Object- and Task-Aware Active Perception for Safe Navigation of Mobile Manipulators in Semi-Static Environments
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.
ProofPaper ↗
Key points
- 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- Semi-static changes between robot visits can lead to outdated scene knowledge and increased collision risk.
- 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.
Sources (1)
- [1]Object- and Task-Aware Active Perception for Safe Navigation of Mobile Manipulators in Semi-Static EnvironmentsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 04:19 PM
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.
Mobile manipulators must navigate safely in cluttered, partially unknown environments across logistics, manufacturing, and service applications.
Extractive summary: sentences quoted from the sources.