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This paper introduces a shared teleoperation framework for multi-object nonprehensile transport, where a human operator controls position and a robot autonomously manages tray orientation. They derive dynamic constraints using a novel virtual object (VO) abstraction to simplify trajectory planning and implement an MPC-based trajectory smoothing algorithm to enforce real-time constraints. Experiments manipulating nine objects show a 72.45% reduction in sliding distance and elimination of tip-overs compared to a baseline, demonstrating robust adaptability.
Coordinating human input with autonomous orientation control enables stable teleoperation of multiple objects at high accelerations, even in complex scenarios.
Multi-object nonprehensile transportation in teleoperation demands simultaneous trajectory tracking and tray orientation control. Existing methods often struggle with model dependency, uncertain parameters, and multi-object adaptability. We propose a shared teleoperation framework where humans and robots share positioning control, while the robot autonomously manages orientation to satisfy dynamic constraints. Key contributions include: 1) A theoretical dynamic constraint analysis utilizing a novel virtual object (VO)-based method to simplify constraints for trajectory planning. 2) An MPC-based trajectory smoothing algorithm that enforces real-time constraints and coordinates user tracking with orientation control. 3) Validations demonstrating stable manipulation of nine objects at accelerations up to 2.4 m/s2. Compared to the baseline, our approach reduces sliding distance by 72.45% and eliminates tip-overs (0% vs. 13.9%), proving robust adaptability in complex scenarios.
