An adaptive locomotion controller for a hexapod robot: CPG, kinematics and force feedback

Insects can perform versatile locomotion behaviors such as multiple gaits, adapting to different terrains, fast escaping, etc. However, most of the existing bio-inspired legged robots do not possess such walking ability, especially when they walk on irregular terrains. To tackle this challenge, a central pattern generator (CPG)-based locomotion control methodology is proposed, integrated with a contact force feedback function. In this approach, multiple gaits are produced by the CPG module. After passing through a post-processing circuit and a delay-line, the control signal is fed into six trajectory generators to generate predefined feet trajectories for the six legs. Then, force feedback is employed to adjust these trajectories so as to adapt the robot to rough terrains. Finally the regulated trajectories are sent to inverse kinematics modules such that the position control instructions are generated to control the actuators. In both simulations and real robot experiments, we consistently show that the robot can perform sophisticated walking patterns. What is more, the robot can use the force feedback mechanism to deal with the irregularity in rough terrain. With this mechanism, the stability and adaptability of the robot are enhanced. In conclusion, the CPG-base control is an effective approach for legged robots and the force feedback approach is able to improve walking ability of the robots, especially when they walk on irregular terrains.

[1]  Xiaodong Wu,et al.  Adaptive creeping locomotion of a CPG-controlled snake-like robot to environment change , 2010, Auton. Robots.

[2]  Ludovic Righetti,et al.  Engineering entrainment and adaptation in limit cycle systems , 2006, Biological Cybernetics.

[3]  Dragomir N. Nenchev Special Issue on Cutting Edge of Robotics in Japan 2013 , 2013, Adv. Robotics.

[4]  Hiroshi Shimizu,et al.  Self-organized control of bipedal locomotion by neural oscillators in unpredictable environment , 1991, Biological Cybernetics.

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

[6]  Sangyoon Lee,et al.  A fast mesoscale quadruped robot using piezocomposite actuators , 2012, Robotica.

[7]  Luc A P At An Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots , 2009, Cognitive Systems Monographs.

[8]  Cynthia Ferrell,et al.  A comparison of three insect-inspired locomotion controllers , 1995, Robotics Auton. Syst..

[9]  Paolo Arena,et al.  Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots II, An Insect Brain Computational Model , 2014, Cognitive Systems Monographs.

[10]  Carlo Menon,et al.  Abigaille II: toward the development of a spider-inspired climbing robot , 2011, Robotica.

[11]  H. Cruse,et al.  Stick insect locomotion in a complex environment: climbing over large gaps , 2004, Journal of Experimental Biology.

[12]  Daniel A. Kingsley,et al.  Improved mobility through abstracted biological principles , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Stefan Schaal,et al.  Fast, robust quadruped locomotion over challenging terrain , 2010, 2010 IEEE International Conference on Robotics and Automation.

[14]  James K. Mills,et al.  Vibration control of elastodynamic response of a 3-PRR flexible parallel manipulator using PZT transducers , 2008, Robotica.

[15]  Yasuhiro Fukuoka,et al.  Adaptive Dynamic Walking of a Quadruped Robot on Natural Ground Based on Biological Concepts , 2007, Int. J. Robotics Res..

[16]  Tatsuo Narikiyo,et al.  Continuous and dynamically equilibrated one-legged running experiments: Motion generation and indirect force feedback control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Teresa Zielinska,et al.  Quadruped Free Gait Generation Based on the Primary/Secondary Gait , 1999, Robotica.

[18]  Darwin G. Caldwell,et al.  On the role of load motion compensation in high-performance force control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Junzhi Yu,et al.  CPG parameter search for a biomimetic robotic fish based on particle swarm optimization , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[20]  Christopher G. Atkeson,et al.  An optimization approach to rough terrain locomotion , 2010, 2010 IEEE International Conference on Robotics and Automation.

[21]  Volker Dürr,et al.  A Bottom-Up Approach for Cognitive Control , 2009, Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots.

[22]  Daniel A. Kingsley,et al.  A Cockroach Inspired Robot With Artificial Muscles , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  Zheng Haojun,et al.  Autonomously clearing obstacles using the biological flexor reflex in a quadrupedal robot , 2008 .

[24]  Marc Timme,et al.  Self-organized adaptation of a simple neural circuit enables complex robot behaviour , 2011, ArXiv.

[25]  Luigi Fortuna,et al.  An adaptive, self-organizing dynamical system for hierarchical control of bio-inspired locomotion , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Y. Go,et al.  Navigability of multi-legged robots , 2006, IEEE/ASME Transactions on Mechatronics.

[27]  Volker Dürr,et al.  Principles of Insect Locomotion , 2009, Spatial Temporal Patterns for Action-Oriented Perception in Roving Robots.

[28]  Junzhi Yu,et al.  Towards development of a slider-crank centered self-propelled dolphin robot , 2013, Adv. Robotics.

[29]  Florentin Wörgötter,et al.  Sensor-driven neural control for omnidirectional locomotion and versatile reactive behaviors of walking machines , 2008, Robotics Auton. Syst..

[30]  Auke Jan Ijspeert,et al.  Online Optimization of Swimming and Crawling in an Amphibious Snake Robot , 2008, IEEE Transactions on Robotics.

[31]  Stefano Stramigioli,et al.  Variable Stiffness Actuators: A Port-Based Power-Flow Analysis , 2012, IEEE Transactions on Robotics.

[32]  José António Tenreiro Machado,et al.  Kinematic and dynamic performance analysis of artificial legged systems , 2008, Robotica.

[33]  Jianhua Wang,et al.  Smooth transition between different gaits of a hexapod robot via a central pattern generators algorithm , 2012, J. Intell. Robotic Syst..

[34]  Tamio Arai,et al.  Wave CPG model for autonomous decentralized multi-legged robot: Gait generation and walking speed control , 2006, Robotics Auton. Syst..

[35]  A. Ijspeert,et al.  From Swimming to Walking with a Salamander Robot Driven by a Spinal Cord Model , 2007, Science.

[36]  Holk Cruse,et al.  Hexapod Walking: an expansion to Walknet dealing with leg amputations and force oscillations , 2007, Biological Cybernetics.

[37]  D. Wilson Insect walking. , 1966, Annual review of entomology.

[38]  Pinhas Ben-Tzvi,et al.  Mapping, localization and motion planning in mobile multi-robotic systems , 2012, Robotica.

[39]  R J Full,et al.  How animals move: an integrative view. , 2000, Science.

[40]  Shinya Aoi,et al.  Functional Roles of Phase Resetting in the Gait Transition of a Biped Robot From Quadrupedal to Bipedal Locomotion , 2012, IEEE Transactions on Robotics.

[41]  Shaoping Bai,et al.  Path generation of walking machines in 3D terrain , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[42]  Xiuli Zhang,et al.  Walking up and down hill with a biologically-inspired postural reflex in a quadrupedal robot , 2008, Auton. Robots.

[43]  Florentin Wörgötter,et al.  Multiple chaotic central pattern generators for locomotion generation and leg damage compensation in a hexapod robot , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[44]  Fred Delcomyn,et al.  Walking Robots and the Central and Peripheral Control of Locomotion in Insects , 1999, Auton. Robots.