Collaborative Multirotor UAVs Subject to Disturbances for Precise Maneuvers

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The Aeronautics industry is undergoing significant changes with an increase in the interest in unmanned aerial vehicles (UAVs), which is creating significant positive social and economic effects. One of the five key priorities of Transport Canada's Drone Strategy for 2025 is to develop strategies to enable cutting-edge (remotely piloted) drone technologies to prepare for international markets by supporting the growth of the drone sector in Canada [11]. The next generations of UAVs for the market are Autonomous UAVs that are still in the research phase. An autonomous UAV, without the intelligence of a human pilot, must have a robust and trustworthy system to perform critical missions, especially in challenging crowded urban environments or critical military missions under different wind gusts and communication errors. Despite developments, there is no robust system for mass deployment of autonomous UAVs in real scenarios. This research program will contribute to the modelling, estimation, control and path planning of collaborative, highly maneuverable multi-rotor UAVs for autonomous exploration of unknown environments subject to a variety of disturbances. External disturbances, including effects of adjacent multi-rotor UAVs; flying near walls, ceilings, or the ground; and wind gusts can deteriorate the performance of multi-rotor UAVs substantially, which may result in complete failure. Current aerodynamics models cannot predict aerodynamics effects for different possible configurations of multi-rotor UAVs in real-time on onboard computing modules. Thus, in this program, the advances in artificial intelligence (AI) and machine learning (ML) will be used to enhance the physics models to cover the complex unknown physics, especially aerodynamics interactions. However, current AI/ML techniques are not perfectly suitable for the aerospace industry, and they need to be improved and adjusted since no risk is tolerated in this industry. Artificial Neural Networks (ANN) are one of the most powerful tools of AI/ML. While there are huge advances in algorithms based on ANN, there is no appropriate ANN algorithm on the non-Euclidean manifold to train highly maneuverable UAVs without simplifying their dynamics. This program is committed to the development of ANN for UAVs able to perform precise maneuvers that require large and fast rotations; this takes into account the real dynamics of UAVs and results in more accurate motion planning and trajectory tracking. The ANN will be developed for non-Eucleadian spaces to augment the physics-based model of the UAV to enable it to autonomously follow the desired path subject to various external disturbances and to generate more efficient paths through cluttered environments. The developed ANN for non-Eucleadian spaces will benefit underwater vehicle control, geometric computer vision, computer animation and humanoid robots, as well.