人机共控下用虚拟物体模型实现多物非抓取运输，抗干扰更强。
Towards Multi-Object Nonprehensile Transportation via Shared Teleoperation: A Framework Based on Virtual Object Model Predictive Control
- 用虚拟物体建模简化轨迹规划中的动态约束。
- 机器人自主调姿，实测9个物体加速2.4米/秒²不滑不倒。
- 适合需要高稳定性多物运输的远程操作场景。
遥操作中的多物体非抓取运输需同步实现轨迹跟踪与托盘姿态控制。现有方法常受限于模型依赖、参数不确定性及多物适应性差。本文提出一种人机共控框架：人类负责位置控制，机器人自主管理姿态以满足动态约束。核心贡献包括：1）基于新型虚拟物体（VO）的方法，理论分析并简化轨迹规划中的动态约束；2）基于模型预测控制（MPC）的轨迹平滑算法，实时协调用户跟踪与姿态控制；3）实验验证在加速度达2.4 m/s²时稳定操控九个物体。相比基线方法，滑动距离减少72.45%，翻倒率从13.9%降至0%，证明在复杂场景中具备强适应性。
原文摘要 · Abstract (English)
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.
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