distributed active traction control system applied to the...

Distributed Active Traction ControlSystem applied to the RoboCupMiddle Size League

José Almeida1 , André Dias1 , Alfredo Martins1 , João Sequeira2 and EduardoSilva1

1INESC TEC - INESC Technology and Science (formerly INESC Porto) and ISEP/IPP - School of Engineering, PolytechnicInstitute of Porto, Porto, Portugal2Institute for Systems and Robotics, Instituto Superior Técnico/ IST, Torre Norte, Av. Rovisco Pais 1, 1049-001 Lisboa,Portugal� [email protected]

Abstract This work addresses the problem of tractioncontrol in mobile wheeled robots in the particular caseof the RoboCup Middle Size League (MSL). The slipcontrol problem is formulated using simple frictionmodels for ISePorto Team Robots with a differentialwheel configuration. Traction was also characterizedexperimentally in the MSL scenario for relevant gameevents. This work proposes a hierarchical traction controlarchitecture which relies on local slip detection and controlat each wheel, with relevant information being relayedto a higher level responsible for global robot motioncontrol. A dedicated one axis control embedded hardwaresubsystem allowing complex local control, high frequencycurrent sensing and odometric information procession wasdeveloped. This local axis control board is integrated in adistributed system using CAN bus communications. Theslipping observer was implemented in the axis controlhardware nodes integrated in the ISePorto Robots and wasused to control and detect loss of traction. An externalvision system was used to perform a qualitative analysisof the slip detection and observer performance results arepresented.

Keywords Traction Control, RoboCup Middle Size League,Embedded System, Friction Force, Slipping Observer

1. Introduction

Traction control is becoming a relevant problem inRoboCup Middle Size League (MSL) competitions. In thisrobotic competition a team of autonomous land robotswithout external sensors or human intervention must playsoccer in a semi-structured environment.

With the increasing size of the fields in the RoboCupMSL, the empty space increases as well(robot number anddimensions are constant).This causes both the players andthe ball to cover wider distances. This factor, along withadvances in perception and robot mechanics, has lead toa substantial increase in game dynamics and hence inhaving higher accelerations and decelerations from therobots to play the game effectively.

Therefore, traction optimization is becoming a relevantissue since traction forces at the wheel-ground interfaceare critical for the robot to achieve a highly dynamicperformance. In addition, a decrease in slippage alsoleads to higher energy efficiency and improves odometryinformation.

Traction optimization is also relevant to detect gameevents that cause changes in the wheel-ground interface.These can be, for instance, pushing or being pushed by

José Almeida , André Dias , Alfredo Martins , João Sequeira and Eduardo Silva: Distributed Active Traction Control System Applied to the RoboCup Middle Size League

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International Journal of Advanced Robotic Systems

ARTICLE

www.intechopen.com Int. j. adv. robot. syst., 2013, Vol. 10, 363:2013

1 INESC TEC - INESC Technology and Science (formerly INESC Porto) and ISEP/IPP - School of Engineering, Polytechnic Institute of Porto, Portugal2 Institute for Systems and Robotics, Instituto Superior Técnico/ IST, Torre Norte, Lisboa, Portugal* Corresponding author E-mail: [email protected]

Received 14 Sep 2012; Accepted 16 Jul 2013

DOI: 10.5772/56828

∂ 2013 Almeida et al.; licensee InTech. This is an open access article distributed under the terms of the CreativeCommons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,distribution, and reproduction in any medium, provided the original work is properly cited.

José Almeida1, André Dias1, Alfredo Martins1, João Sequeira2 and Eduardo Silva1

Distributed Active Traction ControlSystem Applied to the RoboCupMiddle Size LeagueRegular Paper

another robot, collisions with players or fixed structures,or ground unevenness. If undetected and if they arenot considered in the decision and control process,these events can lead to: excessive motor currents (stall)frequently exceeding battery and power system maximumvalues, mechanical degradation, game rule violations(such as faults) or unnecessary energy loss and localizationproblems due to odometry errors [1].

This work intends to contribute to the development of adistributed active traction system addressing the problemsreferred to previously.

Early studies on autonomous vehicle motion control reliedonly on kinematic models [2]. With the development ofnew sensors allowing a better world perception, it waspossible to improve motion [3]. However, part of thiswork focused on the study of motion dynamics for pathtracking. Other lines of research have relied on computingsuitable trajectories in the face of road holding constraintsand varying terrain topography [4].

Terrain (irregularity) poses additional problems inautonomous vehicles which are subjected to dynamicconditions. This can occur due to trajectory dynamics(induced moments and forces) or by variations insurface-wheel contact properties (due to terrainunevenness) [5]. Stringent performance requirements,combined with the traction problems mentioned above,introduce motion degradation and slippage. Therefore,avoiding slippage conditions while preserving motionas much as possible within the required performance isa necessary step. When traction loss cannot be avoided,it must be dealt with appropriately either by relaxingmotion requirements or using a motion planning strategy[6][2].

