Receding horizon trajectory optimization with a finite-state value function approximation

This paper describes a finite-horizon receding horizon trajectory optimization scheme which uses an approximation of the value function to provide cost-to-go (CTG) and associated state information. The value function approximation is computed using a finite-state, motion primitive automaton approximation of the vehicle dynamics. Using an actual value function approximation instead of heuristic CTG allows a tighter integration between the planning and control layers needed for vehicles operating in challenging spatial environments. It also enables a more rigorous use of the receding horizon control framework for autonomous control applications. The paper describes the finite-state value function approximation and its integration into the receding horizon scheme. Simulation examples illustrate the scheme´s capabilities and highlight interesting open issues that will need to be addressed to take full advantage of the approach.