Autonomous Systems, Robotics, and Computing Systems Capability Roadmap: NRC Dialogue

Contents include the following: Introduction. Process, Mission Drivers, Deliverables, and Interfaces. Autonomy. Crew-Centered and Remote Operations. Integrated Systems Health Management. Autonomous Vehicle Control. Autonomous Process Control. Robotics. Robotics for Solar System Exploration. Robotics for Lunar and Planetary Habitation. Robotics for In-Space Operations. Computing Systems. Conclusion

2020 NASA Technology Taxonomy

This document is an update (new photos used) of the PDF version of the 2020 NASA Technology Taxonomy that will be available to download on the OCT Public Website. The updated 2020 NASA Technology Taxonomy, or "technology dictionary", uses a technology discipline based approach that realigns like-technologies independent of their application within the NASA mission portfolio. This tool is meant to serve as a common technology discipline-based communication tool across the agency and with its partners in other government agencies, academia, industry, and across the world

Digital Preservation Services : State of the Art Analysis

Research report funded by the DC-NET project.An overview of the state of the art in service provision for digital preservation and curation. Its focus is on the areas where bridging the gaps is needed between e-Infrastructures and efficient and forward-looking digital preservation services. Based on a desktop study and a rapid analysis of some 190 currently available tools and services for digital preservation, the deliverable provides a high-level view on the range of instruments currently on offer to support various functions within a preservation system.European Commission, FP7peer-reviewe

Managing Temporal Robot Constraints using Reachable Volumes

This project focuses on planning the motion for high degree of freedom manipulator robots under dynamic (or temporal) constraints. Manipulator robots are widely used in industry and are important because they can do jobs that are either too tedious or too dangerous for humans. An example would be picking up toxic waste or exploring underwater archeological sites. Motion planning for high degree of freedom (DOF) manipulators under task constraints is challenging because it gives rise to high dimensional configuration spaces (C-space) that are complex in structure. Our approach reduces the complexity by re-parameterizing the manipulator robots DOFs into a space that contains the valid regions that the end effector of the robot can reach, known as the Reachable Volume space (RV-space). In this way, we can sample valid configurations in Cspace in linear time with the number of DOFs of the manipulator. Current Reachable Volume theory only handles permanent constraints and cannot adapt to scenarios that require constraints that are enabled at certain times in the problem and disabled at other times. For example, when a manipulator grabs an object, closure constraints on the grasper must be satisfied, but when the object is to be dropped, these constraints must be ignored. Additionally, certain scenarios require the cooperation of multiple robots. This is obvious if we consider problems that involve objects that are too large for a single robot to handle. In this work, we produce a working computational framework for efficient motion planning of high degree of freedom manipulator arms under dynamic constraints through the extension of existing work in Reachable Volume spaces

Value Iteration Networks on Multiple Levels of Abstraction

Learning-based methods are promising to plan robot motion without performing extensive search, which is needed by many non-learning approaches. Recently, Value Iteration Networks (VINs) received much interest since---in contrast to standard CNN-based architectures---they learn goal-directed behaviors which generalize well to unseen domains. However, VINs are restricted to small and low-dimensional domains, limiting their applicability to real-world planning problems. To address this issue, we propose to extend VINs to representations with multiple levels of abstraction. While the vicinity of the robot is represented in sufficient detail, the representation gets spatially coarser with increasing distance from the robot. The information loss caused by the decreasing resolution is compensated by increasing the number of features representing a cell. We show that our approach is capable of solving significantly larger 2D grid world planning tasks than the original VIN implementation. In contrast to a multiresolution coarse-to-fine VIN implementation which does not employ additional descriptive features, our approach is capable of solving challenging environments, which demonstrates that the proposed method learns to encode useful information in the additional features. As an application for solving real-world planning tasks, we successfully employ our method to plan omnidirectional driving for a search-and-rescue robot in cluttered terrain

Key Elements for Motion Planning Algorithms

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