To the majority of the authors in the literature theobjective of traction is to reduce the slip rate betweenthe surface and the wheels. This makes it possible tominimize power consumption, improve motion controlperformance (trajectory tracking) and overall vehiclesafety (reducing the vehicle’s effective braking distance).Many technologies have been developed in order toreduce the slip and improve the adhesion performancefor any wheels or surface conditions. Such technologiesinclude: optimal slip ratio control [6], Model FollowingControl (MFC) [6], current disturbance observer [7],sliding mode measurement feedback control [8], slip ratiofuzzy control [9], slip controllers based on disturbanceobservers [10], back-EMF motor control [11] and roadconditions estimators [12][13][14]. In all control algorithmspresented, the wheel speed and vehicle speed are required,with the exception of the back-EMF, which requires themotor’s intrinsic parameters.

Considering the present state of the art, this work proposestwo major lines for improvement:• Extensive slip characterization in an aggressive (highly

dynamic) scenario, such as the Middle Size League(presented in section 4). Slip occurrence in the contextof the MSL is just starting to be addressed [15]. Thiswill have great relevance in the forthcoming changeimposed by the RoboCup where the carpet surfacewill be replaced by artificial grass. These issues are

not specific to the MSL scenario and are common inapplications with wheeled mobile robotic systems in awider range of scenarios.

• Developing the necessary skills in prio-preceptivemotion control. These are required for highperformance motion in highly dynamic situations.Research on the prio-perceptive characteristics ofrobot control is still required. In a similar approachto bio-inspired control, this work is part of a largerframework where this prio-perceptive motion controlplays a relevant role in the application of mobilerobotics in extreme environments and for high motionperformance situations. To support this approach,and contrary to the approaches mentioned above, thesolution provided does not require prior knowledgeand modelling of the wheel-ground interaction. Thisapproach presents a more "reactive" system usinglow-level information available in the control ofeach wheel, namely odometric and electrical currentinformation. This system should monitor and detectrelevant variations in these parameters and react, atwheel level (see section 3), both in a timely fashion andaccording to the detected event.

In order to validate this approach, several typicalMSL events in terms of traction were experimentallycharacterized (presented in section 4). Multipleevents are perceived from the qualitative and visualanalysis of electrical current and wheel velocities. Thischaracterization motivates and provides insight on thephenomenon analysis, along with the analysis of simplefriction models (see section 2), to develop a slippageobserver and to elaborate a traction control systemarchitecture. The slippage observer is developed usinginformation at motor (wheel) level. Therefore, it has a widerange of applications (it is not tied to a particular wheelor robot configuration). This work proposes a distributedhierarchical architecture for motion and traction control.At wheel level, a high frequency loop in the controlhardware makes it possible to detect events quickly andto immediately react to control those events. This leads toslip minimization in adherence situations and reducingover-currents in wheel blocking. In addition, informationon adherence and events is relayed to higher levels ofcontrol. This information is used in the active guidanceand control laws, and can determine transitions for theactive manoeuvres leading to adequate responses toevents. The experimental characterization, the tests andthe validation were performed using the ISePorto TeamRobots in the RoboCup MSL scenario. These robots haveconventional wheels in a differential wheel configurationpowered by electrical DC motors. The traction study forthis wheel configuration, used in the ISePorto Robots,is more suitable in comparison to the more popularomni-directional wheel MSL robots, as it can be appliedmore easily to other outdoor land-based robots. Althoughthe MSL scenario will be the scenario used, similarproblems arise in other mobile robotic applications, notonly in land robots, but also in other scenarios such asmarine surface robots. It is important to highlight that thework can be extended to Autonomous Surface Vehicles(ASV) [16]. These vehicles have multiple applications,

Int. j. adv. robot. syst., 2013, Vol. 10, 363:20132 www.intechopen.com

Figure 1. Autonomous Surface Vehicle ROAZ I with embeddedhardware DATCOS

including patrols, underwater mine hunting, harboursecurity and defence, oceanographic and environmentalmonitoring, bathymetry mapping, pollution control, andrescue missions. All scenarios where the operator is absentand the vehicle is remotely operated, or where control andmonitoring missions are performed autonomously, areimportant [17].

The traction control architecture and the embeddedhardware (DATCOS) presented in section 3 were appliedto the ROAZ I (see figure 1) [16] in order to prevent effectssuch as cavitation by controlling active thrust. To minimizethe thrust reduction propeller axial flow and propellerblocking in fishing nets. They were also used to minimizeWagner’s effects occurring in the presence of waves andperiodic loss of propeller immersion. These phenomenain water propulsion are similar to slip occurrence (section4.1), blocking (section 4.2) and wheels not touching thefloor (section 4.7)in wheeled traction.

The paper is organized as follows: section 2 focuses onthe traction concepts that are required to solve the tractionproblem characterization presented in section 4. Section 3introduces the traction system architecture implementedin the Middle Size League robots focusing on hardwareissues. Section 5 presents the observer state algorithm usedto determine the slipping condition. Section 6 presents thevalidation of slipping occurrence in the robot motion withan external measuring source.

The paper concludes with a short discussion on the resultsobtained and outlines topics that may be studied in thefuture.

