locationcan be obtained by solving for the intersection of the lineof action and the fingertip surface.

sensitive measurements. In general, as task requirementsget smaller, the sensor must be located closer to the con-tact so that the compliance and inertia of the interveningparts of the manipulator do not interfere with the mea-surement. Dario [95] suggests that fingertip force sensorsare useful for forces of 0.1 – 10.0 N while array sensorscan measure distributed forces of 0.01 – 1.0 N. Son etal. [11] find that intrinsic tactile sensing and array sen-sors can both provide accurate (within 1 mm) estimatesof contact location; however the intrinsic tactile sens-ing method is inherently sensitive to the accuracy of theforce/torque sensor calibration and can produce tran-sient errors due to unmodeled dynamic forces.

Proceeding down the left side of Figure 20.6 we cometo the large category of cutaneous array sensors. The in-terpretation of information from array sensors dependsinitially on the transducer type. For arrays of binary con-tact or proximity sensors interpretation amounts mainlyto establishing the location and general shape of the con-tact area. Techniques common to binary vision can beused to obtain sub-pixel resolution and to identify con-tact features. This information, in combination withmeasurements of the grasp forces from actuator effort orforce/torque sensors, is sufficient for basic manipulationtasks [52].

20.3.2 Solid mechanics and deconvolu-tion

A basic problem associated with tactile array sensors isto reconstruct what is happening at the surface of theskin from a finite set of measurements obtained beneaththe surface. Typically we are interested in determiningpressure and perhaps shear stress distributions associ-ated with contacts on the skin. In other cases, as whenthe fingertips consist of a gel or soft foam covered by athin membrane so that the pressure is nearly constant,the local geometry of the contact is of interest.

In the following example we consider the case of anarray of elements located at a depth d below the sur-face of an elastomeric skin. A contact has resulted ina pressure distribution over the region of interest. Weestablish a coordinate system with z pointing in the in-ward normal direction and, for simplicity, we examinea one-dimensional loading case, p(y), in which the pres-sure distribution is unchanging in the x direction. Wefurther assume that the extent of the skin in the x di-rection is large compared to the skin thickness so thatstrains in the x direction are inhibited, leading to a planestrain elasticity problem and we assume that the skinis a homogeneous, isotropic material and the strains aresmall enough that linear elasticity theory can be applied.Of course, none of these assumptions is entirely validin practical cases; however, the results do agree qualita-tively with the measurements obtained with actual robotfingers and tactile arrays. A thorough discussion of thegeneral approach and of the accuracy of the linear elasticmodels can be found in [79, 22, 96, 97, 98].

Figure 20.8 illustrates the case of two line loads, orknife edges, pressed against the surface of the skin (akinto a planar version of the two-point discrimination testfor human tactile acuity). The solution for a single lineload, or impulse response, was derived by Boussinesq in1885. For the case of plane strain the principal stressesin the (y, z) plane from a normal unit impulse can beexpressed in cartesian coordinates as [99]:

σz(y, z) = (−2πz

)1

[1 + (y/z)2]2(20.1)

σy(y, z) = (−2πz

)(y/z)2

[1 + (y/z)2]2(20.2)

σx(y, z) = ν(σy + σz) (20.3)

where ν is Poisson’s ratio for the material (typically 0.5for elastomeric materials).


Figure 20.8: Plane strain stress response for two (unitmagnitude) line loads. Note the “blurring” that occurswith greater depth.

For two such line loads located at distances δ1 and δ2

from the origin, the solution can be obtained by super-position:

σz(y, z) = (−2πz

)

[1

[1 + (y−δ1z )2]2

+1

[1 + (y−δ2z )2]2

](20.4)

σy(y, z) = (−2πz

)

[(y−δ1

z )2

[1 + (y−δ1z )2]2

+(y−δ2

z )2

[1 + (y−δ2z )2]2

](20.5)

For more general pressure distributions the stressescan be found by convolution of the pressure distribution,p(y), and the impulse response, Gi(y, z):

σi =∫ τ=y

τ=−∞[p(τ)dτ ]Gi(y − τ, z) (20.6)

Also plotted in Figure 20.8 are curves corresponding tothe vertical stress components, σz at two different depthsd1 = 2λ and d2 = 3λ, where λ is the sensor spacing.As we go deeper beneath the skin, the stresses becomesmoothed or blurred, and the ability to distinguish be-tween closely spaced impulses diminishes. However, theblurring of concentrated pressure distributions can alsoprovide an advantage when we have a limited numberof sensors because the stresses and strains spread overa larger area and are more likely to affect at least onesensor. The elastic skin also provides a kind of auto-matic “edge enhancement” because stresses are high atthe transitions between loaded and unloaded regions ofthe skin.

