Toward Information-Driven and Risk-Bounded Autonomy for Adaptive Science and Exploration

Abstract

While the primary purpose of robotic space exploration systems is to gather scientific data, it is equally important that engineering operations are performed and engineering constraints are respected in order to prolong the mission life and ensure the integrity of the observations taken. However, science and engineering operations are often at odds with each other as attempting to obtain the "best" data may violate engineering operations constraints and place the mission at risk. Historically, mission systems engineering has separated the process of planning for science from engineering operations, with the engineering operations constrained to support the science measurement plan with acceptable risk. This task division leads to multiple design iterations between the science and engineering operations which results in compromised, conservative operations that reduce science return and are more brittle than desired. To overcome these limitations, we present an approach for autonomous mission planning that explicitly models and reasons about the coupling between science and engineering operations, resulting in higher science return, while maintaining acceptable levels of risk. Our approach is to develop an information-driven, risk-bounded plan executive that is capable of producing missions satisfying the goals and constraints expressed in these programs. In this paper, we describe in detail the risk-bounded, information-driven execution problem and lay out the architecture used in our information-directed plan executive 'Enterprise'. We then show the performance of the current version of Enterprise on two space exploration scenarios. Finally, we conclude with thoughts on future work, including on the design of a proposed information-theoretic language that will allow operators and scientists to specify their objectives in terms of questions about scientific phenomena or the configuration of the space system. I. Nomenclature 𝒜= set of possible actions 𝑎 𝑡 = discrete action at time step 𝑡 ℬ = set of safe states 𝒞 = set of constraints that must be satisfied by returned plan

Information-Driven and Risk-Bounded Autonomy for Scientist Avatars

“…For more information on the unmanned underwater exploration scenario, including a demonstration in simulation, see Ref. [6].…”

“…Spock is a risk-bounded adaptive sampling planner that looks for science targets to maximize measured science information [3,6,19]. When given a state plan to expand with an information objective, Spock extracts the potential observations from the environment model and state estimate.…”

“…Kirk is an optimal activity planner that finds temporally feasible plans that minimize defined costs [2,6,28]. The Kirk activity planner is responsible for making discrete choices and ensuring temporal consistency within risk bounds.…”

“…We have previously described this project in Ref. [6]. In the remainder of this paper, we detail the progress we have made over the past year, which has been largely focused on formalizing the asteroid exploration problem, from both a science and autonomous planning perspective, and developing a simulated space systems testbed on which information-driven and risk-bounded autonomy frameworks can be tested.…”