2. Problem Formulation

In general, the dynamics of a robot depends on thedynamics of the terrain where it moves. For instance,outdoor locomotion must typically account for loose soilconditions, for instance, when moving on sand. Theseconditions cause the robot to slip. In such cases, it isnecessary to identify suitable models for the contactbetween robot and terrain surface. However, in a fairnumber of applications, namely indoors, the terrain isoften rigid, with time invariant dynamics. This is the casefor the RoboCup MSL competition which motivated thework presented in this paper. Nevertheless, the robotsmust perform harsh manoeuvres when playing, requiringhigh accelerations. That may cause the wheels to slip,reducing the traction. The control systems are thenrequired to compensate for the disturbance introducedby the wheel slippage. Moreover, prior knowledge on thecharacteristics of the contact between the robot’s wheelsand the terrain surface is in general difficult to identify,which makes it hard to include these features whendesigning motion controllers. For a mission to be properly

Figure 2. Forces applied to the ISePorto Player

executed, it is desirable that traction is maximal in orderto maximize the conversion of energy from the motorsinto motion. Therefore, the wheel slippage must be keptto a minimum, which also improves the quality of theodometric information. Multiple wheel slippage detectionstrategies are described in the literature. Whenever thereare unactuated wheels connected to encoders, slippage canbe detected by comparing their velocities with those ofthe actuated wheels (see for instance [18]). A high levelstrategy may come from combining information from theIMU (Inertial Measurement Unit) and the GPS (GlobalPositioning System) with information from the encodersand the robot’s model, see for instance [19], for a strategyusing Extended Kalman Filtering.

Figure 2 illustrates the main forces acting on each actuatedwheel of the ISePorto Robots. Mw, Fd, N, Fm, and Vw stand,respectively, for the torque applied by the wheel actuator,the friction force at the point of contact between the wheeland terrain surface, the weight supported by the wheel,the propulsion force at the contact point generated by thetorque applied by the wheel actuator, and the angularvelocity of the wheel. For the sake of simplicity, for theslippage analysis, the wheels are assumed to be rigid.Furthermore, the dimensions and physical properties ofthe wheels and of the rest of the robot are assumed tobe time invariant. As such, masses and inertia do notvary. The effect of the castor passive wheels in the overalldynamics is also assumed to be negligible, their purposebeing only to maintain the necessary static stability. It isworth highlighting that the model can easily be extendedto account for the castors, namely by considering theweight transfer that occurs when braking and acceleratingbetween the actuated wheels and the castors. However,the general principles developed below still apply. Thisis identical for more complex dynamic conditions, forinstance, when the inertia of each wheel varies alongthe trajectory, when the vehicle is requested to performa tight curved trajectory that deforms the wheels or thestructure of the robot, or when the type of contact betweenthe wheels and the floor varies along the trajectory. Asingle wheel model was used in order to design a slippagedetector/observer. Note that there is no lack of generalitywith this approach. In fact, each wheel of a generic robotcan be equipped with a slippage detector. If the detectordepends on configuration parameters, it is possible to use


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high level learning strategies to adjust the performance ofthe global detector to different terrain conditions.

The friction force Fd is assumed to be of Coulomb type,

Fd = N × µ × sgn(V) (1)

where N and µ respectively stand for the contact forcebetween the robot and the ground (normally to the surfaceat the contact point), and the static friction coefficient,and V is the linear velocity of the wheel written inits own frame. More complex friction models could beused, such as the one developed by Dahl and LuGre[20]. Although both models require parameters such asshape-stress curve and stiffness coefficients, informationwill only be available if several tests are performed. Assuch, this is not coherent with the proposed approach. Theforce balance equation (absolute values) yields

(Fm − Fd) = M × V (2)

The force driving the wheel, Fm, is generated by anelectrical actuator as

Fm =1r× Km × Imotor (3)

with Km, Imotor, and r representing, respectively, theintrinsic parameters of the motor, the electrical current,and the wheel radius. Using (3) in (2) yields

Fd =1r× Km × Imotor − M × V (4)

Assuming that the friction properties are constant duringthe movement, slippage is characterized by a suddendecrease in the friction force. Differentiating (4), with Kmand M assumed constant, provides the basic way to detectslippage

Fd =1r× Km × Imotor − M × V (5)

When Imotor < 0 and V > 0, slippage occurs for certainas the wheel’s linear velocity increases when the motorrequires less power. If Imotor < 0 and V < 0, there may alsobe slippage, depending on the relative values of the termsin (5). However, such a case is of no practical interest to thepurpose of this study. This means that the linear velocityof the wheel increases at a decreasing rate when the powerrequired by the motor is reduced. The pratical result forsuch a situation amounts to a wheel slipping slowly, whichin fact is in accordance with Fd, which tends to have asmaller absolute value than when Imotor < 0 and V > 0.

This seldom occurs in the MSL robot competitions as therobots have a relatively low weight. To detect slippage, weare interested mainly in the sign of the terms in (5) (thougha threshold on Fd must also be used to avoid disturbancesleading to erroneous detections).

However, differentiating the observed velocity twice,V, may increase noise effects. Therefore, a strategy isproposed below to estimate the sign of V withoutdifferentiating V. Assuming that the control system ofthe robot specifies a linear reference velocity for thewheel, Vre f , which can be positive/negative without losinggenerality if the wheel is moving forwards/backwards. In

the RoboCup MSL competition, slippage is more likelyto occur when velocity reference commands are sent forthe wheel to change its velocity as this implies suddenaccelerations/decelerations.

The current reference velocity, Vre f and its variations,∆Vre f , define eight conditions of interest for the observedwheel velocity, listed in Table 1.

