基于智能体集群的缆绳悬挂载荷的分布式空中运输与操控
Decentralized Aerial Transportation and Manipulation of a Cable-Slung Payload With Swarm of Agents
AI总结：
本文针对无人机集群通过柔性缆绳运输载荷的问题，提出了基于人工势场的分布式控制方法，实现避障运输与姿态操控，并通过数值模拟验证了其在扰动和故障下的有效性。
摘要：
随着无人机（UAV）和微型飞行器（MAV）在商业领域的出现，它们在运输和操控载荷方面的应用吸引了大量研究工作。一组智能体协同工作以运输和操控载荷，可以克服单个智能体的物理限制，增加冗余性和对故障的容忍度。本文对通过柔性缆绳连接到载荷的智能体集群的动力学进行了建模，并使用人工势场（APF）设计了分布式控制。该集群能够在未知环境中将载荷运输到目标位置，同时利用机载传感器获取的局部信息避开障碍物。主要贡献包括：(a) 使用集中质量模型而非几何约束对缆绳进行更精确的建模；(b) 使用势场方法设计分布式集群控制，以确保在无载荷状态信息的情况下系统的悬停稳定性；(c) 通过APF控制载荷的仰角和方位角的操控；(d) 载荷的运输轨迹由目标点和障碍物产生的势场决定。通过数值模拟评估了所提出方法的有效性，模拟中考虑了外部扰动和智能体失效的影响。
英文摘要：
With the advent of Unmanned Aerial Vehicles (UAV) and Micro Aerial Vehicles (MAV) in commercial sectors, their application for transporting and manipulating payloads has attracted many research work. A swarm of agents, cooperatively working to transport and manipulate a payload can overcome the physical limitations of a single agent, adding redundancy and tolerance against failures. In this paper, the dynamics of a swarm connected to a payload via flexible cables are modeled, and a decentralized control is designed using Artificial Potential Field (APF). The swarm is able to transport the payload through an unknown environment to a goal position while avoiding obstacles from the local information received from the onboard sensors. The key contributions are (a) the cables are modelled more accurately using lumped mass model instead of geometric constraints, (b) a decentralized swarm control is designed using potential field approach to ensure hover stability of system without payload state information, (c) the manipulation of payload elevation and azimuth angles are controlled by APF, and (d) the trajectory of the payload for transportation is governed by potential fields generated by goal point and obstacles. The efficacy of the method proposed in this work are evaluated through numerical simulations under the influence of external disturbances and failure of agents.
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。
