Opportunistic Science Research Articles

Domain Specific Situated Planning (Student Abstract)

Traditionally when planning we assume that we receive the problem instance as input, then formulate a plan, then the clock begins and the agent executes the plan. Sometimes we are forced to consider time passing as we plan, which is known as situated planning. In my dissertation I explore situated planning in three domains: grid based path planning, the orienteering problem and opportunistic science.

Enabling astronaut self-scheduling using a robust advanced modelling and scheduling system: An assessment during a Mars analogue mission

Human long duration exploration missions (LDEMs) raise a number of technological challenges. This paper addresses the question of the crew autonomy: as the distances increase, the communication delays and constraints tend to prevent the astronauts from being monitored and supported by a real time ground control. Eventually, future planetary missions will necessarily require a form of astronaut self-scheduling. We study the usage of a computer decision-support tool by a crew of analog astronauts, during a Mars simulation mission conducted at the Mars Desert Research Station (MDRS, Mars Society) in Utah. The proposed tool, called Romie, belongs to the new category of Robust Advanced Modelling and Scheduling (RAMS) systems. It allows the crew members (i) to visually model their scientific objectives and constraints, (ii) to compute near-optimal operational schedules while taking uncertainty into account, (iii) to monitor the execution of past and current activities, and (iv) to modify scientific objectives/constraints w.r.t. unforeseen events and opportunistic science. In this study, we empirically measure how the astronauts, who are novice planners, perform at using such a tool when self-scheduling under the realistic assumptions of a simulated Martian planetary habitat.

Transit Search for Exoplanets around Alpha Centauri A and B with ASTERIA

Abstract Alpha Centauri is a triple star system with two Sun-like stars, α Cen A (V = 0.01) and B (V = 1.33), and a third fainter red dwarf star, Proxima Centauri. Most current transit missions cannot produce precision photometry of α Cen A and B as their detectors saturate for these very bright stars. The Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) was a technology demonstration mission that successfully demonstrated two key technologies necessary for precision photometry achieving line-of-sight fine-pointing stability of 0.5″ rms and focal plane temperature control of ±0.01 K over a period of 20 minutes. The payload consisted of a 6.7 cm aperture diameter refractive camera and used a scientific complementary metal-oxide semiconductor detector that enabled monitoring of the brightest stars without saturating. We obtained spatially unresolved (blended) observations of α Cen A and B during opportunistic science campaigns as part of ASTERIA’s extended mission. The resulting 1σ photometric precision for the blended α Cen A and B data is 250 ppm (parts per million) per 9 s exposure. We do not find evidence of transits in the blended data. We establish limits for transiting exoplanets around both α Cen A and B using transit signal injection and recovery tests. We find that ASTERIA is sensitive to planets with radii as small as 3.0 R ⊕ around α Cen A and 3.7 R ⊕ around α Cen B, corresponding to signals of ∼500 ppm (signal-to-noise ratio = 5.0) in the blended data, with periods ranging from 0.5 to 6 days.

Demonstrating High-precision Photometry with a CubeSat: ASTERIA Observations of 55 Cancri e

Abstract Arcsecond Space Telescope Enabling Research In Astrophysics (ASTERIA) is a 6U CubeSat space telescope (10 cm × 20 cm × 30 cm, 10 kg). ASTERIA’s primary mission objective was demonstrating two key technologies for reducing systematic noise in photometric observations: high-precision pointing control and high-stability thermal control. ASTERIA demonstrated 0.″5 rms pointing stability and ±10 mK thermal control of its camera payload during its primary mission, a significant improvement in pointing and thermal performance compared to other spacecraft in ASTERIA’s size and mass class. ASTERIA launched in 2017 August and deployed from the International Space Station in 2017 November. During the prime mission (2017 November–2018 February) and the first extended mission that followed (2018 March–2018 May), ASTERIA conducted opportunistic science observations, which included the collection of photometric data on 55 Cancri, a nearby exoplanetary system with a super-Earth transiting planet. The 55 Cancri data were reduced using a custom pipeline to correct complementary metal-oxide semiconductor (CMOS) detector column-dependent gain variations. A Markov Chain Monte Carlo approach was used to simultaneously detrend the photometry using a simple baseline model and fit a transit model. ASTERIA made a marginal detection of the known transiting exoplanet 55 Cancri e (∼2 ), measuring a transit depth of 374 ± 170 ppm. This is the first detection of an exoplanet transit by a CubeSat. The successful detection of super-Earth 55 Cancri e demonstrates that small, inexpensive spacecraft can deliver high-precision photometric measurements.

