Tactile Sensing and Compliance in MicroStressbot Assemblies Ratul Majumdar*a, Vahid Foroutan a, Igor Paprotnya

aUniversity of Illinois at Chicago, 851 South Morgan Street, Chicago, IL, USA 60607

ABSTRACT

Microassembly is one of the applications successfully implemented by group of individually-controllable MEMS microrobots (MicroStressBots). Although the robots are controlled using a centralized optical closed-loop control systems, i.e., a camera mounted on top of a microscope, compliance and self-alignment were used to successfully reduce the control error and permit precise assembly of planar structures. In this work, we further explore the possibility of using compliance to facilitate docking between MicroStressBots. The forces generated by the docking surfaces create a local attractor (pre-image of the goal configuration) that facilitates alignment between the two structures. Through this interaction the robot senses and aligns its position to match the desired configuration. Specifically, in this work we examine two cases: a) docking of two microrobots with straight front edges that promote sliding, and b) docking of two microrobots with patterned edges that restricted sliding between the two robots. In the former case, the robots are engaged in mutual alignment, which is akin to pairwise Self Assembly (SA). This allows generation of highly accurate seed-shapes for further assembly. In the latter case, the robots with matching pattern edges can dock and successfully align. Here, the patterned edge functions as a lock-and-key mechanism, and is akin to the selective affinity in SA-systems. The difference however between a system of MicroStressBots and SA is that MicroStressBots contain active on-board propulsion compared with passive SA systems.

Keywords: MicroStressBots, MEMS, Microrobotic cooperation, compliance

1. INTRODUCTION Microscale mobile robotics systems such as electrostatically driven stress-engineered MEMS microrobots (MicroStressbots) [1], resonating stepping robots [2], stick-slip magnetic walkers [3] and microscrew-based swimmers [4] have been the center of development in the last decade. Virtually all future envisioned microrobotic applications rely on the combined actions of many microrobots, hence multimicorobotic control is currently an active area of research. The high level of underactuation present in such systems makes the simultaneous control of several microrobots significantly more challenging than control of single microrobot. Theunderactuation results from the limited ability of the microrobot to decode a global control signal. The assembly of these structures are programmed and controlled by docking the individual robots, whereas defect formation is avoided by using non-colliding paths, enabling virtually defect-free assembly. The rigidity of the assembled structure is maintained through mutual complaint interaction between the immobilized robots and through electrostatic attraction to the substrate. The term compliance is used to denote the change of pose of one rigid body in accommodation to forces exerted by a second rigid body and friction [5]. This work shows the implementation of microassembly by maneuvering multiple microrobots to dock together and form larger structures. Multi-microrobotic assembly was previously proposed in [6], however related works with magnetically actuated robots was presented in [7]. The magnetic assembly (and disassembly) described in [7] relies on selective electrostatic clamping using the substrate as temporary anchor to achieve independent control. The control methodologies proposed in [8] also had a related multi-robotic control mechanism that does not rely on specialized substrate. Similar to the mechanism presented in [6] and this work, control strategy in [8] depends on the energy differences in the response of the microrobots to the same global control signal. In [8] the robots are differentiated by how their entire chassis interact with the global magnetic field. In order to ensure significantly different motion to enable independent control large design differences were necessary in the system which would prevent simultaneous control of large number of robots. Moreover, the performance of these robots in terms of compliance will be extremely poor. Our system, on the other hand, relies on intersecting trajectories, rather than component affinity and energy minimization to promote structural aggregation. This helps in control of defect formation through collision avoidance and non-intersecting trajectories. *rmajum2@uic.edu; Phone :(312) 286-4403

Next-Generation Robots and Systems, edited by Dan O. Popa, Muthu B. J. Wijesundara, Proc. of SPIE Vol. 9116, 911604 · © 2014 SPIE · CCC code: 0277-786X/14/$18 · doi: 10.1117/12.2058239

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USDA

L, steering arm

2. MICROSTRESSBOTS The stress-engineered MEMS microrobot, MicroStressBot for short, consists of an untethered scratch drive actuator (USDA) [1] which provides forward motion and a curved steering-arm actuator that determines whether the robot moves in a straight-line or turns. The USDA is composed of a 120um×60μm backplate and a 1.5µm tall bushing. The steering arm actuator consists of a 120 to 160µm long cantilever beam with a circular pad and a 0.75 μm-deep dimple to prevent irreversible sticktion. Fig. 1 shows the schematic of the MicroStressBot.

Figure 1: The kinematics of the MicroStressBots.

Surface micromachining PolyMUMPs foundry process [9] is used in fabricating the MicroStressBots. This process includes three layers of polysilicon, namely Poly1, Poly2 and Poly3, with Poly1 fixed to the substrate while Poly2 and Poly3 being the suspended layers. The chassis is formed from the Poly2 layer, whereas the bushing is formed from both the Poly1 and Poly2 layers. Two layers of Phosphosilicate glass (PSG) provide the sacrificial material between the polysilicon layers. The planar steering arms can be curved out-of-plane using a stress engineering process. This process uses reconfigurable shadow masks to add (post-release) a patterned layer of a stressor material (usually Chromium, Cr) with high compressive stress to provide an upward curvature [10]. The deflection of the steering arm can be controlled by precisely defining the thickness of the deposited material and the area covered by the stressor layer.

