Model adaptive gait scheme based on evolutionary algorithm

To let a robot learns to create behaviors without recourse to a certain model, or an action which is designed manually is always a problem. This paper proposes a novel method of autonomous locomotion scheme for legged robot. This method using biological evolutionary algorithm makes robots more robust for their walking. The evolutionary algorithm and a simulation environment is applied to find some actions, with which the robot could walk on. Moreover, the robot knows nothing about how to walk at the beginning and its model can change. Simulation and physical experiments are conducted based on the multi-legs robot platform. Finally the robot learnt to walk and the experimental result validates the algorithm.

[1]  Zbigniew Michalewicz,et al.  Evolutionary algorithms , 1997, Emerging Evolutionary Algorithms for Antennas and Wireless Communications.

[2]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[3]  Marco Tomassini,et al.  Evolutionary Algorithms , 1995, Towards Evolvable Hardware.

[4]  Gregory S. Hornby,et al.  Autonomous evolution of gaits with the Sony Quadruped Robot , 1999 .

[5]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[6]  Hod Lipson,et al.  Resilient Machines Through Continuous Self-Modeling , 2006, Science.

[7]  J. Bongard,et al.  Co‐evolutionary algorithm for structural damage identification using minimal physical testing , 2007 .

[8]  Hod Lipson,et al.  Automated Synthesis of Body Schema using Multiple Sensor Modalities , 2006 .

[9]  Jianhong Liang,et al.  Self identification and control of four-leg robot based on biological evolutionary mechanisms , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[10]  Jindong Tan,et al.  Self-assembly control and experiments in swarm modular robots , 2012 .

[11]  Gregory S. Chirikjian,et al.  Modular Self-Reconfigurable Robot Systems , 2007 .

[12]  Eiichi Yoshida,et al.  Distributed adaptive locomotion by a modular robotic system, M-TRAN II , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[13]  Masahiro Fujita,et al.  Autonomous evolution of dynamic gaits with two quadruped robots , 2005, IEEE Transactions on Robotics.

[14]  Gregory J. Barlow,et al.  Article in Press Robotics and Autonomous Systems ( ) – Robotics and Autonomous Systems Fitness Functions in Evolutionary Robotics: a Survey and Analysis , 2022 .

[15]  H. Kurokawa,et al.  Automatic locomotion design and experiments for a Modular robotic system , 2005, IEEE/ASME Transactions on Mechatronics.

[16]  E. Alba,et al.  Evolutionary algorithms in telecommunications , 2006, MELECON 2006 - 2006 IEEE Mediterranean Electrotechnical Conference.

[17]  Tom V. Mathew.  Genetic Algorithm , 2022 .

[18]  Gregory S. Chirikjian,et al.  Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics] , 2007, IEEE Robotics & Automation Magazine.

[19]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.