@article{ branch_jfr2019_front,
  author = {A. Branch and M. M. Flexas and B. Claus and A. F. Thompson and Y. Zhang and E. B. Clark and S. Chien and D. M. Fratantoni and J. C. Kinsey. and B. Hobson and B. Kieft and F. P. Chavez},
  title = {Front Delineation and Tracking with Multiple Underwater Vehicles},
  journal = {Journal of Field Robotics (JFR)},
  volume = {36},
  number = {3},
  pages = {568-586},
  publisher = {Wiley},
  keywords = {adaptive sampling, autonomous underwater vehicles, multiasset planning, ocean front tracking},
  doi = {10.1002/rob.21853},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/rob.21853},
  eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/rob.21853},
  abstract = {Abstract This study describes a method for detecting and tracking ocean fronts using multiple autonomous underwater vehicles (AUVs). Multiple vehicles, equally spaced along the expected frontal boundary, complete near parallel transects orthogonal to the front. Two different techniques are used to determine the location of the front crossing from each individual vehicle transect. The first technique uses lateral gradients to detect when a change in the observed water property occurs. The second technique uses a measure of the vertical temperature structure over a single dive to detect when the vehicle is in upwelling water. Adaptive control of the vehicles ensure they remain perpendicular to the estimated front boundary as it evolves over time. This method was demonstrated in several experiment periods totaling weeks, in and around Monterey Bay, CA, in May and June of 2017. We compare the two front detection methods, a lateral gradient front detector and an upwelling front detector using the Vertical Temperature Homogeneity Index. We introduce two metrics to evaluate the adaptive control techniques presented. We show the capability of this method for repeated sampling across a dynamic ocean front using a fleet of three types of platforms: short-range Iver AUVs, Tethys-class long-range AUVs, and Seagliders. This method extends to tracking gradients of different properties using a variety of vehicles.},
  clearance = {CL#18-6807},
  featured = 1,
  year = {2019},
  project = {keck_marine},
}

Abstract This study describes a method for detecting and tracking ocean fronts using multiple autonomous underwater vehicles (AUVs). Multiple vehicles, equally spaced along the expected frontal boundary, complete near parallel transects orthogonal to the front. Two different techniques are used to determine the location of the front crossing from each individual vehicle transect. The first technique uses lateral gradients to detect when a change in the observed water property occurs. The second technique uses a measure of the vertical temperature structure over a single dive to detect when the vehicle is in upwelling water. Adaptive control of the vehicles ensure they remain perpendicular to the estimated front boundary as it evolves over time. This method was demonstrated in several experiment periods totaling weeks, in and around Monterey Bay, CA, in May and June of 2017. We compare the two front detection methods, a lateral gradient front detector and an upwelling front detector using the Vertical Temperature Homogeneity Index. We introduce two metrics to evaluate the adaptive control techniques presented. We show the capability of this method for repeated sampling across a dynamic ocean front using a fleet of three types of platforms: short-range Iver AUVs, Tethys-class long-range AUVs, and Seagliders. This method extends to tracking gradients of different properties using a variety of vehicles.

@article{francis-estlin-doran-et-al-2017,
  author = {R. Francis and T. Estlin and G. Doran and S. Johnstone and D. Gaines and V. Verma and M. Burl and J. Frydenvang and S. Montaño and R. C. Wiens and S. Schaffer and O. Gasnault and L. DeFlores and D. Blaney and B. Bornstein},
  title = {AEGIS Autonomous Targeting for ChemCam on Mars Science Laboratory: Deployment and Results of Initial Science Team Use},
  journal = {Science Robotics},
  year = {2017},
  month = {June},
  abstract = {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 percent (where approximately 24 percent 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.},
  clearance = {CL#17-2702},
  project = {rover},
  featured = 1,
}

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 percent (where approximately 24 percent 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.

@article{chien-wagstaff-2017,
  author = {S. Chien and K. L. Wagstaff},
  title = {Robotic Space Exploration Agents},
  journal = {Science Robotics},
  year = {2017},
  month = {June},
  abstract = {By making their own exploration decisions, robotic spacecraft can conduct traditional science investigations more efficiently and even achieve otherwise impossible observations, such as responding to a short-lived plume at a comet millions of miles from Earth.},
  url = {http://robotics.sciencemag.org/cgi/content/full/2/7/eaan4831?ijkey=ygu9BARoFZfzo&keytype=ref&siteid=robotics},
  clearance = {CL#17-2089},
  featured = 1,
}

By making their own exploration decisions, robotic spacecraft can conduct traditional science investigations more efficiently and even achieve otherwise impossible observations, such as responding to a short-lived plume at a comet millions of miles from Earth.

@article{gao-chien-2017,
  author = {Y. Gao and S. Chien},
  title = {Review on space robotics: Toward top-level science through space exploration},
  journal = {Science Robotics},
  year = {2017},
  month = {June},
  abstract = {Robotics and autonomous systems have been instrumental to space exploration by enabling scientific breakthroughs and by fulfilling human curiosity and ambition to conquer new worlds. We provide an overview of space robotics as a rapidly emerging field, covering basic concepts, definitions, historical context, and evolution. We further present a technical road map of the field for the coming decades, taking into account major challenges and priorities recognized by the international space community. Space robotics represents several key enablers to a wide range of future robotic and crewed space missions as well as opportunities for knowledge and technology transfer to many terrestrial sectors. In the greater humanitarian context, space robotics inspires both current and future generations to exploration and critical study of science, technology, engineering, and mathematics.},
  clearance = {CL#17-2439},
  featured = 1,
}

Robotics and autonomous systems have been instrumental to space exploration by enabling scientific breakthroughs and by fulfilling human curiosity and ambition to conquer new worlds. We provide an overview of space robotics as a rapidly emerging field, covering basic concepts, definitions, historical context, and evolution. We further present a technical road map of the field for the coming decades, taking into account major challenges and priorities recognized by the international space community. Space robotics represents several key enablers to a wide range of future robotic and crewed space missions as well as opportunities for knowledge and technology transfer to many terrestrial sectors. In the greater humanitarian context, space robotics inspires both current and future generations to exploration and critical study of science, technology, engineering, and mathematics.

