An increased level of autonomy is required for future Unmanned Aerial Vehicle (UAV) missions. One of the technologies required for this to occur is an adequate sense and avoid system. A sense and avoid system ensures that the UAV can detect threat aircraft and take evasive action if required. This thesis investigates a collision avoidance system to satisfy a significant portion of the requirements for sense and avoid.
An extensive literature review was performed and comparisons were made. It was hypothesised that a recently published method of UAV guidance, Specific Acceleration Matching (SAM) Control, could address the shortcomings of the current implementations. Additionally, a novel algorithm, the Linear 3D Velocity Guidance Control Algorithm (3DVGC) was developed to address the particular requirements of UAV collision avoidance. The SAM Controllers, 3DVGC and a conventional Collision Cone algorithm were integrated into the first Collision Avoidance System using Specific Acceleration Matching (CASSAM V.1). This system was tested in simulation and although all threats were evaded, this was done by rapid diving manoeuvres which were deemed dangerous.
Further investigation revealed that the guidance problem was in fact nonlinear. Subsequently, a new Nonlinear 3DVGC was developed. Furthermore, the conventional Collision Cone algorithm was extended to a new algorithm, the Projected Collision Avoidance Algorithm (PCCA). The PCCA, SAM Controllers and Nonlinear 3DVGC were integrated into the second version of the system, CASSAM V.2. Specific test scenarios were simulated as well as Monte Carlo simulation being performed. The simulation results were analysed and were broadly supportive of the viability of this architecture for use in future sense and avoid systems.