Virtual-Tube-Based Cooperative Transport Control for Multi-UAV Systems in Constrained Environments
This paper proposes a novel control framework for cooperative transportation of cable-suspended loads by multiple unmanned aerial vehicles (UAVs) operating in constrained environments. Leveraging virtual tube theory and principles from dissipative systems theory, the framework facilitates efficient multi-UAV collaboration for navigating obstacle-rich areas. The proposed framework offers several key advantages. (1) It achieves tension distribution and coordinated transportation within the UAV-cable-load system with low computational overhead, dynamically adapting UAV configurations based on obstacle layouts to facilitate efficient navigation. (2) By integrating dissipative systems theory, the framework ensures high stability and robustness, essential for complex multi-UAV operations. The effectiveness of the proposed approach is validated through extensive simulations, demonstrating its scalability for large-scale multi-UAV systems. Furthermore, the method is experimentally validated in outdoor scenarios, showcasing its practical feasibility and robustness under real-world conditions.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Trajectory Planning for a Multi-UAV Rigid-Payload Cascaded Transportation System Based on Enhanced Tube-RRT*
This paper presents a two-stage trajectory planning framework for a multi-UAV rigid-payload cascaded transportation system, aiming to address planning challenges in densely cluttered environments. In Stage I, an Enhanced…
Trajectory PlanningDistributed Multi Robot Lunar Cargo Transportation via Phase Decomposed Reinforcement Learning
Modular reconfigurable robotic systems provide a scalable solution for cooperative surface operations in future lunar missions. However, cooperative cargo transportation remains challenging due to morphology-dependent to…
Reinforcement LearningDecision MakingDeep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport
In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network …
Deep Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Cooperative Load Transportation Using Multiple UAVs
The aim of this paper is cooperative task assignment to multiple unmanned aerial vehicles (UAV) for load transportation. The main goal is to transport a slung load safely with minimal swing. To this end, for each UAV, wh…
A revised model of fluid transport optimization in Physarum polycephalum
Optimization of fluid transport in the slime mold Physarum polycephalum has been the subject of several modeling efforts in recent literature. Existing models assume that the tube adaptation mechanism in P. polycephalum'…
valid