摘要
针对石化厂区环境复杂、路径规划困难问题,提出一种基于改进A^(*)算法的石化巡检机器人的路径规划算法。在传统的A^(*)算法上进行了优化并与动态窗口法融合,提高了巡检机器人的路径搜索效率和规划能力。首先,对传统A^(*)算法的启发式函数进行改进,采用动态权重系数对启发函数进行控制,减少全局路径规划节点数量,提高算法效率;其次,优化邻域搜索,减少节点访问量,使路径规划目的性更强;再次,使用B样条曲线对路径进行平滑处理;最后,选取路径规划线路中动态变化点作为动态窗口法的节点进行动态避障。实验结果表明,与传统A^(*)算法相比,改进A^(*)算法在路径长度和计算时长上均有提升;融合动态窗口后,在保证全局最优的前提下,实现动态避障,兼顾安全性和高效性。
Addressing the complexities of the petrochemical plant environment and the difficulties in path planning,an improved A^(*)algorithm-based path planning algorithm for petrochemical inspection robots is proposed.This design optimizes the traditional A^(*)algorithm and integrates it with the Dynamic Window Approach,enhancing the path search efficiency and planning capabilities of the inspection robot.Firstly,the heuristic function of the traditional A^(*)algorithm is improved by adopting dynamic weight coefficients to control the heuristic function,reducing the number of nodes in global path planning,and improving algorithm efficiency.Secondly,the neighborhood search is optimized to reduce the number of node visits and make path planning more purposeful.Thirdly,B-spline curves are used to smooth the path.Finally,dynamically changing points in the path planning route are selected as nodes for the Dynamic Window Approach to perform dynamic obstacle avoidance.Experimental results show that compared with the traditional A^(*)algorithm,the improved A^(*)algorithm has improvements in both path length and calculation time.After integrating the Dynamic Window Approach,it can achieve dynamic obstacle avoidance while ensuring global optimality,balancing safety,and efficiency.
作者
谭亮
孙国玺
TAN Liang;SUN Guoxi(School of Information and Control Engineering,Jilin Institute of Chemical Technology,Jilin 132022,China;Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis,Guangdong University of Petrochemical Technology,Maoming 525000,China)
出处
《广东石油化工学院学报》
2024年第3期68-73,共6页
Journal of Guangdong University of Petrochemical Technology
基金
国家自然科学基金项目(61933013)。
