Distributed Swarm Trajectory Optimization for Formation Flight in Dense Environments

TL;DR

This paper introduces a novel optimization-based trajectory planning method for aerial swarms that ensures collision-free formation flight in cluttered environments, combining formation preservation and obstacle avoidance.

Contribution

A new differentiable formation similarity metric and an integrated optimization framework for collision-free, formation-preserving trajectory planning in dense environments.

Findings

Outperforms existing methods in benchmark tests.

Demonstrates robustness in real-world obstacle-rich scenarios.

Provides publicly available source code for community use.

Abstract

For aerial swarms, navigation in a prescribed formation is widely practiced in various scenarios. However, the associated planning strategies typically lack the capability of avoiding obstacles in cluttered environments. To address this deficiency, we present an optimization-based method that ensures collision-free trajectory generation for formation flight. In this paper, a novel differentiable metric is proposed to quantify the overall similarity distance between formations. We then formulate this metric into an optimization framework, which achieves spatial-temporal planning using polynomial trajectories. Minimization over collision penalty is also incorporated into the framework, so that formation preservation and obstacle avoidance can be handled simultaneously. To validate the efficiency of our method, we conduct benchmark comparisons with other cutting-edge works. Integrated with an autonomous distributed aerial swarm system, the proposed method demonstrates its efficiency and robustness in real-world experiments with obstacle-rich surroundings. We will release the source code for the reference of the community.