In most cases, for example in the case of capacitiveor magnetic sensors, the sensing elements will measurestrains or local deformations of the skin material in thevertical direction. In a few cases, such as pieces of piezo-electric film embedded in an elastomeric skin [22], thesensors are sufficiently stiff compared to the surroundingmaterial that they can be considered to measure stressesdirectly.

For the case of elastic plane strain, the strains are re-lated to the stresses by [100]:

εy =1E

[σy − ν(σx + σz)] (20.7)

εz =1E

[σz − ν(σx + σy)] (20.8)

where E is the Young’s modulus and ν is the Poisson’sratio, which we assume is 0.5 for an elastomeric skin.

Figure 20.9 shows the typical measurements thatmight be obtained from a row of sensing elements fromthe two line loads applied in Figure 20.8. Each bar cor-responds to the strain, εzi measured by a correspondingelement and computed using equation 20.8, with stressesobtained from equations 20.4, 20.5 and 20.3.

The problem at this point is to produce a best estimateof the surface pressure distribution, p(y) from this finiteset of subsurface strain measurements. The problem is aclassic example of estimating a signal from a sparse setof remote measurements. One approach to this processis based on deconvolution techniques [97][79][22]. Themeasured signal from the sensors, εz, is convolved withthe inverse of the impulse strain response H(y) to findan estimate of the surface pressure that produced the


Figure 20.9: Measured strain with assumed 5% noise

signals. The inversion tends to amplify high frequencynoise and the inverse filter bandwidth must be limitedaccording to the spatial density of the sensors and theirdepth below surface.

Another approach [16, 96] is to assume that the surfacepressure distribution can be approximated by a finite setof impulses p = [p1, p2 . . . pn]t. The sensor readings forma vector, ε = [ε1, ε2 . . . εm]t, where m > n for the band-width limitations discussed above. The strain responsecan then be written as a matrix equation:

ε = Hp. (20.9)

Each element of H is computed using equation 20.8 withσz and σy computed using equations similar to 20.4 and20.5 and with σx from equation 20.3. The estimateddiscrete pressure distribution is then found by taking thepseudoinverse of H:

p̂ = H+ε. (20.10)

Using the strain measurements from Figure 20.9, at adepth d = 2λ , the estimated pressure distribution us-ing the pseudoinverse method is seen in Figure 20.10.In this example, because the assumed set of seven im-pulses matches fortuitously with the actual loading, thereconstruction is fairly accurate despite the assumed 5%noise.

An alternative approach to constructing soft robotfinger tips is to enclose a compliant medium such as

Figure 20.10: Estimated surface pressure distribution us-ing pseudoinverse method for 11 sensors and 7 assumedimpulses.

foam rubber or fluid in a thin elastic membrane (e.g.[84, 101, 102, 103, 80, 104, 18]. Some of the tactile arraysensors developed for these fingers are able to measuredirectly the shape of the membrane, so that a physi-cal model is not needed for interpretation of the signale.g. [18]. Another sensing scheme uses an array of mag-netic sensors at the center of the finger to measure thechanges in the magnetic field generated by deformationsof the magnet-loaded membrane [104]. A statistical al-gorithm has been developed that can robustly determinethe membrane shape from the sensor signals ([18]). How-ever, a mechanical model is still required to find the pres-sure distribution across the contact from the shape in-formation provided by all of these sensors.

20.3.3 Curvature and shape information

Another alternative to measuring subsurface strains ordeflections is to measure directly the local curvature ateach element of an array of sensors [105, 106]. The cur-vature information can be applied directly toward iden-tifying contact type and centroid location or it can beintegrated to obtain the local shape of the contact aswith sensors just described that measure the profile ofa membrane. To reduce the effects of noise it is usefulto assume a simple model for the membrane and use ba-sis functions to fit the curvature data before integrating


[20].Returning to Figure 20.6, once the local contact shape

or geometry has been established the next steps typicallyinclude feature identification (e.g. identifying corners orridges on an object) and determining the overall shapeand pose of the object in the hand.

Often the object shape is at least partially known apriori in which case a variety of surface or data fittingmethods can be used. For example, Fearing [107] de-veloped a method for calculation of the radius of curva-ture and orientation of generalized cylinders from tactilearray data and [108] developed a neural network thatperforms a similar calculation. Other schemes use con-tact locations, surface normals and contact forces to de-termine information about object shape and orientationwith respect to the hand [109, 110, 111, 112].

