Research on Autonomous Obstacle Crossing Control of Electric Power Inspection Robot Based on Meta-reinforcement Learning
The conventional autonomous obstacle control method of electric inspection robot is mainly stat-ic obstacle control,which cannot independently identify the obstacles in front,and the problem of obstacle control error occurs.Therefore,the automatic obstacle crossing control method of electric power inspection robot based on meta-reinforcement learning is designed.The dynamic characteristics of the robot autonomous obstacle crossing are extracted,the adsorption force,support force and friction force received in the process of the power inspection robot are taken into account,and the static balance conditions of the obstacle cross-ing control are analyzed,so as to avoid the problem of obstacle crossing and overturning.Based on meta-re-inforcement learning,the inspection robot autonomous barrier-crossing control model is constructed to automat-ically learn the super parameters of the meta-reinforcement learning algorithm to optimize the network struc-ture of autonomous barrier-crossing control and realize the accurate control of the robot.Planning the trajec-tory of the autonomous obstacle control of the electric inspection robot.On the basis of the inspection robot meeting the gravity balance conditions,the autonomous obstacle control trajectory of the electric inspection robot is planned to control the obstacle control of the robot through the change state of the robot joint.By using comparative experiment,the method has better obstacle control performance and can be applied in real life.
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