提出预测性风险感知规划，让机器人提前避开未来可能被堵死的路径。
RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation
- 基于局部运动假设生成未来障碍物可能行为，评估路径风险
- 在模拟中零碰撞完成任务，安全指标优于非自适应方法
- 适合动态狭窄场景下的机器人导航，可嵌入现有导航系统
移动机器人可能在碰撞前就已失败：当前安全的速度可能导致其进入未来障碍物将封闭的通道。本文研究这一预测性近撞风险问题，提出风险敏感的推测情景规划（RCSP），一种对短期障碍物未来行为进行评估的规划层。RCSP维护轻量级局部运动假设信念，采样未来交互，惩罚高风险尾部，并通过局部安全检查执行。在受控的MuJoCo瓶颈任务中，RCSP规划器无碰撞达成目标，二次安全与路径质量评分均高于非自适应预测器，仅增加少量延迟。在ROS2/Gazebo环境中，将该安全层加入标准Nav2栈后，动态近撞失败显著减少。在官方DynaBARN/Jackal迁移测试中，调优后的DWA和TEB仍表现更优，揭示了该方法的边界。仿真结果表明，RCSP可作为现有导航系统在动态瓶颈场景中的预测风险补充模块。
原文摘要 · Abstract (English)
Mobile robots can fail before they collide: a velocity that is safe now may commit the robot to a passage that moving obstacles will soon close. We study this predictive near-miss commitment problem and propose Risk-Sensitive Conjectural Scenario Planning (RCSP), a planning layer that evaluates candidate commands against plausible short-horizon obstacle futures. RCSP maintains a lightweight belief over local motion conjectures, samples future interactions, penalizes high-risk tails, and executes through a local safety check. In controlled MuJoCo bottleneck tasks, the RCSP planner reaches the goal without collisions and yields higher secondary safety and path-quality point estimates than a non-adaptive predictor, with additional latency. In ROS2/Gazebo, adding the local safety layer to a standard Nav2 stack reduces dynamic near-miss failures. On official DynaBARN/Jackal transfer, tuned DWA and TEB remain stronger on strict benchmark success, revealing the boundary of the approach. These simulation results position RCSP as a predictive-risk module that complements existing navigation stacks in dynamic bottleneck regimes.
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