1a ∆Vre f > 0 and Vre f > 0 → V > 0 and V > 01b ∆Vre f > 0 and Vre f > 0 → V > 0 and V < 02a ∆Vre f < 0 and Vre f > 0 → V < 0 and V > 02b ∆Vre f < 0 and Vre f > 0 → V < 0 and V < 03a ∆Vre f > 0 and Vre f < 0 → V > 0 and V > 03b ∆Vre f > 0 and Vre f < 0 → V > 0 and V < 04a ∆Vre f < 0 and Vre f < 0 → V < 0 and V > 04b ∆Vre f < 0 and Vre f < 0 → V < 0 and V < 0

Table 1. Conditions on the reference velocity Vre f

Conditions 1a and 4b represent fast accelerations,whereas conditions 2b and 3a represent fast decelerations.Conditions 1b and 4a represent slow accelerations,whereas conditions 2a and 3b represent slowdecelerations. Fast accelerations and decelerationsrepresent the conditions where the probability forslippage is higher. This is visible in sgn(V). In a sensethis provides an optimistic form of detecting slippage andprovides the foundations for the slippage test described inthe following section.

3. Traction Control Architecture

The traction control architecture is characterized by twohierarchical levels of action (see figure 3). The lowerlevel is implemented in a dedicated hardware solution ineach wheel.It implements local axis control allowing force,velocity or position feedback. It is responsible for reducingthe occurrence of slip, optimizing the torque applied tothe motor and sending all relevant information, bothcontinuous values and discrete events, to the higher levelvia CAN. This allows the high level control manoeuvresto deal with and adapt to the detected event. Globalmotion control is performed at a high level. At this levelthere is knowledge on the robot’s wheel configurationand the appropriate velocity or force references, whichare sent to each traction axis in order to perform aspecific motion manoeuvre. In the higher control level itis possible to use information regarding loss of adherenceor other characterizing events from the lower level so asto select the appropriate manoeuvre or tune parametersaccordingly.The high level control manoeuvres are usuallyimplemented in a central processing system and canalso use additional information from other sensors, suchas accelerometers or navigation subsystems, to furthercharacterize robot motion.

Detecting motion problems makes it possible to correctand adapt the path[21] or the control manoeuvres.The architecture is also distributed in the sense that itimplies the lower level axis control to be implementedat each wheel, possibly in separate controllers with somelevel of local slip control. This separation of physical


Figure 3. Traction System Architecture

implementations allows flexibility in terms of robotapplicability.

3.1. Traction Control Hardware

New motion controllers and power drives were developednot only to improve traction control, but also the overallrobot reliability. The new axis control node DATCOS(Distributed Active Traction Control System), which isbased on a hybrid DSP (Digital Signal Processor) witha CAN (Controller Area Network) interface (see figure4), makes it possible to reduce the number of cables,providing torque measurements for traction control. TheDATCOS is small-sized (4cm x 4cm), low-powered andprovides computational capabilities supporting relativelysophisticated control algorithms, such as slip, velocity andtraction/force with a high rate at 1KHz.

3.2. ISePorto Robot

The slipping detection observer described in section 5was implemented in the distributed embedded systemmotion control (DATCOS) in the ISePorto soccer robots(see figures 4 and 5).

The robots have conventional wheels in a differentialtraction arrangement powered by electrical DC motors.The traction control study for this traction configurationapplied in the ISePorto Robots has the advantage over themore popular omni-directional wheeled MSL robots beingused in many other land-based robots with conventionalwheels. This is even more relevant in outdoor terrainswhere the presented observer will improve the navigationquality. Each driving wheel has a motion control unit

Figure 4. Axis control node DATCOS developed by the ISePortoMSL Team

Figure 5. ISePorto Robotic soccer player

performing the slipping estimation, implementing a wheelmotion control loop and connected by a CAN bus [22].In addition, this embedded controller also integrates themotor power driver. The main robot CPU controls theglobal motion issuing references to the control nodes andreading relevant information. The current ISePorto maincomputer is based on a single-board computer running aRT-Linux operating system and high level navigation andcoordination software.

4. Traction Issues in the Middle Size League

In a middle size game the robots are exposed to dynamicvariations in adherence [20] values between the surface ofthe field and the wheels. This is caused by events such aspushing, blocks, collisions and crossing of the field lines.All these disturbances can be observed in the current andvelocity values at each wheel. A set of tests were realizedusing an ISePorto Team Robot in order to characterize thereferred slip affecting events occurring in a soccer game.The tests where conducted in a middle size field and all themotor control nodes (DATCOS) were connected to a CANbus with a baud rate of 1Mbit. The DATCOS performedthe current and odometry acquisition with a sample timeof 1kHz. The electrical current acquisition was made witha 12bit A/D converter and odometry with a quadraturedecoder, giving 5000 ticks per wheel turn. The currentand the odometry were filtered using an Equiripple FIRfilter of order 4. The values applied to the filter werecalculated using the Remez Exchange algorithm [23]. Inall tests step voltage references were applied to the tractionmotors in open loop in order reduce the controller-induceddisturbances in the detection analysis.

4.1. Acceleration

In this test, robot acceleration is presented for an initialstopped position in response to a step motor reference.This corresponds to a typical robot motion event leading topotential slippage. In figure 6, it is possible to observe theinstant when the slip occurred, as well as its period, duringan acceleration process. Some loosing/recovering tractioncycles can also be observed during a period of 0.25s.

