Robotic operations in space are indispensable for many missions both in Earth orbit and beyond. Satellite servicing and refueling, space station resupply with consumables, removal of space debris, spacecraft structural integrity inspection, crew assistance, as well as support for deep space missions to Mars and other planets and comets, all require the assistance of highly accurate, reliable and autonomous (or semi-autonomous) space robots. To date, most robotic operations in space are performed in a closely supervised mode by a human operator. This limits both the flexibility and the type of missions that can be performed (for example, the time for light to travel to and from Mars takes about 15 minutes, making "real-time" remote control impossible). This research aims at developing the necessary theory and algorithms to be able to utilize active exploration using robust, reliable sensing and planning of a free-flying space robots in the vicinity of another body, in order to perform proximity operations (including autonomous rendezvous and docking in space). One of the challenges in these types of problems is the uncertainty in understanding the surroundings in order to plan suitable control actions. In order to handle these challenges we utilize novel tools and methodologies from the field of stochastic optimal control along with new advances describing the spacecraft attitude dynamics and kinematics of spacecraft in orbit. In order to ensure that the algorithms we develop perform in real-life as expected, the theoretical results will be experimentally validated on a high-fidelity 5-dof spacecraft simulator facility. This work will have an immediate impact on the US capabilities to perform monitoring and servicing of satellites in space routinely, by advancing the state-of-the-art in perception and path-planning of orbiting spacecraft in the vicinity of another body, man-made or natural. Although the emphasis of this work is primarily on space robotic applications, the same techniques can be used in all similar problems where an intelligent agent needs to navigate autonomously in an uncertain and dynamic environment.
The proposed research tackles a fundamental problem in autonomous/robotic systems, namely, the integrated sensing and planning under uncertainty. The current paradigm in the literature utilizes perceptual cues (especially those based solely on visual information) essentially as surrogates of full-state feedback estimators, thus enforcing an artificial separation of perception and control action. This dichotomy between sensory data acquisition/processing, and control/actuation strategies - deeply rooted in the community from its wide applicability to the stabilization of linear systems subject to additive noise (?separation principle?) - is unsuitable for this problem, where information gathering (perception/sensing) is tightly coupled with motion (control). To overcome the aforementioned limitations, in this work it is proposed to use tools from stochastic optimal control in order to extract actionable information from raw sensory inputs. A key ingredient of the proposed approach is to keep track of the first and second order statistics of the estimation error and treat them as the state, so that control actions depend on both of them. The result is a new, computationally more efficient, methodology to maximize information gathering during the exploration phase and to optimize over distributions of trajectories during the execution phase.
