Improved Autonomy for Unmanned Aerial Vehicles in Unstructured Environments

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Global spending on unmanned aerial vehicles (UAV) is predicted to grow dramatically from USD 4 billion in 2015 to USD 14 billion by 2025, which is a total of USD 93 billion in a decade (source: Teal Group). Military research is expected to add USD 30 Billion in spending over this decade. This remarkable growth is because UAVs extend and complement human capability to do difficult jobs faster, more accurately, safer, and at a reduced budget. Civilian applications include disaster recovery in regions where humans cannot survive, or inspection of infrastructure such as electrical transmission lines covering vast expanses of inaccessible land. UAVs are part of a robotics revolution in the military where they are often used for intelligence, surveillance, and reconnaissance (ISR). They are key to the future of military forces worldwide, and in particular to that of Canada's Air Force as mentioned in strategic planning documents such as “Projecting Power Trends Shaping Canada's Air Force in the Year 2019” by the Canadian Forces Air Warfare Center.*******The aim of the proposed research is to improve UAV autonomy in unstructured environments; the work builds on the applicant's past successful UAV research program begun in 2004. We define autonomy broadly as capabilities achieved by a man-machine team where the UAV operates with some level of independence. Autonomous control for known environments (e.g., a mapped warehouse with beacons) cannot be applied to unstructured environments. There, robots must perceive their environment to make intelligent decisions towards a mission goal. First, we investigate new methods for vision-based navigation in an environment where GPS is degraded (e.g., due to adversarial jamming). An equally important case is navigation relative to an object with unknown GPS coordinate (e.g., grasping a payload). Although vision-based navigation has been used for visual servoing, it can be unnecessarily computationally intensive as it relies on continuous tracking of features and the maintenance of a map. Therefore, a second objective researches efficient visual servoing for specific autonomous motion control tasks including landing on a moving ship deck. Thirdly, we analyze and compensate for time delay in UAV control. Image processing required in visual servoing and navigation or long-range remote teleoperation introduces significant latency which limits performance. Fourthly, we investigate multi-vehicle coordinated control in general and its application to slung load transportation.*******The proposed research is important as it develops novel broadly applicable theory in the field of nonlinear control, visual servoing, visual navigation, and robotics. This theory is demonstrated on specific applications of practical importance. The benefits to Canada include innovation in the large and growing field of unmanned systems which are transforming military and civil applications.******