Controlling Draft Interactions Between Quadcopter Unmanned Aerial Vehicles with Physics-aware Modeling
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Controlling Draft Interactions Between Quadcopter Unmanned Aerial Vehicles with Physics-aware Modeling Ion Matei1 · Chen Zeng2 · Souma Chowdhury2
· Rahul Rai2 · Johan de Kleer1
Received: 10 July 2020 / Accepted: 1 December 2020 © Springer Nature B.V. 2020
Abstract In this paper, we address the problem of multiple quadcopter control, where the quadcopters maneuver in close proximity resulting in interference due to air-drafts. We use sparse experimental data to estimate the interference area between palm sized quadcopters and to derive physics-infused models that describe how the air-draft generated by two quadcopters (flying one above the other) affect each other. The observed significant altitude deviations due to airdraft interactions, mainly in the lower quadcopter, is adequately captured by our physics infused machine learning model. We use two strategies to mitigate these effects. First, we propose non-invasive, online and offline trajectory re-planning strategies that allow avoiding the interference zone while reducing the deviations from desired minimum snap trajectories. Second, we propose invasive strategies that re-design control algorithms by incorporating the interference model. We demonstrate how to modify the standard quadcopter PID controller, and how to formulate a model predictive control approach when considering the interference model. Both invasive and non-invasive strategies show significant reduction in tracking error and control signal energy as compared to the case where the interference area is ignored. Keywords Air draft interactions · Model predictive control · Physics-infused machine learning · Trajectory planning · Unmanned Aerial Vehicle (UAV)
1 Introduction
1.1 Aerodynamic Interactions Between UAVs
With growing popularity of unmanned aerial vehicles (UAVs), as more UAVs start operating in close proximity of each other it becomes important to consider potential interactions between UAVs in the context of control and operations planning. In this paper, we present new approaches to model air draft interactions among quadcopter UAVs with physics-infused machine learning, followed by trajectory planning and control solutions to mitigate detrimental impacts (e.g., on stability) of such interactions. The remainder of this introduction section motivates the need for these solutions, briefly surveys existing approaches w.r.t. modeling and regulating air-draft interactions between UAVs, and converges on the objectives of this paper.
Multirotor UAVs are seeing expanding applications in various civilian, commercial and humanitarian domains [19]. Chief among these emerging applications are close coordinated operation of multiple UAVs, namely UAV swarm and formation flight paradigms. Such paradigms involve a team of small UAVs collaborating in a certain manner to offer sensing, transportation, monitoring and other related solutions with increased redundancy and effectiveness [7, 40]) compared to sophisticated standalone alternatives. In such multi-UAV or shared airspace applications, UAVs of
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