Learning Optimal Guidance Behavior in Unknown Environments within Receding Horizon Planning

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.