一种用于辅助型机器人操作器的基于反应式性能的共享控制框架
A Reactive performance-based Shared Control Framework for Assistive Robotic Manipulators
AI总结：
本文提出一种基于局部性能加权的新型AAN反应式共享控制框架，用于辅助机器人夹持器，可按需为用户提供帮助，实验验证其能提升性能、降低误差且不影响力学参数。
摘要：
在物理人机交互（pHRI）夹持器场景中，人类与机器人可能同时对动作做出贡献，因此有必要确定如何融合二者的指令。控制权限可在一定限度内于二者之间切换，也可依据某些准则融合输入指令。按需辅助（Assist-As-Needed, AAN）范式聚焦于第二种方法，其控制器预期为用户提供最低限度的必要辅助。部分AAN系统依赖预测人类意图来调整动作，但如果预测难度过高，反应式AAN系统可对输入指令加权以生成融合指令。本文提出一种用于机器人夹持器的新型AAN反应式控制系统，该系统根据各输入指令的局部性能对其进行加权。因此，该系统并非最小化跟踪误差或与预期速度的偏差，而是根据人类的需求提供更多帮助。研究人员使用安装在力敏机器人手臂上的夹持器对系统进行了测试，该手臂可提供评估参数。测试内容为用双臂完成空中平面路径运动：机器人夹持住人的前臂后，屏幕上会显示路径和机器人的当前位置，为人类提供反馈。研究将所提控制方法与无辅助情况、以及作为基准的阻抗控制结果进行了对比。对10名志愿者的结果统计分析证明，所提控制器提升了整体性能，降低了跟踪误差。此外，与阻抗控制不同，所提控制器不会显著影响施加的力、指令变化量或分歧度（分歧度以人类指令与输出指令之间的角度差衡量）。结果表明所提控制方案符合AAN范式，不过未来工作需要针对更复杂的环境和任务开展进一步测试。
英文摘要：
In Physical Human--Robot Interaction (pHRI) grippers, humans and robots may contribute simultaneously to actions, so it is necessary to determine how to combine their commands. Control may be swapped from one to the other within certain limits, or input commands may be combined according to some criteria. The Assist-As-Needed (AAN) paradigm focuses on this second approach, as the controller is expected to provide the minimum required assistance to users. Some AAN systems rely on predicting human intention to adjust actions. However, if prediction is too hard, reactive AAN systems may weigh input commands into an emergent one. This paper proposes a novel AAN reactive control system for a robot gripper where input commands are weighted by their respective local performances. Thus, rather than minimizing tracking errors or differences to expected velocities, humans receive more help depending on their needs. The system has been tested using a gripper attached to a sensitive robot arm, which provides evaluation parameters. Tests consisted of completing an on-air planar path with both arms. After the robot gripped a person's forearm, the path and current position of the robot were displayed on a screen to provide feedback to the human. The proposed control has been compared to results without assistance and to impedance control for benchmarking. A statistical analysis of the results proves that global performance improved and tracking errors decreased for ten volunteers with the proposed controller. Besides, unlike impedance control, the proposed one does not significantly affect exerted forces, command variation, or disagreement, measured as the angular difference between human and output command. Results support that the proposed control scheme fits the AAN paradigm, although future work will require further tests for more complex environments and tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