A grid of insulated interdigitated electrodes has been prepared as the running environment for the microrobots. As voltage is applied between sets of electrodes, the electrodes and the conductive chassis of the microrobot form a capacitive circuit inducing an electric potential on the microrobot. The potential helps in bending the microrobot body to the substrate, and the scratch drive converts this vertical motion into forward step. Similar to an electrostatic cantilever beam [11], the steering arm of each microrobot has two distinct voltages at which the arm suddenly changes states. These are the snap-down voltage at which the arm is pulled in contact with the substrate as the robot is turning and the release voltage at which the arm is released and the robot is commanded to move straight. These two voltage levels are defined as the transition voltages of the steering arm. The transition voltages can be changed according to the steering-arm designs.

3. SENSING THROUGH COMPLIANCE Compliant interaction, i.e. mutual alignment between rigid objects exerting force on one another, has been shown to be used for orienting parts during docking and grasping [12][13]. Compliance has also been used to align MicroStressBots during docking. Via direct tactile contact, the docking MicroStressBots can sense the orientation of the structure they are aligning with, and though a combination of the acting forces they align the chassis to match that orientation. Today, all mobile microrobotic systems are controlled through a centralized global controller, usually using visual feedback from a

100 µm

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(i)

microscope camera. However, this form of tactile sensing through compliance may increase the ability of mobile microrobots to perform open loop actuation, i.e. closing the control loop at the device level rather than using a global control loop mechanism. A rudimentary idea for such compliant alignment is shown on Fig. 2. As a moving MicroStressBot makes contact with a larger, rigid, structure, the robots motion is constrained on the side that makes contact, and the robot aligns with the structure.

Figure 2: The concept of compliant alignment of a MicroStressBot with a rigid structure. In this case, the rigid structure is assumed to be another (stable) assembly of MicroStressBots.

The specific applications of compliance to self-aligning of microrobot assemblies and open-loop (from a centralized controller perspective) sorting is shown below.

4. SELF-ALIGNING COMPLIANCE Self-aligning compliance is a form of pairwise self-assembly. Two microrobots that dock together to form the initial stable self-align during the application of power delivery waveform. The straight front edge of an Untethered Scratch Drive Actuator (USDA) causes two opposing microrobots to slide relatively to one another until both robots reach as stable configuration. For the purpose of our experiments, self-aligning compliance was considered successful if the maximum allowed lateral misalignment was less than 90um(2/3 bushing width), while the relative rotational misalignment α was less than 60 degrees. Self-aligning compliance requires that the two robots are actuated when they are moving in a straight line(i.e. steering arm lifted). This results in self-alignment of the two robots. An example of a sequence of two microrobots performing self-aligning compliance is shown on Fig. 3. Self–aligning plays a crucial role in reduction of control error. This is evident from the experiments performed for measuring the docking misalignment. Across all experiments the average docking misalignment was 5µm with a standard deviation of 5 µm. In all of them compliance was used to self-align the initial stable structures. Two experiments were conducted with initial shape purposefully misaligned by at least 50 µm to verify the role of self-alignment. In these two experiments, the average misalignment after completed self-alignment was 9 µm (with 8 µm standard deviation). For the remaining three initial shape docking experiments, precise control was applied to minimize the initial misalignment. In these experiments, the average docking misalignment was 6 µm (with 7 µm standard deviation) before the self-alignment and 2 µm (with 3 µm standard deviation) after self-alignment is complete. Figure 4(a-e) shows the configuration of the five goals assembled in part self-aligning compliance. Note that the experimental data reference here was previously published in [5].

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at contact 4 sec. 10 sec. 42 sec.

Figure 3: A self-aligning sequence extracted from experimental data showing two robots performing self-aligning compliance. Note that the final assembled structure rotated approximately 90° from the initial point of contact.

(G1) (G2) (G3) (G4) (G5)

Figure 4. Optical micrographs of five goals assembles using microrobots. Reprinted with permission from [6].

A summary of the successfully assembly using self-aligning compliance is summarized in Tab. 1. Note that the average alignment error was on the order of the accuracy of the fabrication process (disregarding the purposefully large misalignment of 50 μm or more).

Table 1: Docking accuracy Goal Shape Before Compliance After Compliance

G1 6±7µm 2±3 µm

>50 µm 9±8 µm

G2-G5 - 3±3 µm

5. COMPLIANCE-BASED AFFINITY (CBA) Self-aligning compliance can be used to determine (“sense”) if the docking is performed with a matching microrobot. Recall that the assembly method presented in e.g. [14] contrast with Self-Assembly [15] in that the shape is determined by the trajectories of the individual robots and not their affinities. Compliance however allows us to engineer the chassis of the microrobots such that only matching surfaces will result in successful docking. This concept, called compliance-based affinity (CBA) is illustrated on Fig. 5, where two USDAs with matching chassis show aligning to complete a two-robot shape.

