Trajectory Generation for Multiagent Point-To-Point Transitions via Distributed Model Predictive Control
This work addresses efficient trajectory planning for multiagent systems like drones in confined spaces, offering a scalable solution with significant speed improvements, though it is incremental in optimizing existing distributed control methods.
The paper tackles the problem of generating collision-free trajectories for multiple agents moving between points by introducing a distributed model predictive control algorithm with an on-demand collision avoidance strategy, reducing computation time by over 85% compared to prior methods while maintaining near-optimal plans, as validated with simulations and experiments involving up to 25 quadrotors.
This paper introduces a novel algorithm for multiagent offline trajectory generation based on distributed model predictive control. 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 neighbors, the agents are able to detect and avoid collisions while moving toward 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, 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.