This paper introduces a multi-indicator heuristic evaluation-based rapidly exploring random tree (MIHE-RRT) algorithm to address the key challenges of robot path planning in complex environments. The core innovation lies in a novel dual optimization framework that combines Hammersley sequence sampling with a comprehensive multi-indicator heuristic evaluation mechanism. The Hammersley sequence ensures uniform coverage of the configuration space, while the multi-indicator heuristic evaluation mechanism intelligently guides tree expansion through a three-dimensional evaluation system incorporating diversity, distance, and angle values. After generating the initial path, a pruning algorithm removes redundant points to produce an efficient and practical final path. Extensive experimental validation in four different environmental scenarios (semi-enclosed, maze, chaotic, and crowded) demonstrates that MIHE-RRT outperforms RRT (rapidly exploring random tree), IBi-RRT (improved bidirectional rapidly exploring random tree), and HB-RRT (halton biased rapidly exploring random tree) algorithms. Results show significant improvements in planning efficiency (54–88% reduction in execution time), path quality (15–24% shorter paths), and computational resource utilization (77–94% reduction in nodes). These excellent performance metrics not only prove MIHE-RRT’s advantages in complex environments but also make it particularly suitable for practical robot navigation applications requiring reliable and efficient path planning.