Book excerpt: Rendezvous and docking missions have been a mainstay of space exploration from the Apollo program through present day operations with the International Space Station. There remains a growing interest in several mission types that not only rely on rendezvous and docking, but also rely on maneuvering spacecraft once docked. For example, there is active interest in orbital debris removal, on-orbit assembly, on-orbit refueling, and on-orbit servicing and repair missions. As these missions become more and more popular, the number of rendezvous and docking class operations will increase dramatically. Current methods focus on performing rendezvous and docking to very well-known targets and in very well-known conditions. Inherent to these new mission types, however, is an increasing element of uncertainty to which new guidance and control architectures will need to be robust. As guidance and control techniques become more robust, a corresponding tradeoff in performance can typically be experienced. This thesis attempts to address the uncertainties in rendezvous and docking operations while maintaining a probabilistically optimal level of performance. There are two main focuses in the thesis: spacecraft trajectory optimization and reference-tracking controller selection. With respect to trajectory optimization, the goal is to nd probabilistically optimal trajectories given large uncertainties in mission critical parameters, such as knowledge of an obstacle's position, while knowing that the trajectory is able to be replanned onboard the spacecraft when higher precision information is obtained. This baseline optimal trajectory and subsequently replanned trajectories are then followed by an optimally determined set of reference-tracking controllers. These controllers are selected and scheduled throughout the phases of the mission based on the probabilistically expected performance in the presence of noise and uncertain parameters. This process is explored through its implementation on a generic problem setup for rendezvous, docking, and joint maneuvering. Results specfic to this problem and associated analysis motivate the use of probabilistic planning in future space missions. Specically, the thesis shows that fuel and tracking performance can be improved if multi-stage missions are planned continuously through phase transitions and without the use of waypoints. Furthermore, under the presence of large uncertainties, the techniques in this thesis produce better expected fuel and tracking performance than traditional trajectory planning and controller selection methods.