3D Fast Geometric Collision Avoidance Algorithm (FGA) and Decision-Making Approach based on the Balance of Safety and Cost for UAS

Abstract

Unmanned Aircraft System (UAS) is a fast-growing industry with extensive economic implications and would be integrated into the national airspace system (NAS), which requires UAS to have the efficient sense and avoidance capability. This thesis develops a fast geometry-based algorithm FGA which shortens the collision avoidance computation time, path length and balances the probability of safety and energy cost by calculating and giving UAS proper selective avoidance starting time tc, meaning the last possible point for the UAS to avoiding the potential threaten and itis based on the UAS kinematic, conflicts likelihood map, and navigation constraints. This operation enables the update path to be as close as possible to the UAVs resume designed path, decreasing the length of path variation and the corresponding time cost. In comparison to a current geometry method, the sampling-based method and the search algorithm, the FGA algorithm shows 40% to 90% of reduction in computational time and length of path for the same obstacle avoidance scenarios. Quantitative analysis of the efficiency by different avoiding trigger times is also provided.

FGA with critical avoidance time tc not only could improve the geometry methods, but also could be used for (1) research on the bounds of general geometry based collision avoidance, and (2) solving the multiple obstacles avoidance problem.For a scenarios with crowded obstacles which cannot be avoided at the same time, an applicable algorithm for obstacles classification is provided. It divides the obstacles into small groups with different urgent levels by their critical avoidance trigger time tc, and then avoids them in sequence. Simulation validates the efficiency of this application.

Extremely difficult obstacle avoidance such as the UAV working under maneuver limitation and the obstacles are time-variant are discussed and solved in the following chapters. Monte Carlo simulation, statistical method and Machine learning algorithms especially the supervised logistic learning methods are implemented later to analyze the weight of the factors such as sensor detection distance, ratio of the speed, number of obstacles, which have impacts on the geometric based obstacle avoidance methods. Finally, flight missions in an aircraft simulator and the hardware fixed-wing aircraft experiments validate the algorithm effectiveness with successful results.