Robust randomized trajectory planning for satellite attitude tracking control

Abstract

This thesis presents a novel guidance strategy that uses a randomized trajectory planning algorithm in a closed-loop fashion to provide robust motion planning and execution. By closing the guidance, navigation, and control loop around a randomized trajectory planning algorithm, a robotic vehicle can autonomously maneuver through a field of moving obstacles in a robust manner. The guidance strategy provides executable plans that are robust to known error sources when supplied with an estimate of the initial state, the goal, the predicted locations of obstacles, and bounds on error sources affecting the execution of a planned trajectory. The planning function presented in this thesis extends the Rapidly-exploring Random Tree algorithm to dynamic environments by exploring the configuration- x-time space using a node selection metric based on the maneuvering capability of the vehicle. The guidance strategy and the new randomized trajectory planning algorithm are applied to a challenging satellite attitude guidance problem in simulation.