新算法让机器人路径更安全且不长太多，还自带评估标准。
Balancing Safety and Optimality in Robot Path Planning: Algorithm and Metric
- 用自适应权重动态平衡路径长短与避障距离
- 复杂环境里安全最优综合得分达0.94，路径只多0.5-1%
- 适合需要兼顾安全与效率的机器人实操场景
自主机器人路径规划面临路径长度与障碍物间距之间的根本权衡。现有算法通常只侧重单一目标，本文提出统一路径规划器（UPP），一种图搜索算法，通过自适应启发式加权动态平衡安全性与最优性。UPP采用局部逆距离安全场，并根据实时搜索进度自动调节参数，在保持有界启发式行为的同时实现更优的避障性能。为支持严谨评估，引入了OptiSafe指数，一个归一化指标，量化安全与最优性的权衡关系。在10个环境中的大量测试表明，UPP在杂乱环境中获得0.94的OptiSafe评分，优于现有方法的0.22–0.85，路径长度仅增加0.5–1%；仿真中成功率100%。硬件验证在TurtleBot上成功，证实了其在真实场景下的实用性，尽管存在仿真到现实的差距。
原文摘要 · Abstract (English)
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, maintaining bounded heuristic behavior 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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。