21,633 research outputs found

    Automation and robotics for the Space Exploration Initiative: Results from Project Outreach

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    A total of 52 submissions were received in the Automation and Robotics (A&R) area during Project Outreach. About half of the submissions (24) contained concepts that were judged to have high utility for the Space Exploration Initiative (SEI) and were analyzed further by the robotics panel. These 24 submissions are analyzed here. Three types of robots were proposed in the high scoring submissions: structured task robots (STRs), teleoperated robots (TORs), and surface exploration robots. Several advanced TOR control interface technologies were proposed in the submissions. Many A&R concepts or potential standards were presented or alluded to by the submitters, but few specific technologies or systems were suggested

    Architecture and Information Requirements to Assess and Predict Flight Safety Risks During Highly Autonomous Urban Flight Operations

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    As aviation adopts new and increasingly complex operational paradigms, vehicle types, and technologies to broaden airspace capability and efficiency, maintaining a safe system will require recognition and timely mitigation of new safety issues as they emerge and before significant consequences occur. A shift toward a more predictive risk mitigation capability becomes critical to meet this challenge. In-time safety assurance comprises monitoring, assessment, and mitigation functions that proactively reduce risk in complex operational environments where the interplay of hazards may not be known (and therefore not accounted for) during design. These functions can also help to understand and predict emergent effects caused by the increased use of automation or autonomous functions that may exhibit unexpected non-deterministic behaviors. The envisioned monitoring and assessment functions can look for precursors, anomalies, and trends (PATs) by applying model-based and data-driven methods. Outputs would then drive downstream mitigation(s) if needed to reduce risk. These mitigations may be accomplished using traditional design revision processes or via operational (and sometimes automated) mechanisms. The latter refers to the in-time aspect of the system concept. This report comprises architecture and information requirements and considerations toward enabling such a capability within the domain of low altitude highly autonomous urban flight operations. This domain may span, for example, public-use surveillance missions flown by small unmanned aircraft (e.g., infrastructure inspection, facility management, emergency response, law enforcement, and/or security) to transportation missions flown by larger aircraft that may carry passengers or deliver products. Caveat: Any stated requirements in this report should be considered initial requirements that are intended to drive research and development (R&D). These initial requirements are likely to evolve based on R&D findings, refinement of operational concepts, industry advances, and new industry or regulatory policies or standards related to safety assurance

    Establishing a Multibeam Sonar Evaluation Test Bed near Sidney, British Columbia

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    The Canadian Hydrographic Service (CHS), Naval Oceanographic Office (NAVOCEANO) and the Ocean Mapping Group of the University of New Brunswick (OMG) collaborated on establishing a multibeam sonar test bed in the vicinity of the Institute of Ocean Sciences in Sidney, British Columbia Canada. This paper describes the purpose of the sonar evaluation test bed, the trials and tribulations of two foreign governments collaborating on projects of mutual interest, the evaluation areas and their characteristics for sonar testing, and sample results of sonar evaluations using this test bed. Some target detection comparisons of several systems over a range of artificial sonar targets will also be given

    Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

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    This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

    Real-Time Implementation of Vision-Aided Monocular Navigation for Small Fixed-Wing Unmanned Aerial Systems

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    The goal of this project was to develop and implement algorithms to demonstrate real-time positioning of a UAV using a monocular camera combined with previously collected orthorectified imagery. Unlike previous tests, this project did not utilize a full inertial navigation system (INS) for attitude, but instead had to rely on the attitude obtained by inexpensive commercial off-the-shelf (COTS) autopilots. The system consisted of primarily COTS components and open-source software, and was own over Camp Atterbury, IN for a sequence of flight tests in Fall 2015. The system obtained valid solutions over much of the flight path, identifying features in the flight image, matching those features with a database of features, and then solving both the 6DOF solution, and an attitude-aided 3DOF solution. The tests demonstrated that such attitude aiding is beneficial, since the horizontal DRMS of the 6DOF solution was 59m, whereas the 3DOF solution DRMS was 15m. Post processing was done to improve the algorithm to correct for system errors, obtaining a 3DOF solution DRMS of 8.22 meters. Overall, this project increased our understanding of the capabilities and limitations of real-time vision-aided navigation, and demonstrated that such navigation is possible on a relatively small platform with limited computational power

    Precision Navigation Using Pre-Georegistered Map Data

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    Navigation performance in small unmanned aerial vehicles (UAVs) is adversely affected by limitations in current sensor technology for small, lightweight sensors. Because most UAVs are equipped with cameras for mission-related purposes, it is advantageous to utilize the camera to improve the navigation solution. This research improves navigation by matching camera images to a priori georegistered image data and combining this update with existing image-aided navigation technology. The georegistration matching is done by projecting the images into the same plane, extracting features using the techniques Scale Invariant Feature Transform (SIFT) [5] and Speeded-Up Robust Features (SURF) [3]. The features are matched using the Random Scale and Consensus (RANSAC) [4] algorithm, which generates a model to transform feature locations from one image to another. In addition to matching the image taken by the UAV to the stored images, the effect of matching the images after transforming one to the perspective of the other is investigated. One of the chief advantages of this method is the ability to provide both an absolute position and attitude update. Test results using 15 minutes of aerial video footage at altitudes ranging from 1000m to 1500m demonstrated that transforming the image data from one perspective to the other yields an improvement in performance. The best system configuration uses SIFT on an image that was transformed into the satellite perspective and matched to satellite map data. This process is able to achieve attitude errors on the order of milliradians, and position errors on the order of a few meters vertically. The along track, cross track, and heading errors are higher than expected. Further work is needed on reliability. Once this is accomplished, it should improve the navigation solution of an aircraft, or even provide navigation grade position and attitude estimates in a GPS denied environment
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