Self-adaptive gait planning method, system and device for hexapod robot and medium

Abstract

The invention discloses a self-adaptive gait planning method, system and device for a hexapod robot and a medium. The method comprises the steps that a simulation model of the hexapod robot is built; establishing a gait generator according to the motion characteristics of the hexapod robot; the simulation model and the gait generator are fused, and a reinforcement learning motion control framework is constructed and obtained; training the reinforcement learning motion control framework to obtain parameters of a control network, so that the framework can control a simulation model of the hexapod robot to move in a simulation scene of an unstructured terrain; and integrating the control network obtained after training into a gait control framework of the hexapod robot for controlling the movement of the hexapod robot. The control framework based on reinforcement learning is used for learning the optimal strategy network to control the input of the gait generator, the problems that manual setting is needed and parameters are not easy to change are avoided, and the method can better adapt to the change of topographic conditions. The method can be widely applied to the field of robot control.

Description

technical field [0001] The invention relates to the field of robot control, in particular to a method, system, device and medium for adaptive gait planning of a hexapod robot. Background technique [0002] With the continuous development of social economy and the continuous advancement of science and technology, intelligent robots are used in more and more complex scenarios, such as warehousing and logistics, processing and manufacturing, energy and mineral collection, medical rehabilitation, national defense and military, etc. Nowadays, the scope of human activities continues to expand, and various living and production environments are becoming more and more complex. Therefore, humans have begun to use intelligent robots to complete exploration and detection in various complex environments to improve safety and work efficiency. However, due to the continuous development of technology, the existing robot technology needs to be further improved to meet the existing work need...

AI Technical Summary

Problems solved by technology

However, the control of legged robots is more difficult. In complex terrain, it needs to rely on a variety of sensor information to obtain a suitable control strategy; at the same time, the corresponding gait parameters are manually designed, which has a good effect on known environments, but in In the face of unknown complex terrain, its efficiency is greatly reduced, and there is a lack of a more adaptable strategy to solve the problem of adaptive motion under complex terrain

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