Time and Energy Optimized Trajectory Generation for Multi-Agent Constellation Changes

Planning the simultaneous movement of multiple agents represents a challenging coordination problem, and ideally safety and efficiency are jointly addressed. This paper introduces a planning algorithm for fast and energy-efficient trajectories with reduced collision potential from a start to an end constellation. This new approach combines trajectory approximation based on model predictive control, collision avoidance with potential fields, and flight energy optimization with minimum snap trajectories. Our approach results in unprecedented transition times and success rates with less energy consumption, as shown in simulation and real experiments with 16 drones.