To address the problems of slow convergence speed, numerous redundant points in paths, unsuitability for dynamic environments, and the lack of an effective coordination mechanism to integrate global and local planning results leading to a significant increase in path length in mobile robot path planning, a path planning fusion algorithm based on the Adaptive Improved Potential Function Rapidly-exploring Random Tree* (AIP-RRT*) and the Dynamic Gravity Field Artificial Potential Field method (DGF-APF) was proposed. a path planning fusion algorithm based on the adaptive improved potential function rapidly-exploring random tree* (AIP-RRT*) and the dynamic gravity field artificial potential field method (DGF-APF) is proposed. Firstly, an adaptive goal bias probability strategy is constructed, generating new nodes through a heuristic function to improve the search efficiency of the path planning algorithm. Secondly, an adaptive step size function is developed to enhance path exploration capabilities and accelerate the convergence speed of the path planning algorithm. Thirdly, a pruning optimization strategy based on goal backtracking is employed to remove redundant points in the global path, thereby improving path quality. Finally, a fusion algorithm of AIP-RRT* and DGF-APF path planning for dynamic scenarios is proposed to realize the path planning of AIP-RRT* and DGF-APF fusion algorithms by using the global key nodes as local subgoal points for local path planning in dynamic environments, and a synergy mechanism based on the dynamic gravitational field strategy is constructed to synthesize the global and local path planning results to shorten the path length. The results of the combined simulation and real experiments show that the path planning fusion algorithm has better global path planning capability as well as local path planning capability, which enables the robot to better adapt to static as well as dynamic environments. In the real environment, the improved fusion algorithm reduces the path length by 6.34% and the running time by 10.71% compared with the traditional algorithm.