Fully decentralized, multiagent trajectory planners enable complex tasks like search and rescue or package delivery by ensuring safe navigation in unknown environments. However, deconflicting trajectories with other agents and ensuring collision-free paths in a fully decentralized setting is complicated by dynamic elements and localization uncertainty. To this end, this paper presents (1) an uncertainty-aware multiagent trajectory planner and (2) an image segmentation-based frame alignment pipeline. The uncertainty-aware planner propagates uncertainty associated with the future motion of detected obstacles, and by incorporating this propagated uncertainty into optimization constraints, the planner effectively navigates around obstacles. Unlike conventional methods that emphasize explicit obstacle tracking, our approach integrates implicit tracking. Sharing trajectories between agents can cause potential collisions due to frame misalignment. Addressing this, we introduce a novel frame alignment pipeline that rectifies inter-agent frame misalignment. This method leverages a zero-shot image segmentation model for detecting objects in the environment and a data association framework based on geometric consistency for map alignment. Our approach accurately aligns frames with only 0.18 m and 2.7 deg of mean frame alignment error in our most challenging simulation scenario. In addition, we conducted hardware experiments and successfully achieved 0.29 m and 2.59 deg of frame alignment error. Together with the alignment framework, our planner ensures safe navigation in unknown environments and collision avoidance in decentralized settings.

翻译：完全去中心化的多智能体轨迹规划器通过在未知环境中确保安全导航，能够支持搜索救援或包裹递送等复杂任务。然而，在完全去中心化场景下，动态因素和定位不确定性使得消除智能体间轨迹冲突并确保无碰撞路径变得复杂。为此，本文提出：(1) 一种不确定性感知多智能体轨迹规划器；(2) 一种基于图像分割的帧对齐流水线。不确定性感知规划器传播检测到的障碍物未来运动的相关不确定性，通过将这些传播的不确定性纳入优化约束，规划器能有效绕行障碍物。与强调显式障碍物追踪的传统方法不同，本方法采用隐式追踪。智能体间共享轨迹可能因帧未对齐导致潜在碰撞，为此我们提出一种新型帧对齐流水线来校正智能体间帧未对齐。该方法利用零样本图像分割模型检测环境中的物体，并基于几何一致性的数据关联框架实现地图对齐。在最具挑战性的仿真场景中，本方法仅产生0.18米和2.7度的平均帧对齐误差。此外，我们开展的硬件实验成功实现了0.29米和2.59度的帧对齐误差。结合对齐框架，本规划器能够在未知环境中实现安全导航，并在去中心化场景下避免碰撞。