Autonomous selection of optimal options for data collection from multiple alternatives is challenging in uncertain environments. When secondary information about options is accessible, such problems can be framed as contextual multi-armed bandits (CMABs). Neuro-inspired active inference has gained interest for its ability to balance exploration and exploitation using the expected free energy objective function. Unlike previous studies that showed the effectiveness of active inference based strategy for CMABs using synthetic data, this study aims to apply active inference to realistic scenarios, using a simulated mineralogical survey site selection problem. Hyperspectral data from AVIRIS-NG at Cuprite, Nevada, serves as contextual information for predicting outcome probabilities, while geologists' mineral labels represent outcomes. Monte Carlo simulations assess the robustness of active inference against changing expert preferences. Results show active inference requires fewer iterations than standard bandit approaches with real-world noisy and biased data, and performs better when outcome preferences vary online by adapting the selection strategy to align with expert shifts.

Maximizing the utility of limited Earth observing satellite resources is a difficult ongoing problem. Dynamic Targeting is an approach to this challenge that intelligently plans and executes primary sensor observations based on information from a look-ahead sensor. However, current implementations have failed to account for realistic satellite operational constraints and have used static utility for repeat observations of the same target. To address these limitations, we implement a more general Dynamic Targeting framework that comprises a physics-based slew model, a dynamic model of observation utility, and an algorithm for gathering high-utility observations. To demonstrate this framework, we also supply complex dynamic utility models that are applicable to many missions and new algorithms for intelligently scheduling observations with slewing restrictions and changing utility, including a greedy algorithm and a depth-first search algorithm. To evaluate these algorithms, we test their performance across simulated runs through two datasets and compare to the performance of an algorithm representative of most scheduling algorithms aboard Earth science missions today as well as an intractable upper bound. We show that our algorithms have great potential to improve science return from Earth science missions.

Dynamic targeting (DT) is an emerging concept for improving science yield on Earth-observing missions limited by power-constrained sensors. DT uses a lookahead sensor together with on-board decision making to save resources for valuable observations in the future. Previous work has focused on developing DT mission use cases, such as storm hunting and cloud avoidance, that have relatively straightforward observation goals (i.e., look for storms, avoid clouds). However, DT has the potential to improve the science return of more complex missions and studies. To demonstrate this, we present and develop a new DT mission scenario to study the Planetary Boundary Layer (PBL). This paper describes the elements of our PBL mission scenario, which not only only involves multiple spacecraft, but also more sophisticated instruments, science models, and on-board decision making.

Recent developments in New Space companies have led to a dramatic increase in capabilities in Earth Observation. These advances in edge computing, low latency communications, and many new on orbit assets represent a unique opportunity for Earth Observation. NASA’s New Observation System (NOS) program aims to leverage these new capabilities to achieve global reach of science events such as volcanic eruption, wildfires, flooding, not by wide swath instruments but rather by intelligent, directed sensing, onboard analysis, and dissemination of knowledge rather than data using low latency communications links. We describe ongoing efforts to deploy NOS capabilities to the CogniSAT-6/HAMMER satellite launched in March 2024, with a currently projected flight demonstration of late summer or early fall 2024.

Coral reefs are an important ecosystem to the local communities and indigenous wildlife that rely on them. However, reefs have greatly degraded in recent decades with the remaining at increasing risk of loss. Quantitatively mapping these reefs would provide a resource for us to monitor changes and understand their health. We explore methods leveraging limited spectral data and resources for efficient global scale modeling of coral reefs. We then evaluate performance on a Deep Neural Network and our previously developed Deep Conditional Dirichlet Model. Regions of high uncertainty based on the model output prediction are used to determine informative in situ sampling. An ergodic planner is implemented to generate a path through these regions to acquire samples that best improve the coral map. The result is a resource efficient learning based pipeline that augments existing spectral data and maps coral reefs globally to improve our understanding of their condition.

Robotic explorers are essential tools for gathering information about regions that are inaccessible to humans. For applications like planetary exploration or search and rescue, robots use prior knowledge about the area to guide their search. Ergodic search methods find trajectories that effectively balance exploring unknown regions and exploiting prior information. In many search based problems, the robot must take into account multiple factors such as scientific information gain, risk, and energy, and update its belief about these dynamic objectives as they evolve over time. However, existing ergodic search methods either consider multiple static objectives or consider a single dynamic objective, but not multiple dynamic objectives. We address this gap in existing methods by presenting an algorithm called Dynamic Multi-Objective Ergodic Search (D-MO-ES) that efficiently plans an ergodic trajectory on multiple changing objectives. Our experiments show that our method requires up to nine times less compute time than a naïve approach with comparable coverage of each objective.

