Integrated System for Autonomous and Adaptive Caretaking (ISAAC): Survey Demo
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Abiola Akanni↗
Engineering topics
J Benton
Publications and source records attributed to J Benton.
Integrated System for Autonomous and Adaptive Caretaking (ISAAC) Simulated Cargo Logistics Demo Video
This video shows a simulation of autonomous cargo logistics using ISAAC (Integrated System for Autonomous and Adaptive Caretaking). The video shows autonomous planning and execution of a cargo scenario in which the R2 robot moves a cargo bag from a stowed location to a transfer location in the US Lab on the International Space Station. The Astrobee robot then moves the bag from the transfer location to a new temporary stowage location in the Japanese Experiment Module (JEM).
ISAAC↗
ISAAC: An Integrated System for Autonomous and Adaptive Caretaking
The Integrated System for Autonomous and Adaptive Caretaking (ISAAC) project is developing technology for autonomous caretaking of spacecraft, primarily during uncrewed mission phases. ISAAC aims to integrate autonomous intra-vehicular robots (IVR) with spacecraft infrastructure (power, life support, etc.) and ground control. It focuses on capabilities required for NASA’s Gateway cis-lunar outpost that also apply to human missions to Mars and beyond. Its development strategy is to test using existing IVR on the ISS (the Astrobee free-flyer and Robonaut dextrous manipulator) as an analog for future IVR on Gateway.
Trey Smith↗
APRES Prototype Mission Planner System Demonstration
Activity Planning with Resources for the Exploration of Space (APRES) is a mixed-initiative mission planning system for ground operations. APRES has been designed to support multi-spacecraft missions. The APRES Interface is browser-based and includes a plan editor, a timeline plan display, a temporal constraint editor, display of the state and numeric chronicles, and a violation resolution manager. Automation support is supplied by the APRES Service, which includes components that provide the following capabilities:(1) plan simulation, which determines the state and numeric chronicles (values of the model variables over time) and determines when "processes" are triggered and terminated based on world states in the execution trace, (2) violation detection of constraints and flight rules encoded in the domain model, and of the temporal constraints created by the user, (3) violation resolution suggestions as to how to fix the plan's violations via rescheduling. The user controls when and how to utilize the automation support. Demo video included with paper, runtime 8:54 in color with sound.
John L. Bresina↗
Avoiding Selection Bias in Generating Examples of Plans in the Presence of Heuristic Error
It is generally understood that heuristic error hurts the performance of search algorithms, measured in terms of search effort. Hence there is an interest in understanding how to reduce heuristic error. One way to do this is to learn a heuristic from a set of examples of plans generated offline, e.g. bootstrapping methods. In this paper, we consider how some methods for generating examples of plans may skew the training set in the presence of heuristic errors. Initial theoretical results show that duplicate detection is one source of selection bias in the canonical A* algorithm. We introduce a duplicate selection scheme for A* that avoids selection bias in generating cost-optimal examples, without compromising memory efficiency, and develop ideas in the satisficing setting. We evaluate our approach on n x m grids with multiple cost-optimal solutions and synthetic heuristic error. Finally, we attempt to extend these ideas to the problem of generating extreme examples of plans.
Alison S Paredes↗
Mapping the ISS with the Autonomous Free-Flying Astrobee Robots
Space intra-vehicular robots (IVR) can provide coverage survey and targeted close-up inspection imagery capabilities that improve ground operator situation awareness. We report on a series of activities using the autonomous Astrobee robotic free-flyers that mapped the interior of three ISS modules, generating a panoramic tour of the ISS interior without astronaut manual photography. For the ISS, these capabilities can complement or replace time-consuming crew inspection tasks, providing panoramic data products that are easier to navigate than the baseline crew safety videos. For future exploration vehicles with extended uncrewed mission phases, robotic inspection will play a more critical role of providing the primary “eyes in the sky” for vehicle operators.
robotics↗