Abstract
To improve the fast and stable walking ability of a humanoid robot, this paper proposes a gait optimization method based on a parallel comprehensive learning particle swarm optimizer (PCLPSO). Firstly, the key parameters affecting the walking gait of the humanoid robot are selected based on the natural zero-moment point trajectory planning method. Secondly, by changing the slave group structure of the PCLPSO algorithm, the gait training task is decomposed, and a parallel distributed multi-robot gait training environment based on RoboCup3D is built to automatically optimize the speed and stability of bipedal robot walking. Finally, a layered learning approach is used to optimize the turning ability of the humanoid robot. The experimental results show that the PCLPSO algorithm achieves a quickly optimal solution, and the humanoid robot optimized possesses a fast and steady gait and flexible steering ability.
Highlights
Gait planning is a research hotspot for humanoid robots, and it provides some technical support for humanoid robots walking like humans
By means of a gait generation method based on natural zero-moment point (ZMP) trajectory planning, humanoid robot walking is summarized in the following algorithmic steps
Aiming at the problem that a lot of manual debugging time is required when planning the robot trajectory by directly using a simplified model, this paper proposes a machine learning method based on the parallel comprehensive learning particle swarm optimizer (PCLPSO) algorithm to optimize gait parameters
Summary
Gait planning is a research hotspot for humanoid robots, and it provides some technical support for humanoid robots walking like humans. In the double-leg support phase, according to the linear pendulum model, as shown, the equation of the relationship between the position of robot center of mass and acceleration is given as follows: xd. By means of a gait generation method based on natural ZMP trajectory planning, humanoid robot walking is summarized in the following algorithmic steps. Aiming at the problem that a lot of manual debugging time is required when planning the robot trajectory by directly using a simplified model, this paper proposes a machine learning method based on the PCLPSO algorithm to optimize gait parameters. Update the Local Pool and find the lBest in the LP 19. until the stopping criterion is met 20. return
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