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Proposes an active-perception safe navigation framework for mobile manipulators
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Jingxing Qian et al. proposed in arXiv an object- and task-aware active perception framework that enables high-degree-of-freedom mobile manipulation robots to use hand-mounted cameras to actively inspect uncertain or safety-critical areas when navigating in semi-static environments. The framework adopts a probabilistic next-best-view (NBV) formulation and integrates it into hierarchical task model predictive control (HTMPC), using the kinematic redundancy of the manipulator to adjust the viewpoint while maintaining higher priority for chassis trajectory tracking. The paper states that in real-robot tests, the system improved map consistency and reduced task-relevant map entropy and navigation risk compared with passive or non-task-aware baselines; separate simulation and real-robot sequences demonstrated its response to controlled semi-static scene changes.
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- Object- and Task-Aware Active Perception Framework for Mobile Manipulators in Semi-Static Environments
An arXiv paper proposes an object- and task-aware active perception framework that lets a high-degree-of-freedom mobile manipulator use its hand camera to actively inspect uncertain or safety-critical regions during navigation. The framework uses a probabilistic next-best-view (NBV) formulation and is integrated with hierarchical task model predictive control (HTMPC), exploiting kinematic redundancy to adjust viewpoints while preserving the higher priority of base-trajectory tracking. Real-world tests showed that, compared with passive or non-task-aware baselines, the system improved map consistency and reduced task-related map entropy and navigation risk.
arXiv cs.RO ·