The AEGIS (Autonomous Exploration for Gathering Increased Science) intelligent targeting software system has been in use on the Mars Science Laboratory (MSL) mission since 2016.The system allows on-board autonomous selection of targets for the ChemCam remote geochemistry instrument based on analysis of images taken by the rover.This paper describes the deployment of AEGIS to MSL and the operational use of the system since rollout to science operations in May of 2016.We describe how MSL Science Operations have adapted to include autonomous target selection, both in procedures and in exploration strategies, and how mission planners and scientists have used AEGIS autonomy to enhance their work on the MSL mission.

We present an intelligent autonomous underwater vehicle (AUV) in-situ sensing system with real-time, on-board, resource-aware adaptive planning, control and sampling not currently available by traditional sampling methods of ship point sampling, moorings, profilers, gliders or AUVs which lack realtime responses, on-board analysis and system-wide reasoning.

Limitations on interplanetary communications create operations latencies and slow progress in planetary surface missions, with particular challenges to narrow-field-of-view science instruments requiring precise targeting. The AEGIS (Autonomous Exploration for Gathering Increased Science) autonomous targeting system has been in routine use on NASA's Curiosity Mars rover since May 2016, selecting targets for the ChemCam remote geochemical spectrometer instrument. AEGIS operates in two modes; in autonomous target selection, it identifies geological targets in images from the rover's navigation cameras, choosing for itself targets that match the parameters specified by mission scientists the most, and immediately measures them with ChemCam, without Earth in the loop. In autonomous pointing refinement, the system corrects small pointing errors on the order of a few milliradians in observations targeted by operators on Earth, allowing very small features to be observed reliably on the first attempt. AEGIS consistently recognizes and selects the geological materials requested of it, parsing and interpreting geological scenes in tens to hundreds of seconds with very limited computing resources. Performance in autonomously selecting the most desired target material over the last 2.5 kilometers of driving into previously unexplored terrain exceeds 93% (where ~24% is expected without intelligent targeting), and all observations resulted in a successful geochemical observation. The system has substantially reduced lost time on the mission and markedly increased the pace of data collection with ChemCam. AEGIS autonomy has rapidly been adopted as an exploration tool by the mission scientists and has influenced their strategy for exploring the rover's environment.

This paper describes new autonomy technology that enabled a team of unmanned surface vehicles (USVs) to execute cooperative behaviors in the USV Swarm II harbor patrol demonstration and provides a description of autonomy performance in the event. The new developments extend the NASA Jet Propulsion Laboratory's CARACaS (Control Architecture for Robotic Agent Command and Sensing) autonomy architecture, which provides foundational software infrastructure, core executive functions, and several default robotic technology modules. In Swarm II, CARACaS demonstrated higher levels of autonomy and more complex cooperation than previous on-water exercises, using full-sized vehicles and real-world sensing and communication. The core autonomous behaviors to support the harbor patrol scenario included Patrol, Track, Inspect, and Trail, providing the capability of finding all vessels entering the patrol area, keeping track of them, inspecting them to infer intent, and trailing suspect vessels. Significantly, CARACaS assumed responsibility for not only executing tasks safely and efficiently but also recognizing what tasks needed to be accomplished, given the current state of the world. Since the heterogeneous USV teams shared world model that evolved, such as due to (dis)appearance of vessels in the area or a change in health or availability of a USV, CARACaS replanned to generate and reallocate the new task list. Thus, human intervention was never required in the loop to task USVs during mission execution, though a supervisory role was supported in the autonomy system for mission monitoring and exception handling. Finally, CARACaS also ensured the USVs avoided hazards and obeyed the applicable rules of the road, using its local motion planning modules.

AEGIS (Autonomous Exploration for Gathering Increased Science) is a software suite that will imminently be operational aboard NASA's Curiosity Mars rover, allowing the rover to autonomously detect and prioritize targets in its surroundings, and acquire geochemical spectra using its ChemCam instrument. ChemCam, a Laser-Induced Breakdown Spectrometer (LIBS), is normally used to study targets selected by scientists using images taken by the rover on a previous sol and relayed by Mars orbiters to Earth. During certain mission phases, ground-based target selection entails significant delays and the use of limited communication bandwidth to send the images. AEGIS will allow the science team to define the properties of preferred targets, and obtain geochemical data more quickly, at lower data penalty, without the extra ground-inthe-loop step. The system uses advanced image analysis techniques to find targets in images taken by the rover's stereo navigation cameras (NavCam), and can rank, filter, and select targets based on properties selected by the science team. AEGIS can also be used to analyze images from ChemCam's Remote Micro Imager (RMI) context camera, allowing it to autonomously target very fine-scale features - such as veins in a rock outcrop - which are too small to detect with the range and resolution of NavCam. AEGIS allows science activities to be conducted in a greater range of mission conditions, and saves precious time and command cycles during the rover's surface mission. The system is currently undergoing initial tests and checkouts aboard the rover, and is expected to be operational by late 2015. Other current activities are focused on science team training and the development of target profiles for the environments in which AEGIS is expected to be used on Mars.

