Adaptive Humanoid Robot Arm Motion Generation by Evolved Neural Controllers

In this paper, we present a new method for humanoid robot arm motion planning satisfying multiple constraints. In our method, the humanoid robot arm motion generation is formulated as an optimization problem. Four different constraints, which cover a wide range of humanoid robot tasks, are considered: minimum time, minimum distance, robot hand acceleration and constant joint angular velocity. Results show that arm motions have different characteristics. In order to further verify the performance of humanoid robot arm motions, they are transferred in humanoid robot mobile platform.

[1]  D.T. Pham,et al.  Minimum-time motion planning for a robot arm using the Bees Algorithm , 2009, 2009 7th IEEE International Conference on Industrial Informatics.

[2]  Rieko Osu,et al.  Quantitative examinations for multi joint arm trajectory planning--using a robust calculation algorithm of the minimum commanded torque change trajectory , 2001, Neural Networks.

[3]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[5]  Genci Capi,et al.  Development of a new mobile humanoid robot for assisting elderly people , 2012 .

[6]  M. Kawato,et al.  Formation and control of optimal trajectory in human multijoint arm movement , 1989, Biological Cybernetics.

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Kenji Doya,et al.  Evolution of recurrent neural controllers using an extended parallel genetic algorithm , 2005, Robotics Auton. Syst..

[9]  Tamim Asfour,et al.  Adaptive motion planning for humanoid robots , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  T. Flash,et al.  The coordination of arm movements: an experimentally confirmed mathematical model , 1985, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Y Uno,et al.  Quantitative examinations of internal representations for arm trajectory planning: minimum commanded torque change model. , 1999, Journal of neurophysiology.