提出视觉预测控制方案，实现机器人自动对接的高精度与抗干扰能力
DA-VPC: Disturbance-Aware Visual Predictive Control Scheme of Docking Maneuvers for Autonomous Trolley Collection
- 结合红外标记与视觉伺服，显式建模运动约束与可见性条件
- 在多种环境测试中实现高精度对接，误差小于2.5厘米
- 适合需稳定对接的移动机器人系统，如仓库自动取车
服务机器人在机场或仓库等公共场所展现出自主收集和调度手推车的巨大潜力，可提升效率并降低成本。通常，多辆手推车的自主收集与运输依赖于移动操作臂的领航-跟随编队，其中移动底盘的精准对接动作是将手推车对齐成有序队列的关键。然而，基于视觉的机器人对接系统面临高精度要求、环境干扰及机器人自身约束等挑战。为此，本文提出一种扰动感知的视觉预测控制（DA-VPC）方案，采用主动红外标记以在不同光照条件下实现鲁棒特征提取。该框架显式建模非完整运动学与视觉可见性约束，通过优化求解图像基视觉伺服中的预测控制问题，并引入扩展状态观测器（ESO）以抵消推车过程中的扰动，确保对接的精确与稳定。在多种环境下的实验结果表明，该系统具有强鲁棒性，定量评估验证了其高对接精度。
原文摘要 · Abstract (English)
Service robots have demonstrated significant potential for autonomous trolley collection and redistribution in public spaces like airports or warehouses to improve efficiency and reduce cost. Usually, a fully autonomous system for the collection and transportation of multiple trolleys is based on a Leader-Follower formation of mobile manipulators, where reliable docking maneuvers of the mobile base are essential to align trolleys into organized queues. However, developing a vision-based robotic docking system faces significant challenges: high precision requirements, environmental disturbances, and inherent robot constraints. To address these challenges, we propose a Disturbance-Aware Visual Predictive Control (DA-VPC) scheme that incorporates active infrared markers for robust feature extraction across diverse lighting conditions. This framework explicitly models nonholonomic kinematics and visibility constraints for image-based visual servoing (IBVS), solving the predictive control problem through optimization. It is augmented with an extended state observer (ESO) designed to counteract disturbances during trolley pushing, ensuring precise and stable docking. Experimental results across diverse environments demonstrate the robustness of this system, with quantitative evaluations confirming high docking accuracy.
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