Multi-Indicator Heuristic Evaluation-Based Rapidly Exploring Random Tree Algorithm for Robot Path Planning in Complex Environments
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 sampli...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/13/4/274 |
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| Summary: | 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. |
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| ISSN: | 2075-1702 |