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Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning

A neural network-based fault detection system and reinforcement learning controllers enable stable quadcopter operation with varying numbers of functional propellers.

Year
2020
Venue
arXiv 2020
Authors
3
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arxiv.org/abs/2002.11564v2ARXIV-DEFAULT
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Abstract

Quadcopters can suffer from loss of propellers in mid-flight, thus requiring a need to have a system that detects single and multiple propeller failures and an adaptive controller that stabilizes the propeller-deficient quadcopter. This paper presents reinforcement learning based controllers for quadcopters with 4, 3, and 2 (opposing) functional propellers. The paper also proposes a neural network based propeller fault detection system to detect propeller loss and switch to the appropriate controller. The simulation results demonstrate a stable quadcopter with efficient waypoint tracking for all controllers. The detection system is able to detect propeller failure in a short time and stabilize the quadcopter.

Authors

3