PACE: Proactive UAV-Assisted Collaborative Motion Planning for UGV Navigation Enhancement

Abstract

Existing air–ground collaborative systems often rely on passive or reactive coordination, which can cause discontinuous motion and underuse the UAV's ability for anticipatory support. To address these limitations, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PACE</i>, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i>roactive <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i>erial <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i>ompanion for UGV navigation <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i>nhancement where the UAV autonomously schedules perception tasks to anticipate the navigation needs of an Unmanned Ground Vehicle (UGV). We formulate the global scheduling as an Orienteering Problem with Time Windows (OPTW). Unlike traditional coverage planners, this formulation enables the UAV to selectively maximize information gain from critical frontiers strictly within the dynamic temporal constraints imposed by the UGV's continuous motion. Within the scheduled regions, a motion-aware refinement module further refines the UAV's local navigation path. Finally, the fused aerial perception is integrated into the UGV's planning process, extending its perceptual horizon in cluttered environments. The method has been validated in both simulations and real-world outdoor environments, demonstrating superior efficiency and continuous coordination capabilities.