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
We present an overview of an information-seeking risk-bounded planning and execution system to enable future spacecraft to make decisions and adapt their behavior to seek out the most high-value scientific information, while bounding risk of failure. Information-seeking autonomy is an innovation with potential for transformational impact on the way our spacecraft enable scientific discoveries, by complementing traditional scientist-in-the-loop operations with appropriately-conservative onboard direction of science measurement activities (based on scientist-specified models). Our executive selects a measurement strategy that maximizes information based on the environment and initial measurements. It takes various mission risks into account when planning and executing activities to deal with uncertainties and disturbances. It can adapt its strategy based on collected measurements onthe-fly. Finally, it demonstrates resilience, despite failures and degradations, to achieve its high-level scientific objectives. We have performed an initial demonstration of the capabilities of our executive against a basic spacecraft simulation of an asteroid flyby scenario, leveraging the Robot Operating System and the Basilisk open-source simulation framework. It has also been demonstrated on autonomous underwater vehicle scenarios.
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