Cooperative UAV Search and Intercept
Andrew Sun · TSpace (University of Toronto) · 2009
In this thesis, a solution to the multi-Unmanned Aerial Vehicle (UAV) search and intercept problem for a moving target is presented. For the search phase, an adapted diffusion-based algorithm is used to manage the target uncertainty while individual UAVs are controlled with a hybrid receding horizon / potential method. The coordinated search is made possible by an uncertainty weighting process. The team intercept phase algorithm is a behavioural approach based on the analytical solution of Isaac's Single-Pursuer/Single-Evader (SPSE) homicidal chau ffeur problem. In this formulation, the intercepting control is taken to be a linear combination of the individual SPSE controls that would exist for each of the evader/pursuer pairs. A particle swarm optimizer is applied to find approximate optimal weighting coefficients for discretized intervals of the game time. Simulations for the team search, team intercept and combined search and intercept problem are presented.