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.

Self-Organizing Aerial Swarm Robotics for Resilient Load Transportation : A Table-Mechanics-Inspired Approach

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.

An Image Based Visual Servo Method for Probe-and-Drogue Autonomous Aerial Refueling

With the high focus on autonomous aerial refueling recently, it becomes increasingly urgent to design efficient methods or algorithms to solve AAR problems in complicated aerial environments. Apart from the complex aerodynamic disturbance, another problem is the pose estimation error caused by the camera calibration error, installation error, or 3D object modeling error, which may not satisfy the highly accurate docking. The main objective of the effort described in this paper is the implementation of an image-based visual servo control method, which contains the establishment of an image-based visual servo model involving the receiver's dynamics and the design of the corresponding controller. Simulation results indicate that the proposed method can make the system dock successfully under complicated conditions and improve the robustness against pose estimation error.