面向弹性负载运输的自组织空中集群机器人：一种受桌力学启发的方法
Self-Organizing Aerial Swarm Robotics for Resilient Load Transportation : A Table-Mechanics-Inspired Approach
AI总结：
针对现有空中集群协同运输方法的可扩展性、通信依赖及抗故障鲁棒性问题，提出受桌腿耗散力学启发的去中心化无通信控制方法，经仿真和真机实验验证其在多扰动场景下精度与鲁棒性显著提升。
摘要：
现有方法在可扩展性、通信依赖性以及应对动态故障的鲁棒性方面存在局限，相比之下，借助机器人群体开展协同空中运输在物流和灾害响应领域具有变革性潜力。本文提出了一种受物理学启发的飞行机器人群体协同运输方法，该方法模拟了桌腿负载分配的耗散力学特性。通过构建去中心化耗散力模型，本方法无需显式通信即可实现自主编队稳定和自适应负载分配。每台机器人基于邻近局部机器人以及悬挂的有效载荷动态调整自身位置，这一过程类似于桌腿耗散能量的反作用力。本文严格证明了所得控制系统的稳定性。仿真结果表明，在能力变化、缆索不确定性、视野受限和有效载荷变化四种场景下，所提方法的跟踪误差分别仅为现有方法的20%、68%、55.5%和21.9%。在使用六台飞行机器人开展的真实世界实验中，该协同空中运输系统在单机器人故障、连接断开事件、25%有效载荷变化以及40%缆索长度不确定性的条件下，成功率达到94%，且在户外蒲福风级4级的风力下仍表现出强鲁棒性。总体而言，这种受物理学启发的方法打通了群体智能与机械稳定性原理，为异构空中系统在通信受限环境中共同执行复杂运输任务提供了一种可扩展的框架。
英文摘要：
In comparison with existing approaches, which struggle with scalability, communication dependency, and robustness against dynamic failures, cooperative aerial transportation via robot swarms holds transformative potential for logistics and disaster response. Here, we present a physics-inspired cooperative transportation approach for flying robot swarms that imitates the dissipative mechanics of table-leg load distribution. By developing a decentralized dissipative force model, our approach enables autonomous formation stabilization and adaptive load allocation without the requirement of explicit communication. Based on local neighbor robots and the suspended payload, each robot dynamically adjusts its position. This is similar to energy-dissipating table leg reactions. The stability of the resultant control system is rigorously proved. Simulations demonstrate that the tracking errors of the proposed approach are 20%, 68%, 55.5%, and 21.9% of existing approaches under the cases of capability variation, cable uncertainty, limited vision, and payload variation, respectively. In real-world experiments with six flying robots, the cooperative aerial transportation system achieved a 94% success rate under single-robot failure, disconnection events, 25% payload variation, and 40% cable length uncertainty, demonstrating strong robustness under outdoor winds up to Beaufort scale 4. Overall, this physics-inspired approach bridges swarm intelligence and mechanical stability principles, offering a scalable framework for heterogeneous aerial systems to collectively handle complex transportation tasks in communication-constrained environments.
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。