Allen [113] uses several different primitive representa-tions for object shape attributes based on the particularexploratory procedure used to sense the object. Objectvolume and approximate shape are perceived with en-closure grasping, and the resulting shape is modeled us-ing superquadric surfaces. Similarly, measurement of thelateral extent of object faces leads to a face-edge-vertexmodel and contour following to a generalized cylinderrepresentation.

The question of what constitutes an appropriate setof features is not well understood, although it clearly de-pends upon the intended application. Ellis [69] considersappropriate feature sets and methodologies for acquiringthe needed data. Lederman and Browse [114] suggestthat surface roughness, surface curvature, and orientededges are used in human haptic perception.

20.3.4 Object and surface identification

The most common application of touch information hasbeen in object recognition and classification. In objectrecognition the goal is to identify one object from a setof known objects using information derived from touch.In classification the goal is to categorize objects accord-ing to preselected sensed properties. These systems areusually based on geometric information derived from tac-tile array or force sensors. Recently the use of othertypes of touch information (e.g., compliance, texture,thermal properties) in exploration and identification hasreceived some attention (e.g. Bajcsy [115], Stansfield[116], Klatzky, Bajcsy, and Lederman [117].

A number of systems have used statistical patternrecognition, which can improve noise immunity sinceonly statistics derived from the sensed data are used.

Unfortunately, this also means that only a few objecttypes can be discriminated. Systems have been based onthe statistics of tactile array sensor data [118, 119], andon the statistics of finger joint angles while grasping theobject [120, 121, 122].

Many different features derived from tactile array datahave been used for model-based recognition and classi-fication. Systems have used geometric features such asholes, edges, and corners [105, 123, 114] and object sur-faces [124] Other feature sets include geometric moments[125] [126], linear transforms [127] and sequences of sur-face tangents [128]. Gaston and Lozano-Perez [110] uselocal surface normals and contact locations as features,which could be derived from array, force, or joint sensorinformation. Siegel [129] presents a method for findingthe pose of a known object grasped in a robot hand frommeasurements of the finger joint angles and torques.

20.3.5 Active sensing strategies

Because touch provides only local information, move-ment is an integral part of touch sensing for recogni-tion and exploration. Several workers in object recogni-tion applications have developed strategies for schedul-ing sensor movements so that each additional observa-tion decreases the number of objects which are consistentwith prior observations. This is sometimes described asa “hypothesize and test” approach. Early examples in-clude Gurfinkel et al. [105] and Hillis [123]. Schneiter[111], Grimson and Lozano-Perez [109], Ellis [130] andCameron [131] have developed algorithms for schedul-ing sensor moves to rapidly recognize polygonal objectsusing touch measurements of contact location and lo-cal surface normal. In Schneiter’s scheme, each sensingmove should cross the boundary of the intersection ofall objects consistent with previous sensor observations.Yap and Cole [112] show that a probing strategy to de-termine the shape of a convex planar polygon with Vvertices requires at least 3V − 1 measurements. Roberts[132] proposes a movement strategy for recognition thatincludes tracing the robot finger along object surfacesand edges, rather than moving the finger through freespace between readings.

In non-model based approaches, Klatzky, Bajcsy, andLederman [117] have suggested that robotics systems canemploy the same exploratory procedures used by humansin haptic exploration. These procedures prescribe thefinger motions needed for tasks such as tracing objectcontours, measuring compliance, and determining thelateral extent of object surfaces. Stansfield [116, 133] and


Allen [113] have implemented some of these exploratoryprocedures using multifingered robot hands with tactilearray sensors. Dario et al. [134] have created similar“tactile subroutines,” which they have used in a num-ber interesting applications, including detection of hard-ened lumps (e.g. tumors) beneath a compliant surface.Kaneko and Tanie [45] describe a method for using smallfinger motions to find the location of finger-object con-tacts without distributed tactile sensors.

Edge tracking and surface following have also receivedconsiderable attention. Muthukrishnan et al. [135] lookat edge finding algorithms for matching segments be-tween successive tactile array impressions. Berger andKhosla [136] have demonstrated curved edge tracking inreal time using tactile array information. Pribadi, Bayand Hemami [137] propose a control strategy for track-ing an unknown object surface using tactile information,and Bay [138] designs a surface shape estimator for ex-ploration by a multifingered hand which uses contact lo-cation and surface normal information from a force sen-sor. Zhang and Chen (2000) [139] present an approachto ”tactile servoing” in which they model the contact be-tween a compliant tactile sensor and an object to obtaina tactile Jacobian, which is used to produce incrementalmotion commands for a manipulator. They demonstratethe approach for rolling contact and edge tracking tasks.

