Predictive control of aerial swarms in cluttered environments

Classical models of aerial swarms often describe global coordinated motion as the combination of local interactions that happen at the individual level. Mathematically, these interactions are represented with potential fields. Despite their explanatory success, these models fail to guarantee rapid and safe collective motion when applied to aerial robotic swarms flying in cluttered environments of the real world, such as forests and urban areas. Moreover, these models necessitate a tight coupling with the deployment scenarios to induce consistent swarm behaviours. Here, we propose a predictive model that incorporates the local principles of potential field models in an objective function and optimizes those principles under the knowledge of the agents' dynamics and environment. We show that our approach improves the speed, order and safety of the swarm, it is independent of the environment layout and is scalable in the swarm speed and inter-agent distance. Our model is validated with a swarm of five quadrotors that can successfully navigate in a real-world indoor environment populated with obstacles. The movement of drone swarms can be coordinated using virtual potential fields to reach a global goal and avoid local collisions. Soria et al. propose here to extend potential fields with a predictive model that takes into account the agents' flight dynamics to improve the speed and safety of the swarm.

Automated summary

Classical models of aerial swarms, which use potential fields to describe interactions, have limitations in guaranteeing rapid and safe collective motion in real-world cluttered environments. A predictive model is proposed in this study to improve the speed and safety of aerial swarms by incorporating agents' flight dynamics.