Central Pattern Generators Control of Momentum Driven Compliant Structures

We introduce the concept of Momentum Driven Structures (MDS) made of inertially actuated units linked together by compliant elements as a potential solution for rough environments exploration. We propose a control method for MDS based on the bio-inspired concept of Central Pattern Generator (CPG) and study in simulation the impact of compliance distribution on locomotion performance using population based optimization techniques. Our results suggest that compliant structures outperform their rigid counterparts in terms of distance traveled. In addition, we show that co-evolved structures perform only marginally better than their control-only optimized equivalent, highlighting the fact that compliance modulation may not be a significant asset in such experiments, considering the related hardware complexity it introduces.

[1]  Benjamin Schrauwen,et al.  Design and control of compliant tensegrity robots through simulation and hardware validation , 2014, Journal of The Royal Society Interface.

[2]  Alice M. Agogino,et al.  System design and locomotion of SUPERball, an untethered tensegrity robot , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Hammad Mazhar,et al.  Chrono: An Open Source Multi-physics Dynamics Engine , 2015, HPCSE.

[4]  Ian F. C. Smith,et al.  Active Tensegrity Structure , 2004 .

[5]  Auke Jan Ijspeert,et al.  Central pattern generators for locomotion control in animals and robots: A review , 2008, Neural Networks.

[6]  Daniela Rus,et al.  3D M-Blocks: Self-reconfiguring robots capable of locomotion via pivoting in three dimensions , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[7]  Auke Jan Ijspeert,et al.  Automatic generation of reduced CPG control networks for locomotion of arbitrary modular robot structures , 2014, Robotics: Science and Systems.

[8]  Chandana Paul,et al.  Design and control of tensegrity robots for locomotion , 2006, IEEE Transactions on Robotics.

[9]  Jeffrey A. Hoffman,et al.  Internally-actuated rovers for all-access surface mobility: Theory and experimentation , 2013, 2013 IEEE International Conference on Robotics and Automation.

[10]  Ivan Jordanov,et al.  Intelligent approaches in locomotion , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[11]  Marco Pavone,et al.  Design, Control, and Experimentation of Internally‐Actuated Rovers for the Exploration of Low‐gravity Planetary Bodies , 2016, FSR.

[12]  S. Strogatz,et al.  Synchronization of pulse-coupled biological oscillators , 1990 .

[13]  A. Tibert,et al.  Review of Form-Finding Methods for Tensegrity Structures , 2003 .

[14]  Adrian K. Agogino,et al.  Controlling Tensegrity Robots through Evolution using Friction based Actuation , 2017 .

[15]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[16]  Raffaello D'Andrea,et al.  The Cubli: A cube that can jump up and balance , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Atil Iscen,et al.  Controlling tensegrity robots through evolution , 2013, GECCO '13.

[18]  Hilary Bart-Smith,et al.  Experimental Validation of Robust Resonance Entrainment for CPG-Controlled Tensegrity Structures , 2013, IEEE Transactions on Control Systems Technology.

[19]  Klaus Zimmermann,et al.  Vibration-driven mobile robots based on single actuated tensegrity structures , 2013, 2013 IEEE International Conference on Robotics and Automation.

[20]  Shinichi Hirai,et al.  Crawling by body deformation of tensegrity structure robots , 2009, 2009 IEEE International Conference on Robotics and Automation.

[21]  Koji Yamanaka,et al.  High-Agility, Miniaturized Attitude Control Sensors and Actuators in an All-in-one Module , 2016 .