Onboard Risk-Aware Real-Time Motion Planning Algorithms for Spacecraft Maneuvering

Unlocking the next generation of complex missions for autonomous spacecraft requires significant advances in robust motion planning. The aim of motion planning is to generate a sequence of control inputs which navigates a vehicle from a set of initial states to a specified goal region while minimizing cost (e.g. time and/or propellant) and avoiding obstacles, which may include mission constraints. In future missions, uncrewed spacecraft interacting with asteroids, comets, or distant moons or in proximity operations with debris or other spacecraft will need to plan feasible trajectories which satisfy the mission objectives with a guaranteed margin of safety. Therefore, motion planning systems must be equipped to navigate dynamic, cluttered, and highly constrained environments while actively assessing risk by accounting for uncertainty. In addition, trajectories must be computed in real-time to meet rigid convergence requirements under the tight computational constraints of space-qualified hardware. In support of these goals, this work provided a number of advances to the state-of-the-art. First, we developed a real-time motion planning algorithm for free-floating manipulation of a large payload of significant mass, using the Astrobee robot and its robotic arm and gripper as a model. Second, we developed catered real-time motion planning tool to robotic spacecraft servicing, applying it specifically to the Restore-L Satellite Servicing mission, which has two 7-DoF robotic arms for capturing and refueling a large satellite. Third, we investigated operations using controllable gecko-inspired adhesive grippers for capturing rocket body debris, a challenging robotic task where motion planning and trajectory optimization algorithms are likely to play a role. For such a task, the difficulty of motion planning and trajectory optimization may be greatly eased through use of a gecko-inspired adhesive gripper, rather than a more traditional gripper. Fourth, we developed an optimized approach for grasping a non-cooperative, tumbling object in microgravity using these gecko-inspired adhesives, showing that indeed a grasping problem can be simplified to a docking problem within a trajectory planner or optimizer through use of an adhesive gripper. Fifth, we developed a framework for robot trajectory optimization in standard Euclidean spaces, but also on manifolds, using sequential convex programming. For this work we provided strong theoretical guarantees for a wide range of problem formulations, as well as application to a free-flying spacecraft system. Sixth, we designed and constructed a new free-flying robotic spacecraft test bed using air bearings on a granite table, outfitting a fleet of four free-flying robots. And finally, we used the experience gained over the course of this project to make contributions to geometrically-consistent reactive control on robotic task manifolds, which builds the geometric Page 2 – Revised 02/2018 structure of a robot and its tasks into its control in a way that leads to fast generation of natural motions. Such capability may be a key feature of future space robots planning, executing, and updating trajectories in complex environments.