Heuristic search via graphical structure in temporal interval-based planning for deep space exploration

Operations of conventional spacecraft used to be planned on ground and are uploaded as telecommands and executed on board at due time. However, because of difficulties in communicating with distant spacecraft, direct human control for the spacecraft is infeasible. Therefore, great hopes are placed on automated planning techniques to enhance the security of the spacecraft. By deciding a coon, as to support opportunistic science, the planner is mplex set of activities or states, an onboard planner is able to effectively arrange the daily tasks on a spacecraft. In additialso required to respond in the shortest possible time. Typically, to better characterize the spacecraft, the timeline-based knowledge representation benefits from its powerful ability to describe time and temporal behaviors, which is essential to effectively address real world problems. Compliant with the representation method, an elegant approach is devised for search guidance and solving problems efficiently in space-like contexts. Specifically, the key technique we build on is the heuristic estimate strategy based on a graphical structure defined in the model. Furthermore, a search algorithm joint with the heuristic function is proposed to avoid redundant work. By evaluating the branching nodes, this approach is able to prune irrelevant search space and make improvements in onboard planning efficiency. Our experiments exhibit an excellent performance on tested instances compared to Europa2.

Goal-Based Operations: An Overview

Operating robotic space missions via time-based command sequences has become a limiting factor in the exploration, defense, and commercial sectors. Command sequencing was originally designed for comparatively simple and predictable missions, with safe-mode responses for most faults. This approach has been increasingly strained to accommodate today’s more complex missions, which require advanced capabilities such as autonomous fault diagnosis and response, vehicle mobility with hazard avoidance, opportunistic science observations, etc. Goal-based operation changes the fundamental basis of operations from imperative command sequences to declarative specifications of operational intent and termed goals. Execution based on explicit intent simplifies operator workload by focusing on what to do rather than how to do it. The move toward goal-based operations, which has already begun in some space missions, involves changes and opportunities in several places: operational processes and tools, human interface design, planning and scheduling, control architecture, fault protection, and verification and validation. Further, the need for future interoperation among multiple goal-based systems suggests that attention be given to areas for standardization. This overview paper defines the concept of goal-based operations, reviews a history of steps in this direction, and discusses the areas of change and opportunity through comparison with the prevalent operational paradigm of command sequencing.

Autonomous science for an ExoMars Rover–like mission

AbstractIn common with other Mars exploration missions, human supervision of Europe's ExoMars Rover will be mostly indirect via orbital relay spacecraft and thus far from immediate. The gap between issuing commands and witnessing the results of the consequent rover actions will typically be on the order of several hours or even sols. In addition, it will not be possible to observe the external environment at the time of action execution. This lengthens the time required to carry out scientific exploration and limits the mission's ability to respond quickly to favorable science events. To increase potential science return for such missions, it will be necessary to deploy autonomous systems that include science target selection and active data acquisition. In this work, we have developed and integrated technologies that we explored in previous studies and used the resulting test bed to demonstrate an autonomous, opportunistic science concept on a representative robotic platform. In addition to progressing the system design approach and individual autonomy components, we have introduced a methodology for autonomous science assessment based on terrestrial field science practice. © 2009 Wiley Periodicals, Inc.

Oasis: Onboard autonomous science investigation system for opportunistic rover science

AbstractThe Onboard Autonomous Science Investigation System has been developed to enable a rover to identify and react to serendipitous science opportunities. Using the FIDO rover in the Mars Yard at JPL, we have successfully demonstrated a fully autonomous opportunistic science system. The closed loop system tests included the rover acquiring image data, finding rocks in the image, analyzing rock properties and identifying rocks that merit further investigation. When the system on the rover alerts the rover to take additional measurements of interesting rocks, the planning and scheduling component determines if there are enough resources to meet this additional science data request. The rover is then instructed to either turn toward the rock, or to actually move closer to the rock to take an additional, close‐up image. Prototype dust devil and cloud detection algorithms were delivered to an infusion task which refined the algorithms specifically for Mars Exploration Rovers (MER). These algorithms have been integrated into the MER flight software and were recently uploaded to the rovers on Mars. © 2007 Wiley Periodicals, Inc.