@article{rabideau-chien-galer-et-al-2017,
  author = {G. Rabideau and S. Chien and M. Galer and F. Nespoli and M. Costa},
  title = {Managing Spacecraft Memory Buffers with Concurrent Data Collection and Downlink},
  journal = {Journal of Aerospace Information Systems (JAIS)},
  year = {2017},
  month = {December},
  doi = {http://arc.aiaa.org/doi/abs/10.2514/1.I010544},
  organization = {AIAA},
  abstract = {Space mission planning/scheduling is determining the set of spacecraft activities to meet mission objectives while respecting mission constraints. One important category of mission constraints is data management. As the spacecraft acquires data via science instruments and engineering telemetry, it must store the data on board until it is able to downlink the data to ground communications stations. Because onboard storage and communication opportunities are often limited, this can be a challenging task. This paper first describes the general problem of onboard memory management for spacecraft that allow concurrent data collection and downlink activities. Then, a fast, intuitive heuristic is presented for solving this problem, and its use on the Rosetta comet rendezvous mission is evaluated. Finally, different local heuristics are compared for a greedy search to a global graph-based solution, measuring performance in terms of runtime and robustness to downlink buffer overflow. The results show that local heuristics can produce solutions that are comparable, and often better than, solutions from a global formulation.},
  url = {http://arc.aiaa.org/doi/abs/10.2514/1.I010544},
  clearance = {CL#17-4479},
  project = {rosetta},
  featured = 1,
}

Space mission planning/scheduling is determining the set of spacecraft activities to meet mission objectives while respecting mission constraints. One important category of mission constraints is data management. As the spacecraft acquires data via science instruments and engineering telemetry, it must store the data on board until it is able to downlink the data to ground communications stations. Because onboard storage and communication opportunities are often limited, this can be a challenging task. This paper first describes the general problem of onboard memory management for spacecraft that allow concurrent data collection and downlink activities. Then, a fast, intuitive heuristic is presented for solving this problem, and its use on the Rosetta comet rendezvous mission is evaluated. Finally, different local heuristics are compared for a greedy search to a global graph-based solution, measuring performance in terms of runtime and robustness to downlink buffer overflow. The results show that local heuristics can produce solutions that are comparable, and often better than, solutions from a global formulation.

@article{chien-doubleday-thompson-et-al-2016,
  author = {S. Chien and J. Doubleday and D. R. Thompson and K. Wagstaff and J. Bellardo and C. Francis and E. Baumgarten and A. Williams and E. Yee and E. Stanton and J. Piug-Suari},
  title = {Onboard Autonomy on the Intelligent Payload EXperiment (IPEX) CubeSat Mission},
  journal = {Journal of Aerospace Information Systems (JAIS)},
  year = {2016},
  month = {April},
  organization = {AIAA},
  abstract = {The Intelligent Payload Experiment (IPEX) is a CubeSat that flew from December 2013 through January 2015 and validated autonomous operations for onboard instrument processing and product generation for the Intelligent Payload Module of the Hyperspectral Infrared Imager (HyspIRI) mission concept. IPEX used several artificial intelligence technologies. First, IPEX used machine learning and computer vision in its onboard processing. IPEX used machine-learned random decision forests to classify images onboard (to downlink classification maps) and computer vision visual salience software to extract interesting regions for downlink in acquired imagery. Second, IPEX flew the Continuous Activity Scheduler Planner Execution and Re-planner AI planner/scheduler onboard to enable IPEX operations to replan to best use spacecraft resources such as file storage, CPU, power, and downlink bandwidth. First, the ground and flight operations concept for proposed HyspIRI IPM operations is described, followed by a description the ground and flight operations concept used for the IPEX mission to validate key elements of automation for the proposed HyspIRI IPM operations concept. The use of machine learning, computer vision, and automated planning onboard IPEX is also described. The results from the over-1-year flight of the IPEX mission are reported.},
  clearance = {CL#16-0521},
  project = {ipex},
  featured = 1,
}

The Intelligent Payload Experiment (IPEX) is a CubeSat that flew from December 2013 through January 2015 and validated autonomous operations for onboard instrument processing and product generation for the Intelligent Payload Module of the Hyperspectral Infrared Imager (HyspIRI) mission concept. IPEX used several artificial intelligence technologies. First, IPEX used machine learning and computer vision in its onboard processing. IPEX used machine-learned random decision forests to classify images onboard (to downlink classification maps) and computer vision visual salience software to extract interesting regions for downlink in acquired imagery. Second, IPEX flew the Continuous Activity Scheduler Planner Execution and Re-planner AI planner/scheduler onboard to enable IPEX operations to replan to best use spacecraft resources such as file storage, CPU, power, and downlink bandwidth. First, the ground and flight operations concept for proposed HyspIRI IPM operations is described, followed by a description the ground and flight operations concept used for the IPEX mission to validate key elements of automation for the proposed HyspIRI IPM operations concept. The use of machine learning, computer vision, and automated planning onboard IPEX is also described. The results from the over-1-year flight of the IPEX mission are reported.