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In the field of path planning, rapid exploration random tree (RRT) algorithms are important tools for robotic arms to solve path planning problems in complex environments. However, their purely random sampling process resulted in a large number of invalid or inefficient attempts, wasting computational resources. To solve this problem, an improved RRT algorithm based on multiple sampling heuristic strategy (MH-RRT) is proposed. Firstly, the heuristic function strategy is used to evaluate the proxy value of multiple sampling points, the sampling point with the lowest proxy value is selected, and the path tree is guided to grow faster towards the target point; Then, the heuristic function strategy is effectively improved similarly to the RRT* algorithm and the bidirectional RRT* algorithm; Finally, the impact of different parameters on the performance of the improved algorithm is thoroughly explored, and the optimal parameter combination is determined. The experimental results show that the improved algorithm can significantly improve the time efficiency, path length, and number of sampling points in path search, thereby enhancing the effectiveness of path planning.