基于改进A^(*)算法的变电站自动巡检路径规划研究

Research on automatic path planning of substation inspection robot based on improved A^(*)algorithm

采用自动巡检设备对变电站开展定期巡检是保障电力设备安全运行的有效措施。对于无人值守和偏远的变电站,自动巡检设备基于预设的作业需求,对变电站相关设备按照规划路径定期巡检,保障变电站设备的安全稳定运行。为解决传统自动巡检设备在路径规划时存在的搜索效率低、不支持大区域复杂环境规划等问题,提高自动巡检设备路径规划收敛速度和大范围复杂环境适应能力。文中基于传统A^(*)路径规划算法进行改进,通过引入网格调控因子克服划分网格不可调整的弊端,使规划过程更有效率。通过修改网格大小以平滑所得规划路径,提高算法避障能力与规划效率。同时增加规划循环机制,解决了传统A^(*)算法在此场景下只能进行单目标路径规划的不足,实现了多个目标点路径连续规划。基于改进的A^(*)算法在模拟变电站厂区开展试验,仿真分析了改进后A^(*)算法在变电站自动巡检设备路径规划中的性能。结果表明,新算法不仅缩短了规划路径且减少了折弯数目,并且可以同时规划多个目标。

Using automatic inspection equipment for regular inspections of substations is an effective measure to ensure the safe operation of electrical equipment.For unmanned and remote substations,automatic inspection equipment conducts regular inspections of relevant equipment based on preset operational requirements and planned paths,ensuring the safe and stable operation of the substation equipment.In order to address the low search efficiency and lack of support for large-area complex environment planning in traditional automatic inspection equipment,improving the convergence speed and adaptability of automatic inspection equipment path planning in large-scale complex environments is essential.This paper proposes improvements to the traditional A^(*)path planning algorithm by introducing grid control factors to overcome the drawbacks of non-adjustable grid division,making the planning process more efficient.By modifying the grid size to smooth the resulting planned path,the algorithm's obstacle avoidance capability and planning efficiency are improved.Additionally,a planning loop mechanism is added to address the shortcomings of the traditional A^(*)algorithm,which can only perform single-target path planning in this scenario,achieving continuous planning of multiple target points.The improved A^(*)algorithm is experimentally applied in a simulated substation plant area,and the performance of the improved A^(*)algorithm in substation automatic inspection equipment path planning is analyzed through simulation.The results show that the new algorithm not only shortens and optimizes the planned path,reducing the number of bends,but also allows for simultaneous planning of multiple targets.