让机器人在人群中找空隙，用概率模型引导高效避障。
Finding the Easy Way Through -- the Probabilistic Gap Planner for Social Robot Navigation
- 分两步规划：先找宏观路径空隙，再解决即时交互碰撞
- 仿真中减少碰撞率，保持更大安全距离，降低人群紧张感
- 适合需要穿行密集人流的移动机器人，实时运行于真实机器人
在社交机器人导航中，自主代理需处理与他人连续的互动。当前最先进的规划器能高效合作解决下一个即时交互，但不关注更长的规划视野，难以在需选择策略寻找人群间隙或通道的场景中表现良好。本文提出将轨迹规划分解为两个独立步骤：冲突规避以获取良好的宏观轨迹，以及协作式碰撞避免（CCA）以最优解决下一个交互。我们提出概率空隙规划器（PGP）作为冲突规避规划器，通过改进现有概率碰撞风险模型，引入普遍的协作性假设，并引导短期CCA规划器朝向人群中的空隙。在不同密度人群的大量仿真中，结合PGP与先进CCA规划器可显著提升代理性能：平均而言，代理与他人保持更大间距，减少紧张感，且碰撞更少，通常代价是路径稍长。PGP已在本田研发的WaPOCHI移动机器人上实现实时运行。
原文摘要 · Abstract (English)
In Social Robot Navigation, autonomous agents need to resolve many sequential interactions with other agents. State-of-the art planners can efficiently resolve the next, imminent interaction cooperatively and do not focus on longer planning horizons. This makes it hard to maneuver scenarios where the agent needs to select a good strategy to find gaps or channels in the crowd. We propose to decompose trajectory planning into two separate steps: Conflict avoidance for finding good, macroscopic trajectories, and cooperative collision avoidance (CCA) for resolving the next interaction optimally. We propose the Probabilistic Gap Planner (PGP) as a conflict avoidance planner. PGP modifies an established probabilistic collision risk model to include a general assumption of cooperativity. PGP biases the short-term CCA planner to head towards gaps in the crowd. In extensive simulations with crowds of varying density, we show that using PGP in addition to state-of-the-art CCA planners improves the agents' performance: On average, agents keep more space to others, create less tension, and cause fewer collisions. This typically comes at the expense of slightly longer paths. PGP runs in real-time on WaPOCHI mobile robot by Honda R&D.
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