基于改进跳点搜索和蚁群算法的机器人多目标点巡检规划

Robot Multi-Target Inspection Planning Based on Improved Jump Point Search and Ant Colony Algorithm

针对移动机器人的多目标点巡检规划问题,本文提出了一种融合改进跳点搜索算法(JPS)与蚁群算法(ACO)的路径规划算法.首先,在JPS算法的评估函数中引入角度引导因子,使路径具有更强的导向性;然后,综合考虑路径距离、平滑度、安全性对评估函数的影响,以获得综合性能更优的路径;其次,提出了双向的逆向跳点剔除规则,筛除了多余节点,从而进一步降低路径长度并提高路径平滑度;最后,将多目标优化得到的路径综合性能替代传统旅行商问题(TSP)中的距离因子,并使用自适应蚁群算法来实现多巡检点的路径规划问题.仿真结果表明,改进JPS算法与传统JPS算法相比,具有更好的综合性能;同时应用于多巡检点规划时,具有更强的有效性和实用性.

For the problem of multi-target point inspection planning for mobile robots,this paper proposes a path planning algorithm that integrates the Improved Jump Point Search algorithm(JPS)with the Ant Colony Optimization algorithm(ACO).Firstly,an angle-guided factor is introduced into the evaluation function of the JPS algorithm to provide stronger directional guidance for the path.Then,considering the influences of path distance,smoothness,and safety on the evaluation function,a path with better comprehensive performance is obtained.Next,a bidirectional reverse jump point pruning rule is proposed to eliminate redundant nodes,further reducing path length and improving path smoothness.Finally,the path obtained from multi-objective optimization is used to replace the distance factor in the traditional Traveling Salesman Problem(TSP),and an adaptive ant colony algorithm is used to solve the multi-target point path planning problem.Simulation results show that the improved JPS algorithm has better comprehensive performance compared to the traditional JPS algorithm.When applied to multi-target point planning,it demonstrates stronger effectiveness and practicality.