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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)
framework for while maintajning the ability to easily integrate platform-specific algorithms. Abstract In this article, we will present an overview of the Coupled Layered Architecture for Robotic Autonomy. CLARAty develops a pamework for generic and reusable robotic components that can be adapted to a number of heterogeneous robot platforms. It also(More)
This paper describes and evaluates three methods for coordinating multiple agents. These agents interact in two ways. First, they are able to work together to achieve a common pool of goals which would require greater time to achieve by any one of the agents operating independently. Second, the agents share resources that are required by the actions needed(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)
Most research in planning and learning has involved linear, state-based planners. This paper presents Scope, a system for learning search-control rules that improve the performance of a partial-order planner. Scope integrates explanation-based and induc-tive learning techniques to acquire control rules for a partial-order planner. Learned rules are in the(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)
Robust navigation through rocky terrain by small mobile robots is important for maximizing science return from up-coming missions to Mars. We are addressing this problem at multiple levels through the development of intelligent se-quencing, sensor constrained path planning, natural terrain visual localization, and real-time state estimation. Each of these(More)
The Onboard Autonomous Science Investigation System (OASIS) 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(More)
The Autonomous Exploration for Gathering Increased Science (AEGIS) system enables automated data collection by planetary rovers. AEGIS software was uploaded to the Mars Exploration Rover (MER) mission’s Opportunity rover in December 2009 and has successfully demonstrated automated onboard targeting based on scientist-specified objectives. Prior to(More)