Distributed Swarm Trajectory Optimization for Formation Flight in Dense Environments
This addresses the challenge of formation flight in cluttered settings for aerial swarm applications, representing an incremental improvement by integrating formation preservation with obstacle avoidance.
The paper tackles the problem of enabling aerial swarms to navigate in prescribed formations while avoiding obstacles in dense environments, proposing an optimization-based method that achieves collision-free trajectory generation and demonstrates efficiency and robustness in real-world experiments.
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.