Walking Motion Generation, Synthesis, and Control for Biped Robot by Using PGRL, LPI, and Fuzzy Logic
- 18 November 2010
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)
- Vol. 41 (3), 736-748
- https://doi.org/10.1109/tsmcb.2010.2089978
Abstract
This paper proposes the implementation of fuzzy motion control based on reinforcement learning (RL) and Lagrange polynomial interpolation (LPI) for gait synthesis of biped robots. First, the procedure of a walking gait is redefined into three states, and the parameters of this designed walking gait are determined. Then, the machine learning approach applied to adjusting the walking parameters is policy gradient RL (PGRL), which can execute real-time performance and directly modify the policy without calculating the dynamic function. Given a parameterized walking motion designed for biped robots, the PGRL algorithm automatically searches the set of possible parameters and finds the fastest possible walking motion. The reward function mainly considered is first the walking speed, which can be estimated from the vision system. However, the experiment illustrates that there are some stability problems in this kind of learning process. To solve these problems, the desired zero moment point trajectory is added to the reward function. The results show that the robot not only has more stable walking but also increases its walking speed after learning. This is more effective and attractive than manual trial-and-error tuning. LPI, moreover, is employed to transform the existing motions to the motion which has a revised angle determined by the fuzzy motion controller. Then, the biped robot can continuously walk in any desired direction through this fuzzy motion control. Finally, the fuzzy-based gait synthesis control is demonstrated by tasks and point- and line-target tracking. The experiments show the feasibility and effectiveness of gait learning with PGRL and the practicability of the proposed fuzzy motion control scheme.Keywords
This publication has 30 references indexed in Scilit:
- BIPED WALKING PATTERN GENERATION USING REINFORCEMENT LEARNINGInternational Journal of Humanoid Robotics, 2009
- Natural ZMP Trajectories for Biped Robot Reference GenerationIEEE Transactions on Industrial Electronics, 2008
- A Type-2 Fuzzy Switching Control System for Biped RobotsIEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 2007
- Robust Fault-Tolerant Control for a Biped Robot Using a Recurrent Cerebellar Model Articulation ControllerIEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2007
- Reinforcement learning method-based stable gait synthesis for biped robotPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Control of a Planar Underactuated Biped on a Complete Walking CycleIEEE Transactions on Automatic Control, 2004
- Barycentric Lagrange InterpolationSIAM Review, 2004
- Policy gradient reinforcement learning for fast quadrupedal locomotionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Hybrid control of biped robots in the double-support phase via H∞ approach and fuzzy neural networksIEE Proceedings - Control Theory and Applications, 2003
- Planning and control of a biped robotInternational Journal of Engineering Science, 1999