复杂动态场景下融合改进A*-DWA移动机器人路径规划OA

The dynamic and complex environment of plant factories,characterized by frequent scene changes,narrow operational spaces,and irregular obstacles,poses significant challenges to the path planning of mobile robots.To enhance both the efficiency and safety of navigation in such environments,a hybrid path planning method that integrated an improved A*algorithm with the dynamic window approach(DWA)was proposed.Firstly,an enhanced A*algorithm was developed for grid-based maps by incorporating a neighborhood node selection strategy,an optimized evaluation function,and a path quality refinement mechanism.In particular,a remaining-distance factor and a safety-distance constraint were embedded into the cost function to improve the global path generation,aiming to enhance safety and reduce the number of turning points.Secondly,to address the issue of dynamic obstacle avoidance,the conventional DWA was improved by redesigning the velocity evaluation function.A global guidance term was introduced,along with an adaptive weight adjustment strategy,to better balance local dynamic avoidance with global path optimality.Finally,a series of simulation-based comparative experiments and real-world validation trials were conducted.Experimental results demonstrated that the proposed method exhibited superior performance in terms of both effectiveness,path smoothness and safety,offering a promising and robust solution for mobile robot navigation in the constrained and dynamic environments typical of intelligent plant factories.