Abstract
This article provides a theory for provably safe and computationally efficient distributed constrained control, and describes an application to a swarm of nano-quadrotors with limited on-board hardware and subject to multiple state and input constraints. We provide a formal extension of the explicit reference governor framework to address the case of distributed systems. The efficacy, robustness, and scalability of the proposed theory is demonstrated by an extensive experimental validation campaign and a comparative simulation study on single and multiple nano-quadrotors. The control strategy is implemented in real-time on-board palm-sized unmanned erial vehicles, and achieves safe swarm coordination without relying on any offline trajectory computations.
Highlights
Swarms of aerial robots or Unmanned Aerial Vehicles (UAVs) are emerging as a disruptive technology that enables highly re-configurable, on-demand, distributed intelligent autonomous systems with high impact on many areas of science, technology, and society (Chung et al, 2018).These swarms can be employed to solve real-world tasks where the environment is to be explored (Marconi et al, 2012; Bayram et al, 2017), and to be traversed or exploited (Vásárhelyi et al, 2018) with a prescribed goal state or a desired formation
We present the first results of an extensive experimental validation of the Explicit Reference Governor (ERG) and the Distributed Explicit Reference Governor (D-ERG) frameworks by means of single and multi-robot hardware experiments using the experimental setup described hereafter
8.5 Analysis of Safety and Goal Satisfaction Certificates. In this simulation study we show some relevant statistics on the occurrence of constraint violations or deadlocks and compare the D-ERG with another optimization-free approach solely based on attractive and repulsive
Summary
Swarms of aerial robots or Unmanned Aerial Vehicles (UAVs) are emerging as a disruptive technology that enables highly re-configurable, on-demand, distributed intelligent autonomous systems with high impact on many areas of science, technology, and society (Chung et al, 2018) These swarms can be employed to solve real-world tasks where the environment is to be explored (Marconi et al, 2012; Bayram et al, 2017), and to be traversed or exploited (Vásárhelyi et al, 2018) with a prescribed goal state or a desired formation. To ensure a high level of safety and robustness, the robots should use their on-board computational resources rather than relying on off-board resources (e.g. a ground control station) The latter provide a central point of failure, and are susceptible to time delays, communication overhead, and information loss. This calls for reactive and distributed control algorithms that can be implemented in real-time on-board UAVs and only rely on local information to solve the global navigation task safely
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