Cooperative Transport of Large Objects by a Pair of Unmanned Aerial Systems using Sampling-based Motion Planning

Cooperative carrying of large, cable-suspended payloads by a pair of cooperating unmanned aerial vehicles (UAVs) is tackled in this paper. The proposed system, involving a sampling-based motion planning algorithm and a model predictive control-based coordination of UAVs, aims to achieve a smooth and reliable flight performance in environments with obstacles. The motion planning is designed to satisfy constraints on relative positions of UAVs, which are defined from the cooperative transport task and by onboard mutual localization, which is used for real-time estimation of states of neighboring robots carrying the object. Besides, a guiding principle with a cost-driven expansion is employed to steer the growth of a Rapidly-exploring Random Tree (RRT) to keep the coupled system of UAVs and the object as close to desired mutual positions as possible. A significant deviation of the controlled system from the desired configuration, by increasing or decreasing the relative distance between UAVs carrying the cable-suspended object, is achieved only if it is required by environment constraints (e.g. in narrow passages), while the allowed limits are always satisfied. Using the guiding principle enables us to find feasible solutions of the problem in a reasonable short time using onboard computer even in environments with a complicated structure of obstacles. The proposed system was evaluated in numerous simulations, compared with state-of-the-art solutions using statistical sets of results, and its performance and reliability were verified in experiments in real-world conditions.