Chaos Detection and Mitigation in Swarm of Drones using Machine Learning Techniques and Chaotic Attractors

Abstract

Most existing identification and tackling of chaos in swarm drone missions focus on single drone scenarios. There is a need to assess the status of a system with multiple drones, hence, this research presents an on-the-fly chaotic behavior detection model for large numbers of flying drones using machine learning techniques. A succession of three Artificial Intelligence knowledge discovery procedures, Logistic Regression (LR), Convolutional Neural Network (CNN), Gaussian Mixture Models (GMMs) and Expectation–Maximization (EM) were employed to reduce the dimension of the actual data of the swarm of drone’s flight and classify it as non-chaotic and chaotic. A one-dimensional, multi-layer perceptive, deep neural network-based classification system was also used to collect the related characteristics and distinguish between chaotic and non-chaotic conditions. The Rössler system was then employed to deal with such chaotic conditions. Validation of the proposed chaotic detection and mitigation technique was performed using real-world flight test data, demonstrating its viability for real-time implementation. The results demonstrated that swarm mobility horizon-based monitoring is a viable solution for real-time monitoring of a system's chaos with a significantly reduced commotion effect. The proposed technique has been tested to improve the performance of fully autonomous drone swarm flights.