Learning optimal guidance behavior in unknown environments within receding horizon planning

Aerospace Engineering and Mechanics, University of Minnesota Minneapolis, Minnesota, USA This paper describes a guidance algorithm for autonomous operation in partially known environments. The emphasis of the paper is enabling learning within a receding horizon trajectory optimization framework. The information acquired from an exteroceptive sensor is assimilated into a spatial value function. This setup has the advantage that the system learns information directly relevant to optimal guidance and control behavior and enables efficient trajectory-planning in unknown or partially known environments. The system's performance is demonstrated using successive runs in high-fidelity indoor simulations.