We present a lightweight, decentralized algorithm for navigating multiple nonholonomic agents through challenging environments with narrow passages. Our key idea is to allow agents to yield to each other in large open areas instead of narrow passages, to increase the success rate of conventional decentralized algorithms. At pre-processing time, our method computes a medial axis for the freespace. A reference trajectory is then computed and projected onto the medial axis for each agent. During run time, when an agent senses other agents moving in the opposite direction, our algorithm uses the medial axis to estimate a Point of Impact (POI) as well as the available area around the POI. If the area around the POI is not large enough for yielding behaviors to be successful, we shift the POI to nearby large areas by modulating the agent's reference trajectory and traveling speed. We evaluate our method on a row of 4 environments with up to 15 robots, and we find our method incurs a marginal computational overhead of 10-30 ms on average, achieving real-time performance. Afterward, our planned reference trajectories can be tracked using local navigation algorithms to achieve up to a $100\%$ higher success rate over local navigation algorithms alone.

翻译：我们提出了一种轻量级分散式算法，用于在包含狭窄通道的复杂环境中引导多个非完整智能体进行导航。核心思路是让智能体在开阔区域而非狭窄通道中相互避让，从而提升传统分散式算法的成功率。在预处理阶段，本方法计算自由空间的中心轴线，并为每个智能体生成一条投影至中心轴线的参考轨迹。运行时，当智能体感知到相反方向移动的其他智能体，算法利用中心轴线估计碰撞点（POI）及其周边可用区域。若POI周边区域不足以支撑有效避让行为，则通过调整智能体的参考轨迹与行进速度，将POI迁移至附近开阔区域。我们在包含多达15个机器人的4类环境中进行验证，结果表明该方法平均仅增加10-30毫秒的计算开销，实现实时性能。后续可通过局部导航算法跟踪已规划的参考轨迹，相较于仅使用局部导航算法，成功率提升高达100%。