Intercept angle guidance based motion planning

In this paper we address the problem of minimum control effort intercept angle guidance in a complex environment with obstacles. We utilize the Rapidly Exploring Random Trees (RRT) approach that samples random vertices in the configuration space and connects them in a tree-like graph. For creating a minimum control effort path between the initial and goal states we use the RRT∗ variation, ensuring asymptotical optimality, and propose utilizing an optimal guidance law with terminal angle constraint to create paths between sampled points. To expedite the convergence of the search to the optimal solution we also introduce an easy to calculate lower bound for each new branch of the tree. The efficiency of the algorithm is demonstrated through simulations.