The slip is characterized by an abnormal low current andhigh velocity.

4.2. Blocking

This test corresponds to the situation where the robotcollides with another robot and is capable of pushing


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Figure 6. Slip occurrence in acceleration

it. Blocking occurrence is characterized by an increase inload.

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Figure 7. Blocking caused by robot or obstacle

This phenomenon requires higher traction and thereforehas higher probability of reaching adherence limitscausing slipping (see figure 7).

4.3. Pushing

This experiment corresponds to the robot being pushed byanother robot when moving.

Pushing presents a measured velocity value greater thanthe reference used by the application and low current inrelation to the expected velocity (see figure 8).

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Figure 8. Pushing by another robot

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Figure 9. Reverse the goalkeeper motion

4.4. Motion Reversal

Motion reversal occurs when a robot suddenly reversesits veleocity and is a frequent goalkeeper movement, as itmoves mainly in both directions in a small area.

Without traction control, it is possible to observe that ittakes almost 1s to reverse the motion and recover tractionagain (see figure 9). This delay makes the goalkeepervulnerable to the possibility of a goal.

4.5. Braking Process

In this experiment, a reference command was appliedfrom maximum value to zero. The braking process in theISePorto Robot can be observed in figure 10.

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Figure 10. Braking process in ISePorto Robot

The robot takes 600ms to stop without the traction controlsystem. This is caused by the robot’s low adherencebetween the field surface and the wheels. With the tractioncontrol system, it is possible to reduce the slipping period,improving not only actuation, but also the quality of thelocalization and navigation.

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Collision

Figure 11. Left-side collision with handspike occurrence


4.6. Collision

Lateral Collision with a Moving Robot

Lateral collision presents two different behaviours. Therobot can either present a lateral force that will influencethe motion, or a handspike occurrence that will cause onewheel to lose contact with the field. This will cause anincrease in friction force (see figure 11).

Collision with Fixed Landmark

In the collision with a fixed landmark the robot will presentlow or zero velocity depending the its position in relationto the landmark (see figure 12). The current increases andmaintains a higher value.

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Figure 12. Collision with a landmark

In the previous experiments a fixed landmark was usedand after the collision the robot turned left. This can beobserved in figure 12 because the right wheel maintains alow velocity and the left wheel is stuck.

Collision with Still Robot

A collision with a still robot is different from a collisionwith a landmark as the robot can overcome the inertiaimposed by the other robot. A slower velocity increase anda slower current decrease will occur. This does not occurif the robots collides with a fixed obstacle (see figure 13).The test presented in figure 13 was performed using tworobots with different weights.The still robot with 40kg andthe collision causing robot with 20kg.

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Figure 13. Collision with a still robot

4.7. Wheel without Contact with the Field

During a middle size game it is usual for the ISePortoRobot and an opponent to become stuck and one or bothwheels to lose contact with the ground (see figure 14).

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Figure 14. Left wheel loses contact with the field

The loss of contact of one or two wheels with theground, if undetected, usually introduces relevant errorsin the navigation system when odometry is used. Thisoccurs as a result of the inherent shortcomings of theodometric process even when integrated with otherlocalization sensors. Since higher level control keeps thewheel reference signals, when the wheel regains tractionthe robot suffers a sudden unexpected movement. Thiswill cause degradation in the localization of the robot inthe field, mainly in short-term heading estimation errors .

4.8. Slip Over the Field Line Marks

Due to the fact that field line marks are implemented withplastic strips laid over the carpet ground, robot motionover them can be affected.

It is only possible to observe a slip over a field line markwhen the robot is in an acceleration/braking process. Ifthe robot is moving at constant velocity, the disturbancecaused by the lines is not detected (see figure 15).

4.9. Unevenness of the Ground Field

During the MSL games field degradation occurs and figure16 expresses the instant when the left wheel encountersuneven ground.

This disturbance will cause motion degradation which inturn will influence the path defined by the higher levelcontrol application.

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Figure 15. Left motor cross the field line mark

4.10. Wheel Characteristics

A comparison between two types of wheels with differentadherence properties is presented in figure 17. On theleft figure, it is possible to observe a higher adherence


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Accordingly, this step can only be carried out if thematching procedure was already performed for the firsterror image. Therefore, only areas that were not removedduring the first matching procedure are extended bycorresponding areas of the subsequent error images.Otherwise, the noise (falsely detected areas) would causean enlargement of incorrectly detected areas. The red shortdashed rectangles in Figure 8 mark 2 examples of suchcorresponding areas. Resulting areas that are too largeare removed from the error images In and In+1. This isindicated by the areas in the right lower corner of errorimage In in Figure 8. As can be seen, the resulting errorimage In from Figure 8 is used as input (error image In) inFigure 7. Without the extension of the areas, the midmostcandidate in Figure 7 would have been rejected.

