We present a distributed model predictive control (DMPC) algorithm to generate trajectories in real-time for multiple robots. We adopted the on-demand collision avoidance method presented in previous work to efficiently compute noncolliding trajectories in transition tasks. An event-triggered replanning strategy is proposed to account for disturbances in the system. Our simulation results show that the proposed collision avoidance method can reduce, on average, around 50% of the travel time required to complete a multi-agent point-topoint transition when compared to the well-studied Buffered Voronoi Cells (BVC) approach. Additionally, it shows a higher success rate in transition tasks with a high density of agents, with more than 90% success rate with 30 palm-sized quadrotor agents in a 18 m 3 arena. The approach was experimentally validated with a swarm of up to 20 drones flying in close proximity.

This paper introduces a novel algorithm for multiagent offline trajectory generation based on distributed model predictive control (DMPC). Central to the algorithm's scalability and success is the development of an on-demand collision avoidance strategy. By predicting future states and sharing this information with their neighbours, the agents are able to detect and avoid collisions while moving towards their goals. The proposed algorithm can be implemented in a distributed fashion and reduces the computation time by more than 85% compared to previous optimization approaches based on sequential convex programming (SCP), while only having a small impact on the optimality of the plans. The approach was validated both through extensive simulations and experimentally with teams of up to 25 quadrotors flying in confined indoor spaces.

This paper aims to design quadrotor swarm performances, where the swarm acts as an integrated, coordinated unit embodying moving and deforming objects. We divide the task of creating a choreography into three basic steps: designing swarm motion primitives, transitioning between those movements, and synchronizing the motion of the drones. The result is a flexible framework for designing choreographies comprised of a wide variety of motions. The motion primitives can be intuitively designed using a few parameters, providing a rich library for choreography design. Moreover, we combine and adapt existing goal assignment and trajectory generation algorithms to maximize the smoothness of the transitions between motion primitives. Finally, we propose a correction algorithm to compensate for motion delays and synchronize the motion of the drones to a desired periodic motion pattern. The proposed methodology was validated experimentally by generating and executing choreographies on a swarm of 25 quadrotors.