This paper presents an approach to characterize power system loads through estimation of contributions from individual load types. In contrast to methods that fit one aggregate model to observed load behavior, this approach estimates the inventory of separate components that compose the total power consumption. Common static and dynamic models are used to represent components of the load, and parameter estimation is used to determine the amount each load contributes to the cumulative consumption. Trajectory sensitivities form the basis of the parameter estimation algorithm and give insight into which parameters are well-conditioned for estimation. Parameters of interest are contributions to total load and initial conditions for dynamic loads. Results are presented for two simulation-based studies and demonstrate the feasibility of the approach. In the first study, the composition of multiple loads connected to a bus was estimated by subjecting the bus to a step change in voltage. The second study utilized a disturbance in the WSCC nine-bus test system to facilitate estimates of the combination of loads connected at a bus in the system.

Imaging spectrometers are valuable instruments for space exploration, but their large data volumes limit the number of scenes that can be downlinked. Missions could improve science yield by acquiring surplus images and analyzing them onboard the spacecraft. This onboard analysis could generate surficial maps, summarizing scenes in a bandwidth-efficient manner to indicate data cubes that warrant a complete downlink. Additionally, onboard analysis could detect targets of opportunity and trigger immediate automated follow-up measurements by the spacecraft. Here, we report a first step toward these goals with demonstrations of fully automatic hyperspectral scene analysis, feature discovery, and mapping onboard the Earth Observing One (EO-1) spacecraft. We describe a series of overflights in which the spacecraft analyzes a scene and produces summary maps along with lists of salient features for prioritized downlink. The onboard system uses a superpixel endmember detection approach to identify compositionally distinctive features in each image. This procedure suits the limited computing resources of the EO-1 flight processor. It requires very little advance information about the anticipated spectral features, but the resulting surface composition maps agree well with canonical human interpretations. Identical spacecraft commands detect outlier spectral features in multiple scenarios having different constituents and imaging conditions.

Software has been designed to schedule remote sensing with the Earth Observing One spacecraft. The software attempts to satisfy as many observation requests as possible considering each against spacecraft operation constraints such as data volume, thermal, pointing maneuvers, and others. More complex constraints such as temperature are approximated to enable efficient reasoning while keeping the spacecraft within safe limits. Other constraints are checked using an external software library. For example, an attitude control library is used to determine the feasibility of maneuvering between pairs of observations. This innovation can deal with a wide range of spacecraft constraints and solve large scale scheduling problems like hundreds of observations and thousands of combinations of observation sequences.

Remote-sensed hyperspectral data represents significant challenges in downlink due to its large data volumes. This paper describes a research program designed to process hyperspectral data products onboard spacecraft to (a) reduce data downlink volumes and (b) decrease latency to provide key data products (often by enabling use of lower data rate communications systems). We describe efforts to develop onboard processing to study volcanoes, floods, and cryosphere, using the Hyperion hyperspectral imager and onboard processing for the Earth Observing One (EO-1) mission as well as preliminary work targeting the Hyperspectral Infrared Imager (HyspIRI) mission.

This paper addresses parameter estimation for hybrid dynamical systems, such as electric power systems, with state-dependent events and time-delayed parametric switching. Previous work dealing with hybrid dynamical systems has addressed the calculation of trajectory sensitivities used in gradient-based parameter estimation methods, as well as the computation of proper sensitivity jump conditions at switching events. We extend this work by allowing for state-dependent delay and cancellation of switching events, and by modifying the jump conditions accordingly. We also discuss the treatment of non-triggered events, the calculation of sensitivities with respect to event times and post-switch parameter values, and the use of asynchronously sampled data. A simulation study on the IEEE 39-bus power system is presented to demonstrate the estimation of parameters as the system undergoes delayed switching events.

Planning and scheduling for space operations entails the development of applications that embed intimate domain knowledge of distinct areas of mission control, while allowing for significant collaboration among them. The separation is useful because of differences in the planning problem, solution methods, and frequencies of replanning that arise in the different disciplines. For example, planning the activities of human spaceflight crews requires some reasoning about all spacecraft resources at timescales of minutes or seconds, and is subject to considerable volatility. Detailed power planning requires managing the complex interplay of power consumption and production, involves very different classes of constraints and preferences, but once plans are generated they are relatively stable. A prototype application has been developed that separately supports Crew planning and Power planning for the International Space Station (ISS). Domain requirements have been modeled in a significant level of detail, and loosely-coupled integration has been demonstrated in a realistic scenario. The integration is enabled by implementing a generic collaboration architecture that can be used to coordinate the work of any number of planning domains. The architecture is used to integrate two different planners employing different underlying algorithms and data structures, by means of mapping the overlapping facets of the plans.