As some real moving objects are sometimes not detectedin an error image as a result of an inaccurate optical flowcalculation or (radial) distortion, the temporal matchingwould fail. This could already be the case if only onearea in one error image is missing. Thus, candidates thatwere detected once in 3 temporal succeeding error imagesand 4 greyscale images (original images), respectively, arestored for a sequence of 3 error images subsequent to theimage where the matching was successful, cf. Figure 9(a).Their coordinates are updated for the succeeding errorimages by using the optical flow data. As a consequence,they can be seen as candidates for moving objects inthe succeeding images, but they are not used within thematching procedure as input. If within this sequenceof images a corresponding area is found again, it is

stored for a larger sequence of images (more than 3) andits coordinates are updated for every succeeding errorimage. The number of sequences depends on the followingcondition:

ξ =

{c+cc−c | c �= c2c | c = c,

(13)

where c is the number of found corresponding areas andc is the number of missing corresponding areas for onecandidate starting with the image where the candidatewas found again. If ξ < 0 ∨ ξ > 10, the candidate isrejected. Moreover, the candidate is no longer stored if itwas detected again in 3 temporal succeeding images. Inthis case, it is detected during the matching procedure.An example concerning to this procedure is shown inFigure 9(b). As one can imagine, error image In inFigure 9(a) is equivalent (except area-extension) to In+1in Figure 7, whereas error image In in Figure 9(b) isequivalent to In+2 in Figure 9(a).

For a further processing of the data, only the position(shown as small black crosses in the left lower corners ofthe rectangles in Figures 7 and 9) and size of the rectanglesmarking the candidates are of relevance. Thus, for everyerror image the afore mentioned information is storedfor candidates that were detected during the matchingprocedure, for candidates that were detected up to 3 errorimages before and for candidates that were found again(see above). On the basis of this data, candidates that arevery close to each other are combined and candidates thatare too large are rejected.

(a)

(b)

Figure 9. Preventing rejection of candidates for moving objects that were detected only in a few sequences. (a) Storage of candidatesfor which a further matching fails. These candidates are marked by a blue dotdashed rectangle. The green dashed rectangle marks acandidate for which a corresponding area was found again and the red short-dashed rectangle marks a candidate with successful matching.(b) Storage of candidates for which a corresponding area was found again. The 2 areas drawn with transparency in error image In indicatethe position of the candidates, but they are not part of the error image.

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Figure 16. Ground unevenness field only in the left wheel

comparatively to the one on the right for a test with anaccelerating step followed by a motion stop. Adherenceis higher (figure 17 left) as the current for lower velocitiesincreases as the robot starts moving.

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Figure 17. Relation between velocity and current with twodifferent type of wheels.

This leads to a higher friction force and thus to ahigher torque load on the motor. On the right, it ispossible to observe periods of slippage as "cycles" onthe current/velocity "descent phase". Traction loss isrepresented by a sudden increase in wheel velocity anddecrease in current (a friction force). When the wheelregains adherence the velocity decreases and the frictionforce condition is resumed.

4.11. Remarks

In the analysis performed, normal motion events suchas acceleration, braking or motion reversal can leadto slippage when friction limits are reached. This ischaracterized by a sudden increase in wheel velocityand consequent decrease in current. These cases will beaddressed at the wheel level by reducing motor referencecommand in order to regain adherence. When detected,the reaction should be quick and should take place onthe local control loop. Other events occurring in the MSLscenario are changes in friction properties between thewheels and the ground, either due to surface changes suchas crossing lines or terrain unevenness. In these cases, theslip should also be addressed at low level control as slipsare transient motion disturbances.

Another different situation occurs when one or morewheels lose contact with the ground. Here, the slip

behavior occurs for a potentially long period of time andit must be dealt with at the global robot motion controllevel. Therefore, it is necessary to convey this informationto the central processing system. The event will then beconsidered in the localization process to reduce energyloss and also to avoid sudden mechanical loads causedby the wheel regaining contact. This can be performed bystopping the motor.

The blocking situation is caused by a robot colliding withan obstacle. This obstacle can be either a fixed landmark oranother player. Collision with an opponent player in theMSL can be translated as a game fault when the charge isprolonged or violent. In the case of collision with anotherplayer, depending on the adherence conditions, the playermay be pushed. In these cases, slippage can initially bedealt with at a wheel low level, and if it is persistent, thiscorresponds to the robot being stuck either by collidingwith a fixed landmark or with another robot with highertraction. Persistence slippage must be dealt at the higherlevel of robot motion control. Persistent load increase canlead to mechanical or electrical damage and should beprevented at robot level by changing the current motionmanoeuvre.

A similar situation occurs when another player pushes therobot. In this case, the cause for the disturbances is wheelvelocity and motor torque load and thus in current leadto the slip phenomenon. This must be dealt with by thehigh level robot control since it is not a transient event andcannot be solved at wheel level.

5. Slippage Observer

For highly dynamic robot scenarios such as the roboticsoccer environment, the terrain surface characteristicsand robot dynamic conditions restrict the use ofstandard literature models for quantitative friction forcedescription. These scenarios are not as complex andtherefore cannot represent the richness of the robotdynamics. Furthermore, they are also more costly inrelation to the information provided. However, the modelsprovide qualitative information which makes it possibleto interpret the evolution of robot dynamics. Therefore,using equation 4 and a state defined by the motor currentImotor and wheel velocity V, an algorithm (observer)is presented to determine the slipping condition. Thealgorithm observes the evolution of the variable and theirtransitions occurring in a topological frame (hierarchicalinformation), detecting the different phases expressed inthe friction force.

