This innovation creates observations of both targeted geographical regions of interest and general mapping observations, while respecting spacecraft constraints such as data volume, observation timing, visibility, lighting, season, and science priorities. This tool, therefore, addresses both geometric and state/timing/resource constraints by using a grid-based approach. These set covering constraints are then incorporated into a greedy optimization scheduling algorithm that incorporates operations constraints to generate feasible schedules. The resultant tool generates schedules of hundreds of observations per week out of potentially thousands of observations.
Using greedy combinatorial optimization with gridded coverage representation, both targeted mapping observations (small geographical regions that can be covered in one or a small number of observations) and general mapping observations (large geographical regions that would take large numbers of observations, e.g. hundreds or more) can be scheduled. Using gridded coverage representation of a planetary surface, which maps all polygons (regions) into sets of points on a grid, makes polygon intersection very fast, and compiles the coverage problem into a set point covering the problem. At this point, the problem can be attacked using one of a set of combinatorial optimization techniques.
This work was done by Steve A. Chien, Gregg R. Rabideau, David A. McLaren, and Russell L. Knight of Caltech for NASA’s Jet Propulsion Laboratory.
This software is available for commercial licensing. Please contact Dan Broderick at
This Brief includes a Technical Support Package (TSP).
Scheduling Targeted and Mapping Observations with State, Resource, and Timing Constraints
(reference NPO-47603) is currently available for download from the TSP library.
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Overview
The document outlines the scheduling and observation planning process for the Thermal Emission Imaging System (THEMIS) aboard NASA's Mars Odyssey spacecraft, which was launched in April 2001. Since its science mapping began in February 2002, THEMIS has generated extensive multi-spectral data of the Martian surface, contributing significantly to various scientific studies.
The primary challenge addressed in the document is the complex task of selecting science targets for observation as the Martian surface moves rapidly beneath the spacecraft. The document describes the development of the THEMIS Observation Scheduling Tool (TOST), created through collaboration between the Jet Propulsion Laboratory's Artificial Intelligence Group and the science planning team at Arizona State University (ASU). TOST employs automated planning and scheduling technology to efficiently select observations that meet the intricate requirements of the spacecraft, instruments, and scientific goals.
The scheduling process is divided into three main steps:
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Swath Generation: This initial step involves retrieving the Mars ground track of the Odyssey spacecraft using data from the Navigation Ancillary Information Facility (NAIF) and the SPICE toolkit. The ground track points are used to create polygons that represent the areas of the Martian surface visible to THEMIS at specific times. The instrument operates in two modes—infrared (IR) and visible (VIS)—each with distinct swath widths and operational constraints. For instance, the VIS mode has a swath width of 18.4 km, while the IR mode has a width of 32.0 km. The document also notes that certain combinations of instrument modes and observational conditions, such as acquiring VIS images at night, are not desirable and are excluded from the swath generation.
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Campaign Generation: In this step, prioritized imaging requests from scientists are compiled into campaigns. These campaigns may include targeted observations of specific regions of interest on the Martian surface.
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Target Selection: The final step involves selecting a subset of potential observation polygons to maximize scientific coverage while adhering to operational constraints.
The document emphasizes the importance of efficient scheduling to enhance the observing efficiency of the Mars Odyssey mission, particularly as the spacecraft's capabilities have evolved over time. Overall, it highlights the integration of advanced scheduling techniques in space exploration and the ongoing efforts to optimize data collection from Mars.
