Indoor formation motion planning using B-splines parametrization and evolutionary optimization

Formation generation with connectivity maintenance under efficiency restrictions for a group of autonomous vehicles is a challenging problem. By planning trajectories offline, the vehicles can follow optimized paths, resulting in improved efficiency in terms of time, energy, and resource utilization. This paper introduces a coherent approach that leverages evolutionary computing, notably a differential evolutionary algorithm, along with B-spline parametrizations, to effectively coordinate multiple indoor nanodrones. Off-line trajectories for both the leader and followers are designed to enforce multiple constraints (i.e., position, velocity, angles, thrust, angular velocity, waypoint passing, obstacle avoidance). The proposed approach accommodates intricate maneuvers such as formation switching and obstacle avoidance, facilitated by a knot refinement procedure that minimizes conservatism in constraint enforcement. The theoretical results are validated in both simulation and experiments.