# Decentralized Spatial-Temporal Trajectory Planning for Multicopter Swarms

arXiv: https://arxiv.org/abs/2106.12481 · HTML: https://mlsanity.com/paper/2106.12481/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2021.

## Built on by (most cited first, 1 of 1)

- [SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification](https://mlsanity.com/paper/2308.07874/) (2023)

## Abstract (arXiv)

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.