摘要
针对复杂任务下多机器人鲁棒性问题,提出一种耦合式任务分配和路径规划方法,以满足复杂任务下的轨迹要求。首先,通过Dijkstra算法对环境中各任务栅格间的最短路径和距离进行预处理;其次,中央控制器负责任务下达,监测到机器人进入危险区域故障后,重新分配剩余任务,以确保任务的完成。此外,结合邻域搜索,提出一种带有局部调整策略的改进北方苍鹰优化算法(INGO)以提高求解质量,并采用结合边际成本的竞拍方法来应对机器人故障后的重分配问题。最后,分别在不同地图规模大小和任务数量下进行随机测试,其中在100个栅格的环境中,所提方法比拍卖方法得到的总移动距离节省19.63%,且求解时间也小于其他方法。结果表明,所提方法能更好地均衡移动距离和求解时间,在可扩展性和系统鲁棒性方面也均优于现有方法。
To address the robustness problem of multiple robots in complex tasks,this paper proposed a coupled task allocation and path planning method to address the trajectory requirements under such tasks.Firstly,the Dijkstra algorithm preprocessed the shortest paths and distances between task grids in environments.Secondly,when the central controller monitored the robot entering the danger zone and failure,it was responsible for real-time task assignment and environment updates to ensure task completion.In addition,it proposed an improved northern goshawk optimization algorithm(INGO)with local adjustment strategies in conjunction with a neighbourhood search to improve the solution quality,and an auction method incorporating marginal cost handled the robot failure reassignment problem.Finally,it conducted random tests under different map sizes and numbers of tasks.In 100-grid environment,the proposed method saved 19.63%of the total traveled distance compared to the auction method,and the solution time was shorter than that of the other methods.The results demonstrate that the method better balances the travelling distance and solution time,while also outperforming existing methods in terms of scalability and system robustness.
作者
何舟
何鹏阳
He Zhou;He Pengyang(College of Electrical&Control Engineering,Shaanxi University of Science&Technology,Xi’an 710021,China;College of Mechanical&Electrical Engineering,Shaanxi University of Science&Technology,Xi’an 710021,China)
出处
《计算机应用研究》
北大核心
2025年第6期1684-1690,共7页
Application Research of Computers
基金
国家自然科学基金资助项目(62373234)
陕西省自然科学基金面上项目(2023-JC-YB-564)。
