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.

翻译：随着无人飞行器（UAV）和微型飞行器（MAV）在商业领域的广泛应用，利用其运输与操控有效载荷已成为众多研究工作的焦点。集群智能体通过协同工作实现载荷的运输与操控，可突破单个智能体的物理限制，增强系统冗余性与故障容错能力。本文对通过柔性缆绳连接至有效载荷的集群动力学进行建模，并基于人工势场法（APF）设计了一种分散式控制策略。该集群能够在未知环境中将有效载荷运输至目标位置，同时借助机载传感器获取的局部信息规避障碍物。主要贡献包括：（a）采用集中质量模型替代几何约束，实现了对缆绳更精确的建模；（b）通过势场方法设计分散式集群控制，确保在无载荷状态信息条件下系统的悬停稳定性；（c）通过APF对有效载荷的俯仰角与方位角进行操控；（d）载荷运输轨迹由目标点与障碍物产生的势场共同引导。通过外部扰动及智能体故障场景下的数值仿真，验证了所提方法的有效性。