As part of a new scheduling system for allocating NASA Deep Space Network (DSN) ground antenna and equipment resources to space missions, we have designed and implemented a requirements language oriented towards the clear and efficient specification of mission communication constraints and preferences. In this paper we discuss the requirement language itself, the model into which the language is translated for automated scheduling, and the impact it has had on scheduling algorithm design. The scheduling engine supporting the language is being integrated into a mixed-initiative scheduling system for a 2008 delivery.

CTAEMS MDP Optimal Planner is a problem-solving software designed to command a single spacecraft/rover, or a team of spacecraft/rovers, to perform the best action possible at all times according to an abstract model of the spacecraft/rover and its environment. It also may be useful in solving logistical problems encountered in commercial applications such as shipping and manufacturing. The planner reasons around uncertainty according to specified probabilities of outcomes using a plan hierarchy to avoid exploring certain kinds of suboptimal actions. Also, planned actions are calculated as the state-action space is expanded, rather than afterward, to reduce by an order of magnitude the processing time and memory used. The software solves planning problems with actions that can execute concurrently, that have uncertain duration and quality, and that have functional dependencies on others that affect quality. These problems are modeled in a hierarchical planning language called C_TAEMS, a derivative of the TAEMS language for specifying domains for the DARPA Coordinators program. In realistic environments, actions often have uncertain outcomes and can have complex relationships with other tasks. The planner approaches problems by considering all possible actions that may be taken from any state reachable from a given, initial state, and from within the constraints of a given task hierarchy that specifies what tasks may be performed by which team member.

A general-purpose computer program for planning the actions of a spacecraft or other complex system has been augmented by incorporating a subprogram that reasons about uncertainties in such continuous variables as times taken to perform tasks and amounts of resources to be consumed. This subprogram computes parametric probability distributions for time and resource variables on the basis of user-supplied models of actions and resources that they consume. The current system accepts bounded Gaussian distributions over action duration and resource use. The distributions are then combined during planning to determine the net probability distribution of each resource at any time point. In addition to a full combinatoric approach, several approximations for arriving at these combined distributions are available, including maximum-likelihood and pessimistic algorithms. Each such probability distribution can then be integrated to obtain a probability that execution of the plan under consideration would violate any constraints on the resource. The key idea is to use these probabilities of conflict to score potential plans and drive a search toward planning low-risk actions. An output plan provides a balance between the user s specified averseness to risk and other measures of optimality.

This paper presents a versatile, Matlab-based simulation and analysis code for electric power systems. The code was created for basic research and designed from a control/dynamical systems perspective. Full advantage was taken of Matlab’s inherent cell arrays, structures, vectorizedcalculations, ODE solvers with event handling and complex arithmetic. Design features that make the code easy to use and expand as well as several example analysis tools are outlined. Keywords—power system simulation and analysis, Matlab

The Automated Scheduling and Planning Environment (ASPEN) computer program has been updated to version 3.0. ASPEN is a modular, reconfigurable, application software framework for solving batch problems that involve reasoning about time, activities, states, and resources. Applications of ASPEN can include planning spacecraft missions, scheduling of personnel, and managing supply chains, inventories, and production lines. ASPEN 3.0 can be customized for a wide range of applications and for a variety of computing environments that include various central processing units and random access memories.

Rapid response to the onset of volcanic activity allows for the early assessment of hazard and risk [Tilling, 1989]. Data from remote volcanoes and volcanoes in countries with poor communication infrastructure can only be obtained via remote sensing [Harris et al., 2000]. By linking notifications of activity from ground-based and spacebased systems, these volcanoes can be monitored when they erupt. Over the last 18 months, NASA's Jet Propulsion Laboratory (JPL) has implemented a Volcano Sensor Web (VSW) in which data from ground-based and space-based sensors that detect current volcanic activity are used to automatically trigger the NASA Earth Observing 1 (EO-1) spacecraft to make highspatial-resolution observations of these volcanoes.

A planning system must reason about the uncertainty of continuous variables in order to accurately project the possible system state over time. A method is devised for directly reasoning about the uncertainty in continuous activity duration and resource usage for planning problems. By representing random variables as parametric distributions, computing projected system state can be simplified. Common approximations and novel methods are compared for over-constrained and lightly constrained domains within an iterative repair planner. Results show improvements in robustness over the conventional non-probabilistic representation by reducing the number of constraint violations during execution. The improvement is more significant for larger problems and those with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels.