Experimental demonstration of an online trajectory optimization scheme using approximate spatial value functions

Receding Horizon (RH) control is an established control methodology which has been used successfully for many control applications. More recently it has been applied for autonomous vehicle guidance. Its successful implementation, in particular for applications involving agile vehicles like rotorcraft, hinges on two critical factors: (1) adequately accounting for the vehicle dynamics to guarantee that the trajectory is feasible and also that the capabilities of the vehicle are fully exploited; (2) using an appropriate cost-to-go (CTG) function to account for the discarded tail of the trajectory. In this paper we describe the experimental evaluation of a RH trajectory optimization scheme with a CTG function which approximates the value function associated with the minimum time optimal trajectory planning problem. The paper describes how the CTG function is computed; how the system is integrated; and finally describes the experimental demonstration of the guidance scheme. The experiments were performed in our Interactive Guidance and Control Laboratory which combines state of the art software architecture with a customized miniature helicopter.