Predictive control of aerial swarms in cluttered environments

Enrica Soria,Fabrizio Schiano,Dario Floreano

doi:10.1038/s42256-021-00341-y

Enrica Soria, Fabrizio Schiano + Show 1 more

Open Access

https://doi.org/10.1038/s42256-021-00341-y

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Abstract

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.

Highlights

From the fluid wavelike movements of starling flocks to the swift turning maneuvers of bee swarms, nature displays many examples of coordinated flight [1]–[7]
The results show that the inter-agent distance error is smaller with Nonlinear Model Predictive Control (NMPC) swarms (Ed = 0.11 ± 0.02) than with Potential Fields (PFs) swarms (Ed = 0.27 ± 0.12), and the inter-agent distance range is shorter for NMPC swarms (Rd = 0.56 ± 0.18) than with PF swarms
This article shows that a Nonlinear Model Predictive Control (NMPC) model achieves a faster and more synchronized flight in cluttered environments as compared to state-of-the-art models based on potential fields (PFs)

Summary

Introduction

From the fluid wavelike movements of starling flocks to the swift turning maneuvers of bee swarms, nature displays many examples of coordinated flight [1]–[7]. Recent progress in aerial robotics technologies led to the availability of smart drones at the price of smartphones [8], but the deployment of drone swarms that autonomously coordinate their local trajectories remains a challenge. Autonomous aerial swarms can enable functionalities that are beyond the capabilities of a single drone, such as cooperative transportation of large objects and aerial construction [12], [13]. The coordinated, synchronized motion of biological swarms is a self-organized behavior that emerges from local information[4]–[6], [17]–[19], and can cope with unforeseen situations, such as flying through forests or in urban canyons

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