基于内在动机强化学习算法的煤矿井下运输机器人自主避障

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中图分类号：TD67 文献标志码：A

Abstract： Existing robot obstacle avoidance methods mostly rely on preset rules or external reward signals， making it dificult toadaptto thecomplexand variable underground environment incoal mines.Toachieve autonomous and eficient obstacle avoidance for underground coal mine transport robots，an autonomous obstacle avoidancemethod forunderground coal mine transport robot based on Intrinsic Motivation Reinforcement Learning （IM-RL）algorithm was proposed.The underground coal mine transport robot perceived external environmental information through visual sensors，calculated internal reward values for identifying external environmental atributes using acuriosity-driven intrinsic motivation orientation function，and computed external reward values for its action atributes using anexternal motivation reward function.Bycombining the reward weights of the intrinsic motivation orientation function and the external motivation reward function，it calculated a comprehensive reward value based on the robot's state before and after performing an action，forming the reward mechanism of the reinforcement learning algorithm. The robot's state was trained through a deep belief network， which encouraged the transport robot to actively explore unknown environments.Meanwhile， it used its own memory mechanism to store knowledge and experience， achieving autonomous obstacle avoidance through continuous learning and training.Autonomous obstacleavoidance experiments for the transport robot were conducted in static environments，dynamic environments， and actual underground coal mine environments. The results showed that robots using the IM-RL algorithm achieved the short obstacle avoidance paths and search times， demonstrating strong generalization and robustness.

Key words： intrinsic motivation; reinforcement learning; transport robot; autonomous obstacle avoidance; path planning

0引言

煤矿井下运输机器人通常用于矸石、煤炭、设备和材料的运输。（剩余11488字）