发表机构
School of Engineering and Computer Science, The Hebrew University of Jerusalem; Faculty of Data and Decision Science, Technion Israeli Institute of Technology; Hatter Department of Marine Technologies, University of Haifa; Faculty of Computer Science, Technion Israeli Institute of Technology（耶路撒冷希伯来大学工程与计算机科学学院; 以色列理工学院数据与决策科学学院; 海法大学哈特海洋技术系; 以色列理工学院计算机科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究构建统一基准对比经典与基于学习的实时运动规划方法，发现随机环境中PPO策略在延迟、成功率及路径质量上优于经典方法，障碍物演化不确定性是规划范式有效性的主导因素。
AI 中文摘要
本研究通过对经典规划方法与基于学习的方法进行受控对比，探究动态危险场中的实时运动规划问题。我们并未提出新的规划器，而是构建了一个统一基准，其中代表性的经典方法与基于学习的方法需面对相同的环境、运动约束、信息假设及评估指标。测试环境包含平面域，域内分布着旋转的洒水器式危险，这些危险通过扫过的角扇区生成随时间变化的禁入区域。我们的结果显示存在明显的机制转变：在确定性环境中，经典规划器实现了近乎完美的成功率与更高质量的路径，尽管有时需以大量规划或重规划时间为代价；然而在随机障碍物动态下，在线搜索对预算高度敏感：低预算会导致频繁失败，高预算虽提升成功率，但会以延迟和更长的路径为代价。在相同场景分布下训练的基于PPO的策略，在这些随机场景中始终在延迟、成功率及路径质量上表现更优。总体而言，结果表明，障碍物演化的不确定性而非部分可观测性，是决定哪种规划范式对当前问题实际有效的主导因素。
英文摘要
We study real-time motion planning in dynamic hazard fields through a controlled comparison between classical planning and learning-based methods. Rather than introducing a new planner, we construct a unified benchmark in which representative classical and learning-based methods face the same environments, motion constraints, information assumptions, and evaluation metrics. The test environment consists of planar domains populated with rotating sprinkler-like hazards that generate time-varying forbidden regions via sweeping angular sectors. Our results show a clear regime shift. In deterministic environments, classical planners achieve near-perfect success and higher-quality paths, though sometimes at the cost of substantial planning or replanning time. Under stochastic obstacle dynamics, however, online search becomes strongly budget-sensitive: low budgets lead to frequent failure, while high budgets improve success at the cost of latency and longer trajectories. PPO-based policies, trained under the same scenario distribution, consistently outperform in latency, success rate, and path quality in these stochastic regimes. Overall, the results indicate that uncertainty in obstacle evolution, more than partial observability, is the dominant factor determining which planning paradigm is practically effective for the problem at hand.
CommentsThis paper is scheduled to be presented at the 2027 International Symposium on Artificial Life and Robotics (AROB 2027)
