In mobile robot path planning, the conventional A* algorithm often suffers from redundant node expansion and excessive turning points, whereas the Dynamic Window Approach (DWA) is prone to local optima and deviations from the global path in dynamic environments. To address these issues, this paper proposes a hybrid algorithm, termed A*-GA-DWA, which combines an improved A* algorithm with a GA-optimized DWA method. In the global planning stage, a directional six-neighborhood search strategy, an obstacle-aware adaptive heuristic function, and a turning-point smoothing method are introduced to improve path quality and reduce redundant node expansion. In the local planning stage, genetic algorithm optimization is applied to the DWA evaluation weights to enhance obstacle avoidance adaptability in dynamic environments. In addition, key nodes extracted from the global path are used as sub-goals to strengthen the coordination between global guidance and local replanning. Simulation results on a 30 × 30 map with dynamic obstacles show that, compared with conventional A*-DWA, the proposed method reduces the path length by 14.07% and the navigation execution time by 45.98%; compared with M-A*-DWA, the path length and navigation execution time are further reduced by 0.32% and 21.23%, respectively. Additional experiments on a ROS-based mobile robot platform were conducted to further validate the deployability and obstacle-avoidance capability of the proposed framework. These results provide an effective solution for mobile robot path planning tasks.
Zhang et al. (Tue,) studied this question.