Enhancing AGV Path Planning in Dynamic Environments: Integration and Optimization of DWA and A* Algorithm

In the production of complex products that involve multiple varieties and small batches, the production process is typically discrete, and the assembly workshop is characterized by a dynamic environment with high obstacle mobility. As a result, automated guided vehicles (AGVs) require advanced obstacle avoidance capabilities. This chapter proposes an enhanced DWA combined and A* algorithm, providing theoretical support for AGV path planning in dynamic environments. First, a local DWA algorithm is designed to classify the kind and assess the velocity of dynamic obstacles in the local map, thereby improving the AGV’s ability to detect and avoid dynamic obstacles while minimizing deviations from the optimal path. Secondly, to reduce the time needed to adjust the path of AGV toward the target point, the turning points of the AGV’s global path are optimized, and the DWA evaluation function is refined. Finally, simulation results demonstrate that the enhanced DWA algorithm enables AGV to effectively avoid dynamic obstacles in complex environments, thereby reducing transportation time and improving overall efficiency.