Algorithm 1 Observer

if Imotor < 0 AND V > 0 thenStatus: Slipping process

else if Imotor > 0 AND V < 0 thenStatus: Adherence process

else if Imotor > 0 AND V > 0 thenStatus: Acceleration process

else if Imotor < 0 AND V < 0 thenStatus: Breaking (deceleration)process

end if


The information process hierarchy leads to four discretestates which determine the wheel friction condition:adherence, slipping, acceleration and braking.• Slipping process - Slipping process - characterized by a

significant decrease in motor current Imotor and by adrastic increase in wheel velocity V. When the robotenters this state, friction force is reduced, which leads toa loss of adherence areas and to an increase in velocity.

• Adherence process - characterized adherence areas onthe surface. In order to regain adherence, an increasein friction force is required. In these cases, there isan increase in current Imotor and a reduction of wheelvelocity V.

• Acceleration process - characterized by an increase incurrent Imotor and wheel velocity V. This state occurswhen there is a transition from a static friction forcecondition to a dynamic friction force condition. Whenthe wheel is accelerating it can go from an adherenceto a slipping condition depending on the frictioncapability of the wheel/surface.

• Breaking process - characterized by a decrease in currentImotor and an increase in wheel velocity V. When thewheel brakes it goes from a dynamic friction forcecondition to a static force condition. The ability toregain the connection points depends on the physics ofthe wheel/surface.

The observer provides an index (DATCOSTCS)characterizing the friction condition and this wasimplemented in the embedded distributed motion controlsystem for the ISePorto Robots [22].

A set of thresholds configured at the initialization stageare used to determine when a CAN bus message issent to the robot global motion controller containing thecorresponding friction state and index value (see figure 3).

The performance of the algorithm implemented in theDATCOS embedded system can be observed in section 6.2.The qualitative analysis with an external vision system onthe developed slippage observer is visible in section 6.4.

6. Results Using an Exogenous Slipping ConditionObserver

In the experimental work described above 4 the slipoccurrence in differential drive DC electricity-poweredmobile robots was studied. This made it possible tounderstand the phenomenon associated with the tractionprocess and provided insights on the traction controlarchitecture and on the development of a traction controlsystem. Nevertheless, the identification and validationof slipping in the robot motion required an externalmotion measuring source. An experimental setup wasimplemented combining an external computer visionprocess to obtain the required information, as depicted infigure 18, and the data on the axis control node DATCOS .

An ISePorto Robot was used in the tests. The computervision system provides information on the robot’sposition. This made it possible to determine the velocityand orientation of the robot. Simultaneously, information

Figure 18. Experimental setup

on the motor current and encoder odometry is recorded foreach traction motor/wheel. The synchronization betweenthe two CPUs, robot and an external computer, used inthe experimental setup was achieved using the NetworkTime Protocol (NTP) [24]. The NTP was a requirement toachieve time accuracy and reliability in the experimentalscenario.

The robot’s position and orientation was determinedusing two circle markers (orange and blue) with a fixeddistance detected in the overhead image (see figure 21).In order to achieve good vision based on the robot’sposition accuracy, it was necessary to determine thegeometric and optical characteristics of the internal camera(intrinsic parameters) [25], as well as the 3D position andorientation of the camera frame relatively to a certainworld coordination system (extrinsic parameters) [26] andprecise colour segmentation. The image processing stepsare summarized in figure 19.

6.1. Physical Setup

The experimental physical setup consists of one USBPhilips camera at 15 FPS with 640x480 resolution, blueand orange 15cm diameter circular markers, an IntelPentium Dual Core PC with 2GHz and 2GB memoryfor image acquisition and processing, and a RT Linuxoperating system. The ISePorto experimental robot setupis the hardware presented in section 4 plus the triaxialMicroStrain 3DM-GX1 accelerometer. A test target wasused in order to verify the external vision system imageacquisition quality, with particular emphasis on the coloursegmentation precision. This target had the followingcharacteristics:• Full 30cm circle markers

• Inter marker distances is 25cm in x and in y 33.5cm

• Target to camera distance 2.78m

Figure 19. Image processing


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Figure 20. Know target for accuracy test

Figure 21. Circular markers image segmentation

By analysing the identification of markers (figure 20), itwas possible to find a medium error of 20mm . This errorvalidates the external vision tracking system as a suitablemeasurement setup for the intended purpose.

6.2. Embedded Control System Slipping Observer

The following section will describe the state observerperformance implemented in the embedded systemDATCOS motion for wheel state detection. In figure 22 onthe left it is possible to observe velocity and current graphsfor each wheel, while on the right it is possible to see theobserver performance.

Having analysed the graphic, it was possible to observethe occurrence of slipping on the right wheel, with thecorresponding identification by the observer at instant4.19s. Information from the observer implemented in thelocal motion control board (DATCOS) is transmitted via

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Traction Period

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Figure 22. Slipping observer performance

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Figure 23. Region transitions detected by the observer algorithmimplemented in the DATCOS embedded system

CAN bus to the higher robot motion control level withthe message Event Status (see figure 3). In figure 23, itis possible to observe the behaviour of the embeddedalgorithm system detection transitions related to figure22. The four regions represented in the figure 23 are thepossible states identified by the implemented algorithm.High dispersion is observed in two regions, traction andslipping. A movement without disturbances in terms ofadherence loss implies a lower dispersion in the figurepresented with a highly concentrated set of points aroundthe origin. The observer performance was measured fora different set of wheels with higher adherence capability.The data are presented in figure 24. In figure 24, slippingand traction instants are identifiable by the observer at4.2/4.4s and 4.3s respectively.

