Emma Wilson; Sean R. Anderson; Tareq Assaf; Martin J. Pearson; Peter Walters; Tony J. Prescott; Chris Melhuish; Jonathan Rossiter; Tony Pipe ...
Bioinspired Control of Electro-Active Polymers for Next Generation Soft Robots Emma Wilson1 , Sean R. Anderson1 , Tareq Assaf2 , Martin J. Pearson2, Peter Walters2 , Tony J. Prescott1 , Chris Melhuish2 , Jonathan Rossiter2 , Tony Pipe2 , Paul Dean1 , and John Porrill1 1
Sheffield Centre for Robotics (SCENTRO), University of Sheffield, UK 2 Bristol Robotics Laboratory (BRL), Bristol University and University of the West of England, UK
The emerging field of soft robotics offers the prospect of replacing existing hard actuator technologies with new soft-smart materials [7]. Such materials have the potential to form a key component of safer, more compliant and light-weight robots. Soft robots constructed from these advanced materials could be used in a progressively wide range of applications, especially those involving interactions between robots and people in unstructured environments such as homes, hospitals and schools. Electroactive polymer (EAP) technologies such as dielectric elastomer (DEA) actuators and ionic polymer-metal composites (IPMCs) are a class of smart materials that are of particular interest for use in soft robotics [2]. However, despite their great potential, EAP devices present a number of challenges for control. They are, for example, non-linear in behaviour, prone to degradation over time, and fabricated with wide tolerances. In this paper we describe a project that aims to develop novel bioinspired control strategies for EAPs addressing these key challenges. The overarching aim of the project is to develop a robotic platform with both active touch and vision systems actuated by EAPs. The control system will use an adaptive bioinspired algorithm based on the ‘cerebellar chip’, which has a structure similar to that of an adaptive filter [4]. The platform will adapt to wide fabrication tolerances, and degradation and traumatic changes in muscles and sensors, while retaining its ability to perform accurate behaviours. In bringing together distinct advances in active touch robotics [1, 6], cerebellar adaptive control [5] and EAP actuators [3] we aim to implement a step-change in design and control of soft robotic systems. The specific demonstrator tasks will involve control of two compliant EAP driven systems: an array of whiskers for active touch and an artificial eye for active vision. These sensory systems will be fused in adaptive collicular sensory/motor maps in order to localise and orient to an object in the environment (Fig. 1). In this contribution, we outline the project and summarise initial results on cerebellar control of a data-driven model of an EAP cone actuator, described in [3]. We identify a low-order linear dynamic model of the EAP actuator from a narrow operating range of data (voltage to displacement). In simulation we then demonstrate that the linear adaptive filter model of cerebellum can effectively compensate EAP dynamics in an inverse model control scheme. Further to this basic control scheme, we show that learning rate can be improved by the G. Herrmann et al. (Eds.): FIRA-TAROS 2012, LNAI 7429, pp. 424–425, 2012. c Springer-Verlag Berlin Heidelberg 2012
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Fig. 1. Soft-robot control scheme. In the proposed robot platform there are four basic robot tasks: (i) sensory cancellation, (ii) motor cancellation, (iii) map calibration, (iv) task calibration. Each task is assigned its own cerebellar control module. These modules receive copies of the available sensory and motor information (green arrows) and their outputs project back to robot sensors and motors as shown.
use of orthonormal signal transforms and we connect this well-known principle in adaptive filtering to the function of the granular layer in cerebellar cortex. These initial results illustrate the potential of bioinspired control of EAPs for soft robotics.
References 1. Anderson, S., Pearson, M., Pipe, A., Prescott, T., Dean, P., Porrill, J.: Adaptive cancelation of self-generated sensory signals in a whisking robot. IEEE Trans. Robotics 26(6), 1065–1076 (2010) 2. Bar-Cohen, Y.: Electroactive polymer (EAP) actuators as artificial muscles: reality, potential, and challenges. Society of Photo Optical (2004) 3. Conn, A., Rossiter, J.: Towards holonomic electro-elastomer actuators with six degrees of freedom. Smart Materials and Structures 21, 035012 (2012) 4. Dean, P., Porrill, J., Ekerot, C., Jorntell, H.: The cerebellar microcircuit as an adaptive filter: experimental and computational evidence. Nature Reviews Neuroscience 11(1), 30–43 (2010) 5. Lenz, A., Anderson, S., Pipe, A., Melhuish, C., Dean, P., Porrill, J.: Cerebellarinspired adaptive control of a robot eye actuated by pneumatic artificial muscles. IEEE Trans on Systems, Man, and Cybernetics, Part B: Cybernetics 39(6), 1420– 1433 (2009) 6. Pearson, M., Pipe, A., Melhuish, C., Mitchinson, B., Prescott, T.: Whiskerbot: a robotic active touch system modeled on the rat whisker sensory system. Adaptive Behavior 15(3), 223–240 (2007) 7. Trivedi, D., Rahn, C., Kier, W., Walker, I.: Soft robotics: Biological inspiration, state of the art, and future research. Applied Bionics and Biomechanics 5(3), 99– 117 (2008)
