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针对煤矿井下巡检机器人，系统综述了其在运动学建模与路径规划方面的研究进展。首先，剖析狭窄巷道、低照度、密集障碍物及瓦斯粉尘等复杂、恶劣且非结构化特殊环境对机器人移动底盘选型及运动控制带来的通过性不足、稳定性差与控制精度难以保障等多重挑战。进而系统梳理了适用于轮式、履带和轮履复合式等不同底盘结构的运动学建模方法。在路径规划方面，全面评述图搜索算法（如A*、Dijkstra）、随机采样算法（如RRT系列）及智能仿生算法（如蚁群、粒子群算法）等全局规划方法，并分析其与DWA、APF等局部避障算法相结合的混合策略在井下的适用性。最后，指出动态未知环境下的实时规划、多机协同巡检等关键技术难题，并对未来研究方向进行展望。
Abstract：A systematic review was conducted on the research progress of kinematic modeling and path planning for underground inspection robots in coal mine. Firstly, analyzed the complex, harsh, and unstructured special environments such as narrow roadways, low lighting, dense obstacles, and gas dust,which pose multiple challenges to the selection and motion control of robot mobile chassis, including insufficient passability, poor stability, and difficulty in ensuring control accuracy. Furthermore, the kinematic modeling methods applicable to different chassis structures such as wheeled, tracked, and wheel track composite were systematically reviewed. In terms of path planning, comprehensively review global planning methods such as graph search algorithms(such as A*, Dijkstra), random sampling algorithms(such as RRT series), and intelligent biomimetic algorithms(such as ant colony and particle swarm algorithms), and analyzed the applicability of their hybrid strategies combined with local obstacle avoidance algorithms such as DWA and APF in underground. Finally, key technical challenges such as real-time planning and multi machine collaborative inspection in dynamic unknown environments were pointed out, and future research directions were discussed.
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基本信息:
DOI：10.13436/j.mkjx.202601001
中图分类号:TP242;TD67
引用信息:
[1]赵琴,冉进财,刘新源,等.煤矿井下巡检机器人运动学建模及路径规划算法综述[J].煤矿机械,2026,47(01):1-8.DOI:10.13436/j.mkjx.202601001.
基金信息:
新疆工程学院博士科研启动基金项目（2024XGYBQJ05）; 新疆维吾尔自治区重点研发计划项目（2024B01007,2025B04042）; 新疆维吾尔自治区“天池英才”引进计划项目（2024XGYTCYC02）