Decentralized Spatial-Temporal Trajectory Planning for Multicopter Swarms
This addresses the challenge of flexible and robust trajectory planning for multicopter swarms in cluttered environments, representing an incremental improvement with careful engineering.
The paper tackles efficient spatial-temporal trajectory planning for decentralized multicopter swarms, achieving high-quality local planning with millisecond-level optimization and validating applicability through extensive benchmarks and experiments.
Multicopter swarms with decentralized structure possess the nature of flexibility and robustness, while efficient spatial-temporal trajectory planning still remains a challenge. This report introduces decentralized spatial-temporal trajectory planning, which puts a well-formed trajectory representation named MINCO into multi-agent scenarios. Our method ensures high-quality local planning for each agent subject to any constraint from either the coordination of the swarm or safety requirements in cluttered environments. Then, the local trajectory generation is formulated as an unconstrained optimization problem that is efficiently solved in milliseconds. Moreover, a decentralized asynchronous mechanism is designed to trigger the local planning for each agent. A systematic solution is presented with detailed descriptions of careful engineering considerations. Extensive benchmarks and indoor/outdoor experiments validate its wide applicability and high quality. Our software will be released for the reference of the community.