In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is intr...In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is introduced to reduce the randomness of the RRT^(*)algorithm,and then the initial path planning is carried out in a static environment.Secondly,apply the path in a dynamic environment,and use the initially planned path as the path cache.When a new obstacle appears in the path,the invalid path is clipped and the path is replanned.At this time,there is a certain probability to select the point in the path cache as the new node,so that the new path maintains the trend of the original path to a greater extent.Finally,MATLAB is used to carry out simulation experiments for the initial planning and replanning algorithms,respectively.More specifically,compared with the original RRT^(*)algorithm,the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19%on average.展开更多
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr...Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.展开更多
基金National Natural Science Foundation of China(No.61903291)。
文摘In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is introduced to reduce the randomness of the RRT^(*)algorithm,and then the initial path planning is carried out in a static environment.Secondly,apply the path in a dynamic environment,and use the initially planned path as the path cache.When a new obstacle appears in the path,the invalid path is clipped and the path is replanned.At this time,there is a certain probability to select the point in the path cache as the new node,so that the new path maintains the trend of the original path to a greater extent.Finally,MATLAB is used to carry out simulation experiments for the initial planning and replanning algorithms,respectively.More specifically,compared with the original RRT^(*)algorithm,the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19%on average.
基金National Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548,BK20250876)+2 种基金Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87)Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101)。
文摘Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT^(*)),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT^(*)-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT^(*),the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT^(*).Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications.
