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 t he 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.
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