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This paper describes the ASPEN system for automation of planning and scheduling for space mission operations. ASPEN contains a number of innovations including: an expressive but easy to use modeling language, multiple search (inference) engines, iterative repair suited for mixed-initiative human in loop operations, real-time replanning and response (in the(More)
We present in detail some of the challenges in developing reusable robotic software. We base that on our experience in developing the CLARAty robotics software, which is a generic object-oriented framework used for the integration of new algorithms in the areas of motion control, vision, manipulation, locomotion, navigation, localization, planning and(More)
Work on generative planning syste[ns ha-~ focllsed On two di-t,crsc aplJroac}lcs to plan construction. Ilierarchical task network (11'~N) planners build plans by successively refining high-level goals into lower-level activities. Operator-baqed planners employ means-end analysis to formulate plans consisting of Iow-level activities. While many have argued(More)
— This paper presents an overview of a newly developed Coupled Layer Architecture for Robotic Autonomy (CLARAty), which is designed for improving the modularity of system software while more tightly coupling the interaction of autonomy and controls. First, we frame the problem by briefly reviewing previous work in the field and describing the impediments(More)
We will present an overview of the CLARAty architecture which aims at developing reusable software components for robotic systems. These components are to support autonomy software which plans and schedules robot activities. The CLARAty architecture modifies the conventional three-level robotic architecture into a new two-layered design: the Functional(More)
This paper presents an overview of the intelligent decision-making capabilities of the CLARAty robotic architecture for autonomy. CLARAty is a two layered architecture where the top Decision Layer contains techniques for autonomously creating a plan of robot commands and the bottom Functional Layer provides standard robot capabilities that interface to(More)
This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The Multi-P~over Integrated Science Understanding System combines concepts from machine learning with planning and scheduling to perform autonomous scientific exploration by cooperating rovers. The integrated system(More)
—The Onboard Autonomous Science Investigation System (OASIS) evaluates geologic data gathered by a planetary rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and(More)
Space mission operations require flexible, efficient and reliable plan execution. In typical operations command sequences (which are a simple subset of general executable plans) are generated on the ground, either manually or with assistance from automated planning, and sent to the spacecraft. For more advanced operations more expressive executable plans(More)