RCSP: 面向安全动态机器人导航的风险敏感推测性场景规划
RCSP: Risk-Sensitive Conjectural Scenario Planning for Safe Dynamic Robot Navigation
AI总结：
提出风险敏感推测性场景规划（RCSP），通过轻量级信念维护、未来交互采样和高风险尾部惩罚，结合局部安全检查，解决移动机器人在动态障碍物环境中的预测性近碰撞承诺问题，并在仿真中验证其提升安全性和路径质量。
摘要：
移动机器人在碰撞之前就可能失败：当前安全的速度可能使机器人陷入即将被移动障碍物关闭的通道。我们研究了这种预测性近碰撞承诺问题，并提出了风险敏感推测性场景规划（RCSP），这是一个规划层，它根据合理的短视障碍物未来对候选命令进行评估。RCSP维护一个关于局部运动推测的轻量级信念，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规划器无碰撞地到达目标，并且与非自适应预测器相比，提供了更高的次要安全性和路径质量点估计，但增加了延迟。在ROS2/Gazebo中，将局部安全层添加到标准Nav2堆栈可减少动态近碰撞失败。在官方DynaBARN/Jackal迁移中，调整后的DWA和TEB在严格的基准成功率上仍然更强，揭示了该方法的边界。这些仿真结果将RCSP定位为一个预测风险模块，在动态瓶颈机制中补充现有的导航堆栈。
英文摘要：
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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