The path planning problem of complex wild environment with multiple elements still poses challenges.This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path pla...The path planning problem of complex wild environment with multiple elements still poses challenges.This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path planning.The modeling process of wild environment map is designed.Three optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning modes.Furthermore,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional DWA.For the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple elements.The improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.展开更多
A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS sea...A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.展开更多
为实现移动机器人在复杂动态障碍物环境中的避障,提出一种改进的快速随机扩展树(rapidly-exploring random tree,RRT^(*))与动态窗口法(dynamic window approach,DWA)相融合的动态路径规划方法。基于已知环境信息,利用改进RRT^(*)算法...为实现移动机器人在复杂动态障碍物环境中的避障,提出一种改进的快速随机扩展树(rapidly-exploring random tree,RRT^(*))与动态窗口法(dynamic window approach,DWA)相融合的动态路径规划方法。基于已知环境信息,利用改进RRT^(*)算法生成全局最优安全路径。通过消除RRT^(*)算法产生的危险节点,来确保全局路径的安全性;使用贪婪算法去除路径中的冗余节点,以缩短全局路径的长度。利用DWA算法跟踪改进RRT^(*)算法规划的最优路径。当全局路径上出现静态障碍物时,通过二次调整DWA算法评价函数的权重来避开障碍物并及时回归原路线;当环境中出现移动障碍物时,通过提前检测危险距离并转向加速的方式安全驶离该区域。仿真结果表明:该算法在复杂动态环境中运行时间短、路径成本小,与障碍物始终保持安全距离,确保在安全避开动态障碍物的同时,跟踪最优路径。展开更多
基金Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation(No.USCAST2022-11)。
文摘The path planning problem of complex wild environment with multiple elements still poses challenges.This paper designs an algorithm that integrates global and local planning to apply to the wild environmental path planning.The modeling process of wild environment map is designed.Three optimization strategies are designed to improve the A-Star in overcoming the problems of touching the edge of obstacles,redundant nodes and twisting paths.A new weighted cost function is designed to achieve different planning modes.Furthermore,the improved dynamic window approach(DWA)is designed to avoid local optimality and improve time efficiency compared to traditional DWA.For the necessary path re-planning of wild environment,the improved A-Star is integrated with the improved DWA to solve re-planning problem of unknown and moving obstacles in wild environment with multiple elements.The improved fusion algorithm effectively solves problems and consumes less time,and the simulation results verify the effectiveness of improved algorithms above.
基金National Natural Science Foundation of China(No.62241503)Natural Science Foundation of Shanghai,China(No.22ZR1401400)。
文摘A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.
文摘为实现移动机器人在复杂动态障碍物环境中的避障,提出一种改进的快速随机扩展树(rapidly-exploring random tree,RRT^(*))与动态窗口法(dynamic window approach,DWA)相融合的动态路径规划方法。基于已知环境信息,利用改进RRT^(*)算法生成全局最优安全路径。通过消除RRT^(*)算法产生的危险节点,来确保全局路径的安全性;使用贪婪算法去除路径中的冗余节点,以缩短全局路径的长度。利用DWA算法跟踪改进RRT^(*)算法规划的最优路径。当全局路径上出现静态障碍物时,通过二次调整DWA算法评价函数的权重来避开障碍物并及时回归原路线;当环境中出现移动障碍物时,通过提前检测危险距离并转向加速的方式安全驶离该区域。仿真结果表明:该算法在复杂动态环境中运行时间短、路径成本小,与障碍物始终保持安全距离,确保在安全避开动态障碍物的同时,跟踪最优路径。
