论文标题
使用Voronoi单元和基于拥塞度量的重建的密集多代理导航
Dense Multi-Agent Navigation Using Voronoi Cells and Congestion Metric-based Replanning
论文作者
论文摘要
我们提出了一种分散的路径规划算法，用于在密集的环境中导航多个微分驱动机器人。与先前的分散方法相反，我们提出了一个新颖的基于拥堵的重型，该重新融合了本地和全球规划技术，以有效地在具有多个走廊的情况下进行导航。为了处理狭窄段落的密集场景，我们的方法使用晶格规划师计算每个代理商的初始路径。根据邻居的信息，每个代理商都使用拥塞指标在线进行在线重新启动，该指标倾向于减少碰撞并改善导航性能。此外，我们使用每个代理的伏诺元细胞来计划局部运动以及走廊选择策略，以限制狭窄通道中的拥塞。我们在复杂的仓库样场景中评估了方法的性能，并在先前的方法上表现出提高的性能和效率。此外，我们的方法在无碰撞导航到目标方面的成功率更高。
We present a decentralized path-planning algorithm for navigating multiple differential-drive robots in dense environments. In contrast to prior decentralized methods, we propose a novel congestion metric-based replanning that couples local and global planning techniques to efficiently navigate in scenarios with multiple corridors. To handle dense scenes with narrow passages, our approach computes the initial path for each agent to its assigned goal using a lattice planner. Based on neighbors' information, each agent performs online replanning using a congestion metric that tends to reduce the collisions and improves the navigation performance. Furthermore, we use the Voronoi cells of each agent to plan the local motion as well as a corridor selection strategy to limit the congestion in narrow passages. We evaluate the performance of our approach in complex warehouse-like scenes and demonstrate improved performance and efficiency over prior methods. In addition, our approach results in a higher success rate in terms of collision-free navigation to the goals.
