利用多架无人机协同运输带有偏移CG的有效载荷

IF 1 Q4 AUTOMATION & CONTROL SYSTEMS
Shraddha Barawkar, Manish Kumar
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引用次数: 1

摘要

近十年来,多架无人机(uav)协同运输一直是机器人研究人员感兴趣的话题。研究人员开发了不同的控制方案来解决与合作运输有关的一些问题。然而,大多数现有的控制策略都假设一个与载荷几何中心一致的静止重心。在现实世界的应用中,如包裹递送或人类运输,一般来说,CG的位置不会在几何中心,或者甚至不会先验地知道。本文提出了一种比例、积分和导数(PID)控制器,以解决在使用多架无人机运输共同有效载荷时,当CG偏移时的控制问题。所提出的PID方案在本质上是集中的,因为它为所有无人机提供相同的控制动作。通过大量的数值模拟,本文表明,无论CG在有效载荷上的位置如何,这种方案都能有效地工作。控制方案也独立于有效载荷几何形状和用于协同运输的无人机数量。本文的工作为解决具有先验未知CG的多无人机协同运输问题提供了更好的控制策略基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cooperative Transport of a Payload With Offset CG Using Multiple UAVs
Cooperative transportation by multiple Unmanned Aerial Vehicles (UAVs) has been a topic of interest amongst robotics researchers since a decade. Researchers have developed different control schemes to address some of the issues related to cooperative transport. However, most of the existing control strategies assume a stationary center of gravity (CG) coinciding with the geometric center of the payload. In real world applications such as package delivery or human transport, position of CG, in general, would not be at geometric center, or would not be even known a – priori. This paper proposes a Proportional, Integral and Derivative (PID) controller to address the issue of control when the CG is offset during transportation of a common payload using multiple UAVs. The proposed PID scheme is centralized in nature in that it provides the same control action to all UAVs. Using extensive numerical simulations, the paper shows that such a scheme is shown to work effectively irrespective of the location of CG on the payload. The control scheme is also independent of the payload geometry and number of UAVs employed for cooperative transport. The work presented in this paper provides the groundwork to develop better control strategies to solve the problem of multi-UAV cooperative transport with a – priori unknown CG.
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来源期刊
Mechatronic Systems and Control
Mechatronic Systems and Control AUTOMATION & CONTROL SYSTEMS-
CiteScore
1.40
自引率
66.70%
发文量
27
期刊介绍: This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.
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