基于改进蚁群算法的路径重规划避障策略研究OA

With the widespread application of mobile robots in industrial automation,logistics transportation,and other dynamic complex environments,path planning technology faces critical challenges such as frequent changes of dynamic obstacles,coexistence of static and dynamic obstacles,and insufficient real-time path planning performance.The collaborative static-dynamic obstacle detection and incremental path replanning strategy can achieve real-time obstacle prediction,dynamic path replanning,and stable robot operation.Specifically,the vector projection method enables rapid static obstacle identification,while a time-window-based approach predicts the trajectories of moving obstacles,allowing real-time updates to the temporary environmental model and local path adjustments.By integrating an improved ant colony algorithm,the strategy achieves efficient obstacle avoidance in hybrid environments while reducing the computational overhead of global replanning.The simulation experiments verify the effectiveness of the obstacle avoidance algorithm and strategy in complex dynamic scenarios.The collaborative detection and replanning strategy can effectively handle sudden obstacles,and complex multi-obstacle scenarios,successfully achieving autonomous obstacle avoidance.In typical situations such as head-on encounters,node conflicts,and same-direction chases,the algorithm can ensure both operational safety and pathfinding efficiency by the multi-stage path re-planning.