This paper presents a fully decentralized approach for realtime non-cooperative multi-robot navigation in social mini-games, such as navigating through a narrow doorway or negotiating right of way at a corridor intersection. Our contribution is a new realtime bi-level optimization algorithm, in which the top-level optimization consists of computing a fair and collision-free ordering followed by the bottom-level optimization which plans optimal trajectories conditioned on the ordering. We show that, given such a priority order, we can impose simple kinodynamic constraints on each robot that are sufficient for it to plan collision-free trajectories with minimal deviation from their preferred velocities, similar to how humans navigate in these scenarios. We successfully deploy the proposed algorithm in the real world using F$1/10$ robots, a Clearpath Jackal, and a Boston Dynamics Spot as well as in simulation using the SocialGym 2.0 multi-agent social navigation simulator, in the doorway and corridor intersection scenarios. We compare with state-of-the-art social navigation methods using multi-agent reinforcement learning, collision avoidance algorithms, and crowd simulation models. We show that $(i)$ classical navigation performs $44\%$ better than the state-of-the-art learning-based social navigation algorithms, $(ii)$ without a scheduling protocol, our approach results in collisions in social mini-games $(iii)$ our approach yields $2\times$ and $5\times$ fewer velocity changes than CADRL in doorways and intersections, and finally $(iv)$ bi-level navigation in doorways at a flow rate of $2.8 - 3.3$ (ms)$^{-1}$ is comparable to flow rate in human navigation at a flow rate of $4$ (ms)$^{-1}$.

翻译：本文提出一种完全去中心化的方法，用于在社交迷你博弈场景（如穿越狭窄门道或在走廊交叉口协商路权）中实现实时非协作多机器人导航。我们的贡献在于一种新颖的实时双层优化算法：顶层优化计算公平且无碰撞的通行顺序，底层优化则基于该顺序规划最优轨迹。我们证明，在给定优先级顺序的情况下，可为每个机器人施加简单的运动学约束，使其能够规划无碰撞轨迹，同时尽可能减少对偏好速度的偏离——这与人类在此类场景中的导航方式类似。我们成功在真实环境中部署了该算法，使用F$1/10$机器人、Clearpath Jackal以及Boston Dynamics Spot，同时通过SocialGym 2.0多智能体社交导航模拟器在门道和走廊交叉口场景中进行仿真验证。我们将该方法与基于多智能体强化学习的最先进社交导航方法、碰撞避免算法及人群仿真模型进行了对比。结果表明：（i）经典导航方法相比最先进的基于学习的社交导航算法性能提升$44\%$；（ii）无调度协议时，我们的方法在社交迷你博弈中会导致碰撞；（iii）在门道和交叉口场景中，我们的方法相比CADRL的速度变化次数减少$2$倍和$5$倍；（iv）门道场景中双层导航的通量率$2.8-3.3$ (ms)$^{-1}$与人类导航的通量率$4$ (ms)$^{-1}$相当。