Methods from machine learning have successfully been used to improve the performance of control systems in cases when accurate models of the system or the environment are not available. These methods require the use of data generated from physical trials. Transfer Learning (TL) allows for this data to come from a different, similar system. This paper studies a simplified TL scenario with the goal of understanding in which cases a simple, alignment-based transfer of data is possible and beneficial. Two linear, time-invariant (LTI), single-input, single-output systems are tasked to follow the same reference signal. A scalar, LTI transformation is applied to the output from a source system to align with the output from a target system. An upper bound on the 2-norm of the transformation error is derived for a large set of reference signals and is minimized with respect to the transformation scalar. Analysis shows that the minimized error bound is reduced for systems with poles that lie close to each other (that is, for systems with similar response times). This criterion is relaxed for systems with poles that have a larger negative real part (that is, for stable systems with fast response), meaning that poles can be further apart for the same minimized error bound. Additionally, numerical results show that using the reference signal as input to the transformation reduces the minimized bound further.

Much of the mission autonomy development before and since these reports has focused on robotic autonomy, the onboard processing of raw or low-level data products that enables a spacecraft and/or flight instrument(s) to proceed safely and efficiently with mission objectives using minimal human interaction [1]. We use the term non-robotic science autonomy to refer to the ability of a science instrument to analyze its own data in order to calibrate itself, optimize operational parameters based on real-time findings, and ultimately make mission-level decisions based on scientific observations and determine which data products to prioritize and send back first. Science autonomy also includes data processing software that could be used for rapid data interpretation by scientists. Four of the Planetary Mission Concept Studies (PMCS) in preparation for this Decadal Survey, as well as the Europa Lander concept, target planetary environments from which communications are limited in data link rates and/or in time, including outer solar system targets, subsurface oceans, and hot and/or highly irradiated surfaces. Indeed, 4 of these mission concepts, including the PMCS Mercury lander, Intrepid moon rover, and Venus Flagship, as well as the Europa Lander concept describe needs for autonomy. Beyond the coming decade, future submarine missions beneath ice shells of ocean worlds would only be enabled by the ability to operate and make decisions autonomously.There are two broad categories of non-robotic science autonomy: flight instrument autonomy and data interpretation autonomy. Flight instrument autonomy deals with an instrument's ability to autonomously collect and selectively transmit data to Earth. Instruments capable of autonomous data collection, both robotically and in terms of decision-making (what samples to analyze, when, for how long, and fidelity of transmitted data) would, for example, greatly enhance the science return for missions in extreme environments, and are being planned for e.g., the proposed Europa Lander mission [2]. Autonomy in terms of data transmission would address the Key Points 1) Future planetary missions, especially those to the outer solar system, face significant challenges to increase sampling, reduce uncertainties, and manage and transmit increasing data volumes with limited data link rates. 2) Autonomous platforms enable instruments to perform inter-calibrations, sample validation, and discriminate data transmission, which reduces measurement uncertainties for optimal science return. 3) Science autonomy is necessary to achieve science goals for planetary missions under extreme conditions, short mission timeframes, and long delays in communication, and is specifically discussed in 3 of the Planetary Mission Concept Studies informing this Decadal Survey and the proposed Europa Lander. 4) Science autonomy will enable missions that are otherwise not possible, such as a sub-ice shell ocean submersible or a Venus lander. 5) Science autonomy has already enhanced science return for the Mars Science L...

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<p> </p><p><strong>Introduction</strong></p><p>In almost every planetary surface investigation, the characterization from a camera is a common initial step [1]. Mission Control is developing a science autonomy system called Autonomous Soil Assessment System: Contextualizing Rocks, Anomalies and Terrains in Exploratory Robotic Science (ASAS-CRATERS). It can enable automated surface characterization on planetary missions, which can benefit a wide range of science investigations and rover navigation alike. It can perform terrain classification and novelty detection using convolutional neural networks, and data aggregation to produce relevant data products for supporting science operations. Built on cutting-edge algorithms and off-the-shelf computing components, it offers low-cost ways to speed up tactical decision-making in next-generation commercial lunar missions.</p><p><strong>Background and Motivation</strong></p><p><em>Autonomy in Science Operations</em></p><p>Several factors are increasingly driving the need for autonomy in science operations. In traditional Mars rover operations, visual surface characterization and subsequent analysis and decision-making takes place in day-long tactical cycles [2]. Upcoming commercial lunar rover missions will have reduced latency, short lifetimes, and constrained bandwidth shared across several payloads. This will result in a need for rapid tactical decision-making processes with limited data, leaving little time for analysis, target identification,  and making decisions. Payload operators may not receive data in a timely fashion, or worse, may not receive some data at all. Autonomous onboard terrain classification offers a way to downlink light-weight data products and reduce the bottleneck in scientific terrain assessment. Autonomous classification and novelty detection increase the chances of detecting novel/sparse features (e.g., lunar outcrop or pyroclasts) that may otherwise be missed or not downlinked when driving and other mission needs are prioritized.</p><p><em>Application to Lunar Geology</em></p><p>While dedicated science instruments that reveal mineralogical and elemental composition improve our understanding of geological processes, a rover’s navigation sensors can document the morphology, morphometry, and composition of surface materials, regardless of primary investigation goals. High-resolution colour images and 3D data from stereo cameras provide information such as the size-frequency distribution and physical characteristics of craters and rocks, and regolith properties. All this offers valuable insight into the geologic setting. To provide a practical output as a science support tool for several types of missions, a classification scheme is being developed that segments a surface image into geological features that are visually distinct based on morphology, tone, and texture. This will be adapted for specific missions. See Figure 1 for a hand-labelled example.</p><p><img 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