网络机器人无速度测量的任务空间单方位编队控制

IF 3.2 3区 计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS
Kun Li, Kai Zhao, Zhi Li, Yongduan Song
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引用次数: 0

摘要

本文提出了两种基于无源性的网络机器人纯方位编队控制方案,该方案能够利用车载视觉传感器实现末端执行器编队。与现有的方法相比,所开发的策略表现出以下特点。首先,它消除了对速度测量和机械手之间通信的要求,提高了系统的灵活性和可操作性,同时显著降低了计算开销。其次,建立了目标编队的指数稳定性,并证明了非冗余机械手的对偶性。此外,通过结合近似微分滤波器来补偿不可用的速度测量,进一步降低了地层的测量条件,使该方法适用于冗余和非冗余机械手。这种改进使制造商不再需要在每个机械手上安装额外的传感器,从而使地层系统更具成本效益,减少了负载,并便于实施。通过对一组双连杆机器人的仿真,验证了理论结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Task-Space Bearing-Only Formation Control for Networked Robotic Manipulators Without Velocity Measurement

In this article, two novel bearing-only formation control schemes based on the passivity property are proposed for networked robotic manipulators, which are capable of achieving end-effector formation utilizing onboard vision-based sensors. In contrast to the existing methods, the developed strategy exhibits the following features. Firstly, it eliminates the requirement for velocity measurements and communication among manipulators, which improves the flexibility and maneuverability of the system while significantly reducing computational overhead. Secondly, we establish the exponential stability of the desired formation and possess the duality property if the manipulator is non-redundant. Furthermore, by incorporating an approximate differentiation filter to compensate for unavailable velocity measurements, the measurement conditions for formation are further reduced, making the approach applicable to both redundant and non-redundant manipulators. This modification enables manufacturers to eliminate the need for an additional sensor on each manipulator, thereby making the formation system more cost-effective, reducing load, and facilitating implementation. Two simulations involving a group of two-link robotic manipulators are conducted to validate the efficiency of the theoretical results.

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来源期刊
International Journal of Robust and Nonlinear Control
International Journal of Robust and Nonlinear Control 工程技术-工程:电子与电气
CiteScore
6.70
自引率
20.50%
发文量
505
审稿时长
2.7 months
期刊介绍: Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.
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