This study aims to tackle the challenges encountered by autonomous mobile robots in point-to-point path planning, encompassing issues such as low search efficiency, susceptibility to local optima, and inadequate real-time handling of unknown dynamic and static obstacles. To this end, we have carried out an effective integration of the enhanced A* algorithm with the improved DWA. Within the enhanced A* algorithm, we have introduced obstacle-rate-based weighting factors and a bidirectional optimization strategy, aiming to bolster search efficiency and facilitate the generation of smoother paths. Furthermore, the refined DWA algorithm integrates two novel obstacle evaluation functions and adeptly addresses the local optima issue through the adjustment of weight coefficients. By unifying the enhanced DWA algorithm with the improved A* algorithm, we have enabled proficient real-time obstacle avoidance for unknown dynamic and static obstacles. Simulation results indicate that the improved A* algorithm proposed in this paper, compared with the traditional A* algorithm and the enhanced algorithm from reference ［23］, demonstrates significant performance improvements in four different environments. Specifically, the number of path turns decreased by an average of 30.14% and 18.16%, the search space was reduced by 35.09% and 15.21%, and the planning time was shortened by 82.36% and 38.26%, respectively. Furthermore, when integrated with the improved DWA algorithm, the time required for path planning, the length of the planned path, and the average motion speed were optimized compared to combining with the traditional DWA algorithm and the fusion algorithm from reference ［23］, showing an average reduction of 37.46% and 9.82% in planning time, a decrease of 4.59% and 3.63% in path length, and an increase of 53.49% and 7.09% in average motion speed.