In practice, surface following and manipulation are of-ten combined. Okamura and Cutkosky [140] present anapproach in which a rounded fingertip equipped with atactile sensor is used to trace object surfaces and locatefeatures, which are defined as regions having local curva-ture that is high in comparison to that of the fingertip.

20.3.6 Dynamic sensing and event detec-tion

For dynamic tactile sensors used to detect such eventsas gentle contacts or slippage between the fingertips andan object, the main challenge is to reliably detect theevent in question, without false positives. The dynamictactile sensors that produce large signals in response tocontact events are also prone to producing large signalsin response to vibrations from the robot drive train andto rapid accelerations of the robot hand. Solutions formore robustly detecting contact events include compar-ing the signals from dynamic tactile sensors at and awayfrom the contact regions and statistical pattern recogni-tion methods to identify the ”signature” of true contactevents [65, 93, 68].

20.3.7 Integration of thermal and othersensors

Sensors such as thermal contact sensors are rarely usedin isolation; their signals are generally integrated withthose from tactile arrays and other sensors to produceadditional information for identifying objects. For exam-ple, Dario et al. [141] demonstrate an approach in whicha thermal contact sensor is used in combination with atactile pressure sensing array and a dynamic tactile sen-sor used to characterize surface roughness, for discrim-inating among different objects. Different exploratoryprocedures, inspired by those used by humans [142] arecalled upon to resolve ambiguities.

20.4 Integration Challenges

A critical problem that we have not yet addressed is thedifficulty of connecting to a large and diverse array oftactile sensors. In 1987 Jacobsen et al. [143] cited therouting of wires as perhaps the most difficult problemin dexterous hand design and, to a large extent, this re-mains true today. However, some solutions to this prob-lem have been presented in recent years by either usingwireless sensors or by use of clever bussing for power andsignal connections. Shinoda and Oasa [144] embed tinywireless sensing elements in an elastic skin that uses aninductive base coil to provide power and signal transmis-sion. Each sensing element is a tuned resonator with adistinct resonant frequency whose resonant frequency isstress sensitive. Yamada et al. [68] use wireless sensorchips that use light transmitted through a transparentelastomer both for power and to communicate 6-stresscomponents to a power-receiver chip positioned safelybeneath the skin’s surface. Hakozaki and Shinoda [145]embed tactile sensor chips between 2 layers of conductiverubber that serve to provide means for power as well asa bussed serial communication, thus eliminating discretewiring. The shrinking dimensions of microprocessors alsomake it possible to mount devices for multiplexing, sig-nal conditioning, etc. in the immediate proximity of thesensors, thereby reducing the amount of “raw” informa-tion that must be relayed back to the robot.

20.5 Conclusions and Future De-velopments

In comparison to computer vision, tactile sensing alwaysseems to be “a few years away” from widespread adop-


tion. As explained in the introduction to this chapter,the reasons include physical problems (placement androbustness of sensors, wiring challenges) and the diver-sity of sensor types for detecting forces, pressures, localgeometries, vibrations, etc. As we have seen, the trans-duction and interpretation methods are typically differ-ent for each of these tactile quantities. However, thereare some basic issues that apply to tactile sensing in gen-eral. For example, sensors are generally located withinor beneath a compliant skin, which affects the quanti-ties that they sense in comparison to pressures, stresses,thermal gradients or displacements applied to the skinsurface.

When choosing tactile sensors for a robot arm or hand,it is effective to begin with a consideration of which tac-tile quantities are most desired and for what purpose.For example, the main concern is to obtain accuratemeasurements of loads or contact forces at sufficient datarates for force servoing, then intrinsic tactile sensing maymake the most sense. If manipulating objects with softcontacts and with sliding or rolling, curved array sen-sors for measuring pressure distributions, or perhaps lo-cal skin deflections, may be desirable. If exploring ob-jects to learn about their texture and material compo-sition, dynamic tactile sensors and thermal sensors maybe effective.

In an ideal world, one would incorporate all these tac-tile sensors in a robotic end-effector without regard tocost, signal processing or wiring complexity. Fortunately,the cost and size of transducers suitable for tactile sens-ing are steadily dropping and the ability to perform lo-calized processing is improving with surface-mounted de-vices on flexible circuits. In the near future it will beincreasingly possible to fabricate dense arrays of trans-ducers in-situ on contoured surfaces, using material de-position and laser machining techniques. In this way,robots may finally start to approach the tactile sensitiv-ity and responsiveness of the simplest of animals.

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