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Figure 5. A sequence showing two matching chassis USDAs demonstrating the concept of compliance-based affinity (CBA). The optical micrographs were taken at consecutive time intervals shown in images (i) – (iv). Although in this case, the

The robots can thus lock into stable shapes only if their chassis are matching, extending the previously presented assembly concept. This form of tactile sensing extends the assembly concept to enable open-loop control, as seen from the perspective of the global controller. This rudimentary form of sensing closes a local control loop within the electromechanical structure of the individual microrobots. CBA can be used to link the concept of SA with the concept of Global Control Selective Response (GCSR) [14] which governs the control of multiple microrobots.

6. BEHAVIOR-DIFFRENTIATED SORTING The theory of independent MicroStressBot control is based on differentiating the behavior of the individual microrobots by variations in their design (in particular features of their steering arms). Consequently, during a control primitive, some robots are designed to turn while other are designed to move in straight line. It is possible to develop an open-loop sorting algorithm, which take advantage of this behavior differentiation to separate microrobots into separate groups without any form of closed-loop sensing (apart from local-device level closed loop sensing based on compliance). An illustration of this concept is shown on Fig. 6. Initially, the robots are in their starting (assumed random) configuration (i). Two species of micorobots are present on the field (red and green). We assumed that there exists a region of the electrode field on which the robots locomote that can be independently powered down (marked in blue) to trap approaching microrobots. A control primitive sequence is applied causing the red robots to turn and the green robots to move straight. Assuming the blue region of the field is powered down, after the primitive sequence has been applied for a sufficiently long amount of time allowing the robots to travel from one side of the field to the other, the robots in the center are only of the red species, while the robots at the trapping site are only of the green species (ii). This concept falls into and extends the theory of nonprehensile manipulation [11]. In the previous example we have neglected the effect of collisions. One can imagine that some of the microrobots that move straight will collide with the microrobots that are turning, and remain in the central area of the electrode field. However, the robots that reach the trapping sites at the periphery of the field certainly must be of the green species. However, the concept of using the patterned chassis of the robots can be used to trap, or not to trap (make assemblies unstabvle) selected pairs if species of microrobots. It is possible to design a species of CBA robots that can be “unstuck” using a sequence of control primitives if the chassis of the robots do not match, and conversely it is possible to design the chassis such that if the robots match the species cannot be unstuck (an example of this is shown in Fig. 5). A sorting approach using this concept has been illustrated on Fig. 7. In this case as well, an open-loop control sequence can be designed such that the robots are sorted and some of the devices remain within a certain area of the electrode field, while removing other robots from that area altogether. The robots start at an initially random configuration, and are commanded to turn with the application of a “wiggle” primitive, i.e. a combination of straight and turning primitive that periodically breaks up non-stable assemblies (i). Collisions are common, but because the chassis of the robots are engineered to only generate a stable shape when the red-green robots collide (intermediate configuration – (ii)), only the red-green colliding robot pairs remain in the field after a sufficient long time has passed after the application of this

50 µm (i) (ii) (iii) (iv)

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control primitive. This method is analogous to SA [14], and can be a great link between SA and Global Control Selective Response (GCSR) .

Figure 6. An illustration of the concept of behavior-based sorting on a group of MicroStressBots. The red robots turn in place, while the green robots move in straight lines. The robots are shown in their initial (i) and final (ii) configurations. The blue line indicates areas of the underlying electrode field that are powered down, causing temporary trapping of the microrobots. This simple illustration disregards collisions.

Figure 7. An illustration showing open-loop microrobot sorting using the CBA concept, assuming that the red and the green robots is the only pair with matching chassis. The robots are shown in their initial (i), intermediate (ii) and final (iii) configurations. All robots turn, and some robots collide to form stable shapes. However, only the green-red pair forms a stable configuration (has matching chassis). Consequently, only the red-green robots remain in the center of the electrode field (iii). The blue line indicates areas of the underlying electrode field that are powered down, causing temporary trapping of the microrobots.

It is important to note that the CBA concept assumes that all collisions that do not have matching chassis will not remain stable, and disregards the generation of stable deadlocked assemblies, i.e. assemblies which do not consist of the target pair (in the case of Fig. 7 it was red-green), but still consist of a stable shape. The development of such shapes is challenging, and is currently an active area of research in our laboratory. However, our preliminary results indicate that creation of MicroStressBot chassis that reduces the number of dead-lock assemblies is feasible.

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7. CONCLUSION In this paper, we described how compliance can be used as a form of local tactile sensing to help align the assembling structures composed of MicroStressBots, by locally closing the control loop of docking microrobots. Specifically, the ability to selectively pattern the chassis of the microrobots such that docking is only possible if a set of matching robots are docking. This concept, called compliance-based affinity (CBA) can be used to ensure open-loop sorting of microrobots on substrates that have been fabricated with selective trapping regions. This concept extends the theory of nonprehensile manipulation and connects the theory of Global Control Selective Response (GCSR) with Self-assembly (SA), constituting an important extension of the theory of multi-microrobot control.

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