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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.23197 |
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| _version_ | 1866917339625160704 |
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| author | Arora, Jatin Kumar Bandyopadhyay, Soutrik Sulania, Sunil Bhasin, Shubhendu |
| author_facet | Arora, Jatin Kumar Bandyopadhyay, Soutrik Sulania, Sunil Bhasin, Shubhendu |
| contents | Path planning for autonomous robots faces a fundamental trade-off between path length and obstacle clearance. While existing algorithms typically prioritize a single objective, we introduce the Unified Path Planner (UPP), a graph-search algorithm that dynamically balances safety and optimality via adaptive heuristic weighting. UPP employs a local inverse-distance safety field and auto-tunes its parameters based on real-time search progress, achieving provable suboptimality bounds while maintaining superior clearance. To enable rigorous evaluation, we introduce the OptiSafe index, a normalized metric that quantifies the trade-off between safety and optimality. Extensive evaluation across 10 environments shows that UPP achieves a 0.94 OptiSafe score in cluttered environments, compared with 0.22-0.85 for existing methods, with only 0.5-1% path-length overhead in simulation and a 100% success rate. Hardware validation on TurtleBot confirms practical advantages despite sim-to-real gaps. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23197 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric Arora, Jatin Kumar Bandyopadhyay, Soutrik Sulania, Sunil Bhasin, Shubhendu Robotics Artificial Intelligence Path planning for autonomous robots faces a fundamental trade-off between path length and obstacle clearance. While existing algorithms typically prioritize a single objective, we introduce the Unified Path Planner (UPP), a graph-search algorithm that dynamically balances safety and optimality via adaptive heuristic weighting. UPP employs a local inverse-distance safety field and auto-tunes its parameters based on real-time search progress, achieving provable suboptimality bounds while maintaining superior clearance. To enable rigorous evaluation, we introduce the OptiSafe index, a normalized metric that quantifies the trade-off between safety and optimality. Extensive evaluation across 10 environments shows that UPP achieves a 0.94 OptiSafe score in cluttered environments, compared with 0.22-0.85 for existing methods, with only 0.5-1% path-length overhead in simulation and a 100% success rate. Hardware validation on TurtleBot confirms practical advantages despite sim-to-real gaps. |
| title | Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2505.23197 |