With the rapid development of intelligent transportation systems,autonomous navigation path planning technology has become increasingly critical.As a novel technological approach,the design and implementation of an autonomous navigation system for unmanned delivery vehicles are key to achieving efficient and cost-effective logistics delivery.However,the traditional Artificial Poten-tial Field(APF)method encounters issues with unreachable goals and a tendency to fall into local minima during path planning,which limits its application in unmanned delivery vehicle navigation systems.To address these issues,this paper proposes an improved artifi-cial potential field method that enhances the repulsive field model by introducing the concept of randomized algorithms,thereby enhan-cing the obstacle avoidance capabilities and path search efficiency of unmanned delivery vehicles in complex environments.The im-proved algorithm not only enhances the global optimization performance of path planning but also strengthens the robustness of the algo-rithm,enabling unmanned delivery vehicles to quickly find safe and efficient delivery paths in response to dynamically changing envi-ronments.Simulation experiments have confirmed the effectiveness of the proposed method in reducing node expansion,shortening planning time,and improving path smoothness.
维普
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