This analysis made it possible to determine the "quality"of the robot’s movement, verifying absence of significantslippage, as having low dispersion in the points presentedin the previous figure. The non existence of slippinginstants (as detected in figure 24 ) would imply a furtherreduction of dispersion with a high concentration aroundthe origin.

6.3. Traction Tests Using an External Vision System

This section presents the results obtained with theexternal vision system, the triaxial MicroStrain 3DM-GX1accelerometer and the embedded motion system data.Comparing vision data with velocity measurements, it waspossible to obtain highly coherent results, validating theembedded system that gathered the information.

In figure 26 slipping occurs with higher incidence on theright traction wheel, which causes disturbances in therobot’s motion at 0.8m.In the braking process, friction forceand wheel velocity variations are detected. Adherence and

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Figure 24. Observer performance for higher qualitywheels(higher friction)

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Figure 25. Region transitions detected by observer algorithm inthe embedded system for second wheel set


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Figure 26. Robot movement for a 1.2m/s reference

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Figure 27. Robot movement for 1.2m/s reference

slipping instants are identified in figure 26 from time 9.8sto 10.2s.

In figure 27 it is possible to confirm that most of thetime the robot motion does not present slipping. Theonly exception is during the braking process with thetransitions identified between adherence and slipping.The external vision system does not make it possible toidentify disturbances in each traction wheel, presentingonly a global decrease in velocity. By analysing the threefigures, the results of the embedded system are confirmedby the external motion data collected.

6.4. Qualitative analysis - Slipping Observer vs. ExternalVision System

This section presents a qualitative analysis for theslipping with the data collected by the external visionsystem. Figures 28 and 29 demonstrate the traction wheelbehaviour with information given by the embeddedmotion control system and external vision tracking.

The figure plots identified by Right wheel slip or Leftwheel slip present slippage captured by the external visionsystem and by the embedded motion. Figure 28 shows thateven in low reference commands slipping has occurred


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Slip

Figure 28. Slipping obtained by vision comparing withthe observer algorithm data for a 0.5m/s reference command

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Transaction from slip to adherence

Figure 29. Slipping obtained by vision comparing withthe observer algorithm data for a 1.2m/s reference command

with higher prevalence around time instant 5.25s. Thisevent was promptly identified by the observer algorithmimplemented in the embedded system.

Figures 28 and 29 show that the embedded systemdetects slippage in accordance with slipping instantsdetected by the external system. During the brakingphase, it was possible for the embedded system toidentify loss of adherence in accordance with the externalvision. However, a performance increase in the detectionalgorithm would be expected if the measurement ofcurrent included signal. Here only an absolute value formotor current is measured.

7. Conclusions and Future Work

The problem of traction in mobile wheeled robots inthe RoboCup MSL league scenario was analysed in thispaper. In particular, this work provided a study on slipoccurrence in differential drive DC electrical-poweredmobile robots. Traction control problems in this scenariohave a much wider applicability in mobile robotics.Traction loss was characterized for the set of possible gameevents, ranging from excessive acceleration to collisions.The analysis was performed using high rate (1KHz)measurements of electrical current and odometry in eachmotor using the distributed local control axis/powersystem (DATCOS). This characterization, along withsimple friction models, motivated a discrete slippingobserver proposal along with a hierarchical motion

control architecture. The approach to overall tractioncontrol relying on electrical current data, combined withmotion data, does not depend on prior knowledge onthe operating surface or the robot’s motor model. Inaddition, traction control is achieved in real time and ina distributed form. The embedded slipping observer forthe mobile wheeled robots was analysed in this paper. Anexperimental setup based on an external computer visionsystem was implemented providing an independentsource of motion data and allowing a qualitative analysis.The overall motion control infrastructure is alreadyimplemented and tested in RoboCup competitions. Thisinfrastructure makes it possible to reduce slip by detectingdiscrepancies in current and motor velocity, and as aconsequence reducing reference commands. It also relaysto higher hierarchical levels the information requiredto re-plan the motion when necessary. By comparingvision data with the detection of slipping events, it waspossible to confirm that the results are highly coherent,which validated the embedded system detection.Local slipcontrol is currently implemented, validated and tested inoperational conditions and should be tested in additionalscenarios. Higher level response to drastic tractionproblems is currently under development. RoboCupcompetitions will present a relevant scenario for thesevalidation tests. In the future, the system can be furtherdeveloped by including the dynamic robot model in thehigher level response. The developed slippage observer,combined with specific motion control hardware, willmake it possible to develop an adaptive generic tractioncontrol system in the future where occurrence rate eventscould be used as feedback in a terrain learning strategyapproach. This would allow for the robot to adapt itselfto specific terrain characteristics and define acceleratingramps according to the detected event rate. This work wasalso extended to other robotic motion control applications,particularly to marine robotics, preventing effects such ascavitation, controlling active thrust and minimizing thrustreduction due to the propeller’s axial flow.

8. Acknowledgment

The authors acknowledge the major support given by theISEP-IPP Institution, by the INESC TEC, to this project.This work is financed by the ERDF - European RegionalDevelopment Fund through the COMPETE Programmeand by National Funds through the FCT - PortugueseFoundation for Science and Technology within projectFCOMP-01-0124-FEDER-022701.

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