PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment

Understand

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.