Object- and Task-Aware Active Perception for Safe Navigation of Mobile Manipulators in Semi-Static Environments

Abstract

Mobile manipulators must navigate safely in cluttered, partially unknown environments across logistics, manufacturing, and service applications. Semi-static changes between robot visits can lead to outdated scene knowledge and increased collision risk. We propose an active perception framework that enables a high degree-of-freedom (DoF) mobile manipulator to inspect uncertain or safety-critical regions with its hand camera during navigation. A probabilistic, object- and task-aware next-best-view (NBV) formulation prioritizes observations near the desired path and around uncertain or potentially changing objects. The perception objective is integrated into hierarchical-task model predictive control (HTMPC), exploiting kinematic redundancy for viewpoint adjustment while preserving the higher priority of base-trajectory tracking. The system unifies navigation, map maintenance, and active perception in a closed loop. Real-world tests show improved map consistency, reduced task-dependent map entropy, and lower navigation risk than passive or non-task-aware baselines. Additional simulation and real-world sequences demonstrate responses to controlled semi-static scene changes.