Dynamic Targeting (DT) will enable future Earth Observing instruments to intelligently reconfigure and point instruments to dramatically enhance science return. In this work we present a realistic simulation study of DT for tracking of storm features. To this end we have developed several algorithms from Operations Research and Artificial Intelli-gence/heuristic search. We benchmark these algorithms and show that DT is a powerful tool with the potential to significantly improve science yield.

Earth and planetary sciences often rely upon the detailed examination of spectroscopic data for rock and mineral identification. This typically requires the collection of high resolution spectroscopic measurements. However, they tend to be scarce, as compared to low resolution remote spectra. This work addresses the problem of inferring high-resolution mineral spectroscopic measurements from low resolution observations using probability models. We present the Deep Gaussian Conditional Model, a neural network that performs probabilistic super resolution via maximum likelihood estimation. It also provides insight into learned correlations between measurements and spectroscopic features, allowing for the tractability and interpretability that scientists often require for mineral identification. Experiments using remote spectroscopic data demonstrate that our method compares favorably to other analogous probabilistic methods. Finally, we show and discuss how our method provides human-interpretable results, making it a compelling analysis tool for scientists.

Maintaining high levels of productivity for planetary rover missions is very difficult due to limited communication and heavy reliance on ground control. There is a need for autonomy that enables more adaptive and efficient actions based on real-time information. This paper presents an autonomous mapping and exploration approach for planetary rovers. We first describe a machine learning model that actively combines remote and rover measurements for mapping. We focus on spectroscopic data because they are commonly used to investigate surface composition. We then incorporate notions from information theory and non-myopic path planning to improve exploration productivity. Finally, we demonstrate the feasibility and successful performance of our approach via spectroscopic investigations of Cuprite, Nevada; a well-studied region of mineralogical and geological interest. We first perform a detailed analysis in simulations, and then validate those results with an actual rover in the field in Nevada.

This work extensively studies and analyses several unsupervised clustering methods for hyperspectral data. We look at unsupervised classification solutions that accomplish adaptive cluster formation in anticipation for new data discoveries. We provide qualitative and quantitative answers to significant problems like high-dimensionality of hyperspectral datasets, multiple sources and relative amounts of existing noise in data and low class separability. The effectiveness of various clustering techniques is illustrated on diverse hyperspectral datasets by intensive experimentation, comparison between techniques and analysis.

Remote sensing measurements can provide crucial information about the material properties of a planetary surface but their application is limited by their spatial resolution, typically tens of meters per pixel, when constituent materials are mixed at much finer scale. Consequently the orbital observations must be validated with in situ measurements from a spectrometer on the ground. In planetary exploration this means that a rover must visit selected locations that jointly improve a model of the environment and satisfy mobility and sampling constraints. Conventional planning methods used in this situation follow sub-optimal greedy strategies that are not scalable to large areas. We show how the problem can be effectively defined in a Markov Decision Process framework and propose a planning algorithm based on Monte Carlo Tree Search, which is efficient but devoid of these drawbacks thereby providing superior performance. We evaluate our approach using hyperspectral imagery of a well-studied geologic site in Cuprite, Nevada.

Planetary exploration involves frequent scientific reformulation and replanning. It is limited by communication constraints and to overcome this limitation, this paper formulates the process as a collaboration in which the human scientist and the robot work together to fill in gaps in knowledge to make discoveries. It introduces the science hypothesis map as the probabilistic structure in which scientists initially describe their abstract beliefs and hypotheses, and in which the state of this belief evolves as the robot makes raw measurements. It discusses how to incorporate path planning for maximizing scientific information gain, which is efficiently computed. As proof of concept, this paper describes a geologic exploration problem where a robot uses a spectrometer to infer the geologic composition of different regions in a mining district at Cuprite, Nevada. It shows that the science hypothesis map can infer geologic units with high accuracy, and that exploration using information gain-based path planning has better performance than exploration with conventional science-blind algorithms.

Efficient exploration of unknown terrains by extraterrestrial rovers requires the development of strategies that reduce the entropy in the geological classification of a given terrain. Without such intelligent strategies, teleoperation of the rover is reliant either on human intuition or on the exhaustive exploration of the entire terrain. This paper highlights the use of low-resolution reconnaissance using satellite imagery to generate plans for rovers that reduce the overall uncertainty in the various geological classes. This becomes pivotal when exploration to collect diverse samples is resource constrained through exploration budgets and transmission bandwidths. We put forward two major contributions - a science-aware planner that uses information gain and a novel method of estimating this information gain. We propose an exploration strategy, based on the Multi-Heuristic A*, to solve the trade-off between optimizing path lengths and geological exploration through Pareto-optimal solutions. We show that our algorithm, which explicitly uses projected entropy-reduction in planning, significantly outperforms science-agnostic approaches and other science-aware strategies like greedy best-first searches. We further propose a feature-space based entropy formulation in contrast to the frequently used differential entropy formulation and show superior results when reconstructing the unsampled data from the set of sampled points.