用少量经验数据提升复杂环境下的路径规划效率与成功率。
Experience-based Optimal Motion Planning Algorithm for Solving Difficult Planning Problems Using a Limited Dataset
- 基于单次经验生成微路径，动态调整轨迹形状适应环境复杂度。
- 在100次经验下成功率达49.3%提升，路径成本降低56.3%。
- 仅需1次经验即可显著优化规划性能，适合数据稀缺场景。
本研究旨在通过泛化有限数据，在短时间内获得高质量路径解。在有知经验驱动的随机树连接星（IERTC*）算法中，通过形态化单一经验生成的微路径，结合重连机制与有知采样策略，灵活探索搜索树并降低路径代价。该算法根据局部环境复杂度采用不同策略：障碍物密集时使用复杂曲线轨迹，稀疏区域则采用直线。在通用运动基准测试中，使用100次经验时，IERTC*在杂乱环境中平均成功率提升49.3%，路径成本降低56.3%；即使仅提供1次经验，成功率仍提升43.8%，路径成本下降57.8%，表现出卓越规划性能。
原文摘要 · Abstract (English)
This study aims to address the key challenge of obtaining a high-quality solution path within a short calculation time by generalizing a limited dataset. In the informed experience-driven random trees connect star (IERTC*) process, the algorithm flexibly explores the search trees by morphing the micro paths generated from a single experience while reducing the path cost by introducing a re-wiring process and an informed sampling process. The core idea of this algorithm is to apply different strategies depending on the complexity of the local environment; for example, it adopts a more complex curved trajectory if obstacles are densely arranged near the search tree, and it adopts a simpler straight line if the local environment is sparse. The results of experiments using a general motion benchmark test revealed that IERTC* significantly improved the planning success rate in difficult problems in the cluttered environment (an average improvement of 49.3% compared to the state-of-the-art algorithm) while also significantly reducing the solution cost (a reduction of 56.3%) when using one hundred experiences. Furthermore, the results demonstrated outstanding planning performance even when only one experience was available (a 43.8% improvement in success rate and a 57.8% reduction in solution cost).
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