The book aims to present innovative algorithms to provide autonomous flight capability to a fixed-wing unmanned aircraft. Particularly it contributes to research on path optimization, trajectory tracking and collision avoidance with two algorithms designed respectively for path planning and aircraft guidance. These algorithms are combined to generate the shortest path from start to target avoiding known obstacles represented on a map and to drive the aircraft on the optimum path avoiding unpredicted objects sensed in flight. The path planning algorithm, named Kinematic A*, is developed on the basis of graph search algorithms like A* or Theta* and is meant to generate feasible paths through a simple aircraft kinematic-model, evaluating moving cost between nodes of tridimensional graphs. The guidance algorithm on the other hand faces the concurring tasks of trajectory tracking and collision avoidance with Nonlinear Model Predictive Control. It exploits the optimal path provided with KA* and a visual servoing control logics based on a spherical camera to steer the aircraft toward the desired path.

About the Author:

Is research assistant in the Department of Mechanics and Aerospace Engineering of Politecnico di Torino. He earned a PhD on Path Planning and Collision Avoidance Algorithms for UAVs. His research interests include control-system design, space dynamics and control, bio-inspired optimization, Human Supervising (HS) and Human Machine Interfaces (HMIs)

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