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Regulation of 2D Arm Stability Against Unstable, Damping-Defined Environments in Physical Human-Robot Interaction

机译:在物理人员机器人交互中对2D臂稳定性的调节,对不稳定的,阻尼界定的环境

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This paper presents an experimental study to investigate how humans interact with a robotic arm simulating primarily unstable, damping-defined, mechanical environments, and to quantify lower bounds of robotic damping that humans can stably interact with. Human subjects performed posture maintenance tasks while a robotic arm simulated a range of negative damping-defined environments and transiently perturbed the human arm to challenge postural stability. Analysis of 2-dimensional kinematic responses in both the time domain and phase space allowed us to evaluate stability of the coupled human-robot system in both anterior-posterior (AP) and medial-lateral (ML) directions, and to determine the lower bounds of robotic damping for stable physical human-robot interaction (pHRI). All subjects demonstrated higher capacity to stabilize their arm against negative damping-defined environments in the AP direction than the ML direction, evidenced by all 3 stability measures used in this study. Further, the lower bound of robotic damping for stable pHRI was more than 3.5 times lower in the AP direction than the ML direction: -30.0 Ns/m and -8.2 Ns/m in the AP and ML directions, respectively. Sensitivity analysis confirmed that the results in this study were relatively insensitive to varying experimental conditions. Outcomes of this study would allow us to design a less conservative robotic impedance controller that utilizes a wide range of robotic damping, including negative damping, and achieves more transparent and agile operations without compromising coupled stability and safety of the human-robot system, and thus improves the overall performance of pHRI.
机译:本文提出了一种实验研究,调查人们如何与机器人臂进行互动模拟主要不稳定,阻尼定义,机械环境,并量化人类可以稳定地与之相互作用的机器人阻尼的下限。人类受试者在机器人臂模拟一系列负阻尼定义的环境中进行了姿势维护任务,瞬时扰乱了人臂以挑战姿势稳定性。在时域和相空间中的二维运动响应分析允许我们评估前后(AP)和内侧 - 横向(ML)方向上的耦合人机系统的稳定性,并确定下限稳定物理人体机器人相互作用的机器人阻尼(PHRI)。所有受试者都表现出更高的容量,以稳定其在AP方向上的负阻尼定义的环境比ML方向稳定,所以通过本研究中使用的所有3个稳定性措施所证明。此外,在AP和ML方向上分别在AP方向上的稳定PHRI的机器人阻尼的下限分别比ML方向:-30.0ns / m和-8.2ns / m越低3.5倍。敏感性分析证实,该研究的结果对不同的实验条件相对不敏感。本研究的结果允许我们设计一种较少保守的机器人阻抗控制器,该阻抗控制器利用广泛的机器人阻尼,包括负阻尼,并且在不影响人机系统的耦合稳定性和安全性的情况下实现更透明和敏捷的操作,从而实现了更透明和敏捷的操作,因此提高了Phri的整体表现。

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