Small unmanned aerial vehicles (UAVs) have generally little capacity to carry payloads. Through collaboration, the UAVs can increase their joint payload capacity and carry more significant loads. For maximum flexibility to dynamic and unstructured environments and task demands, we propose a fully decentralized control infrastructure based on a swarm-specific scripting language, Buzz. In this paper, we describe the control infrastructure and use it to compare two algorithms for collaborative transport: field potentials and spring-damper. We test the performance of our approach with a fleet of micro-UAVs, demonstrating the potential of decentralized control for collaborative transport.
@cite_5: We present a cooperative aerial robot system for the transportation of hoses. The hose–robot attachment makes the whole system physically interconnected but not rigid, so that control design becomes a difficult nonlinear optimization problem. The hose in quasistationary state can be modeled by sections of catenary curves. We use proportional integral derivative PID controllers for both quadrotor attitude and trajectory control, tuned by particle swarm optimization PSO. In this work we test PSO minimizing an energy function to achieve the PID controller tuning for horizontal motion of quadrotor teams transporting hoses under different stress conditions.
@cite_13: This paper presents a method for enabling a fleet of circularly arranged quadrocopters to throw and catch balls with a net. Based on a first-principles model of the net forces, nominal inputs for all involved vehicles are derived for arbitrary target trajectories of the net. Two algorithms that generate open-loop trajectories for throwing and catching a ball are also introduced. A set of throws and catches is demonstrated in the ETH Zurich Flying Machine Arena testbed.
@cite_2: 
The transport of DLOs has been recently studied @cite_5 . The feasibility of this task, with three UAVs, was demonstrated in simulation using a particle swarm optimization to get the proper set of PID gains before flight. This work, as with most of the literature of flexible payload transport with UAVs, adopted a centralized controller. Similarly, the impressive body of work of D'Andrea's group at ETH Zurich includes works such three UAVs throwing and catching a ball in a net @cite_13 and the transport of a flexible ring with six UAVs @cite_2 ; but they rely on a fully centralized and latency-free state estimator and controller.