Robust Autonomous Landing of Multirotor Unmanned Aerial Vehicles on Static and Moving Platforms

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Autonomous drones are becoming more and more popular in various industry sectors for their flexibility andfast deployment and have demonstrated usefulness in applications such as search and rescue, inspection, andmapping. Our new research direction in autonomous drone operations is robust autonomous landing on staticand moving platforms in wind to enable the integration of drones with ground vehicles (trucks) or ships. Thiswill lead to scientific breakthroughs and practical innovations in delivery services, agriculture, search andrescue, inspections, border control, and maritime operations.Presently, drones do not have the capability to land repeatably in a small-enough landing zone to enable robusttruck- or ship-based operations. This project's goal is to develop new modeling, state estimation, and controlmethods for the precise and consistent landing of drones particularly in wind, ultimately enabling automatedlanding on moving platforms such as truck cargo beds and ships.This project consists of four research pillars: (1) aerodynamic modeling to develop accurate mathematicalmodels of multi-rotor drone flight, especially in turbulent wind conditions, (2) state estimation to enableestimation of wind effects on the drone during flight, (3) motion planning to automatically find the optimalpath to land on a moving platform while considering wind effects and arbitrary motion of the landing platform,and (4) flight control to ensure drones' fast response to wind conditions and motion of landing platform, andclose following of the optimal landing path for successful drone landing.The effectiveness of the algorithms will be evaluated through real-world flight experiments. Thisproject will help Canada to maintain its key role in this sector and will lead to innovation in short-hop delivery.