arXiv:2603.08089cs.RO2026-03ICRA被引 8

让冗余机器人在未知环境中自适应协作,兼顾任务执行与安全交互

Adaptive Vision-Based Control of Redundant Robots with Null-Space Interaction for Human-Robot Collaboration

  • 基于视觉的自适应控制+空闲空间交互,实现无需标定的任务执行
  • 理论证明系统稳定,位置误差与阻尼模型均收敛
  • 适合人机协作场景,尤其适用于动态变化环境

人机协作旨在通过机器人增强人类能力,现已应用于辅助残障人士、改造制造业流程、提升手术精度,并有望在未来改变日常生活。如何使协作效果优于单一机器人或人类,仍是开放问题。本文提出一种针对冗余机器人的新控制方案,包含任务空间的自适应视觉控制项与空闲空间的交互控制项。该设计使机器人可在未知环境中自主完成任务而无需预先标定,同时能与人类交互以应对突发状况(如碰撞风险、临时需求),在冗余配置下实现安全高效协作。任务空间与空闲空间的解耦确保了主任务不受干扰。采用李雅普诺夫方法严格证明闭环系统稳定性,保证任务空间位置误差和空闲空间阻尼模型的收敛性。实验采用增强现实(AR)引导机械臂验证控制方案性能。

原文摘要 · Abstract (English)

Human-robot collaboration aims to extend human ability through cooperation with robots. This technology is currently helping people with physical disabilities, has transformed the manufacturing process of companies, improved surgical performance, and will likely revolutionize the daily lives of everyone in the future. Being able to enhance the performance of both sides, such that human-robot collaboration outperforms a single robot/human, remains an open issue. For safer and more effective collaboration, a new control scheme has been proposed for redundant robots in this paper, consisting of an adaptive vision-based control term in task space and an interactive control term in null space. Such a formulation allows the robot to autonomously carry out tasks in an unknown environment without prior calibration while also interacting with humans to deal with unforeseen changes (e.g., potential collision, temporary needs) under the redundant configuration. The decoupling between task space and null space helps to explore the collaboration safely and effectively without affecting the main task of the robot end-effector. The stability of the closed-loop system has been rigorously proved with Lyapunov methods, and both the convergence of the position error in task space and that of the damping model in null space are guaranteed. The experimental results of a robot manipulator guided with the technology of augmented reality (AR) are presented to illustrate the performance of the control scheme.

人机协作冗余机器人视觉控制

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。