CRUSER's TechCon

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Now showing 1 - 10 of 142
  • Publication
    Design and Evaluation of an Acoustic Homing System Integrating NAVY Relevant Research into Robotics Classroom
    (2018-04-18) Dobrokhodov, Vlad; Leary, Paul; Consortium for Robotics and Unmanned Systems Education and Research (CRUSER); Mechanical and Aerospace Engineering (MAE); Physics
  • Publication
    Considerations for Cross-Echelon Situational Awareness in a Manned-Unmanned Teaming Environment [video]
    (2018-04-18) Fout, Mike; Ploski, Jim; Consortium for Robotics and Unmanned Systems Education and Research (CRUSER)
  • Publication
    Coordinating a Multi-Organization Research and Development Program to Enable MDUSV Acquisition
    (2018-04-18) Gallup, Shelley; Dillard, John; Wood, Brian; MacKinnon, Doug; Irvine, Nelson; Garz, Bob; Consortium for Robotics and Unmanned Systems Education and Research (CRUSER)
  • Publication
    CRUSER TechCon 2018 Roster of speakers, Wednesday 2: Teaming
    (2018-04-18) Consortium for Robotics and Unmanned Systems Education and Research (CRUSER)
  • Publication
    Analysis & Experimentation of an Autonomous Aerial Manipulator Interaction with a Vertical Wall [video]
    (2017-04-12) Tavora, Bruno; Consortium for Robotics and Unmanned Systems Education and Research (CRUSER); Mechanical and Aerospace Engineering (MAE)
    The applications of unmanned aerial vehicles (UAVs) are usually limited to the exchange of information for tasks without physical interaction such as tracking, surveillance, mapping, and visual inspection. The use of UAVs equipped with robotic arms, capable of interacting with the environment, could significantly increase their capability. In recent years, a new area of research has been growing on the use of aerial manipulators for a larger variety of applications, such as physical inspection, maintenance, cleaning walls, and collecting objects in areas of difficult access. An important problem that has not been addressed by the academic community is the interaction between a multicopter equipped with a robotic arm and a vertical wall. There are several applications that would demand such study, like performing maintenance on a piece of a vertical equipment, cleaning a wall, opening a door knob, or rescuing from a tall building. However, there is no record in the literature of an aerial manipulator, with a more complex robotic arm, capable of producing forces or torques about any direction under interaction with a vertical obstacle. We have conducted analysis and experimentation on the aerial manipulation on a wall, taking advantage of an indoor laboratory facility and a multicopter equipped with a three-link robotic arm. Simulations and experiments have been implemented to generate desired force and torque against a vertical wall using a multi-layer control algorithm. To address difficulties caused by the near-wall effect, a near-wall effect model has been developed and experimentally verified. An overview of the wall-interaction controller and analysis on results from simulations and experiments will be provided.
  • Publication
    Runway Detection and Tracking for Autonomous Landing of a UAV [video]
    (2017-04-12) McCarthy, Tyler; Consortium for Robotics and Unmanned Systems Education and Research (CRUSER); Systems Engineering (SE)
    This presentation outlines an approach to develop a robust, real-time runway detection and tracking system for an unmanned aerial vehicle (UAV) using computer vision and standard guidance, navigation, and control (GNC) equipment. Feature extraction techniques, specifically the Hough transform, are used to identify the position and orientation of a runway from cameras on board an aircraft. Information relating the position of the aircraft and the runway is then integrated into a control system for the final approach and landing stages of UAV flight. While conceptual in nature, this presentation utilizes modeling and simulation to demonstrate the feasibility of a computer vision approach to an autoland capability for a UAV. This approach could also have potential applications in other autonomous fields by illustrating the practicality of vision-based feedback for guidance and control of unmanned autonomous systems.
  • Publication
    DIUx Innovation Loop - COTS+ for UAS [video]
    (2017-04-12) Petty, Clayton; Consortium for Robotics and Unmanned Systems Education and Research (CRUSER); Mechanical and Aerospace Engineering (MAE)
    The Defense Innovation Unit - Experimental (DIUx) is a Silicon Valley-based innovation effort that supports the Office of the Secretary of Defense to rapidly accelerate commercial techology for the Department of Defense. In that spirit, there are a variety of COTS technologies which can provide an outstanding tactical offset in the realm of UAS. However, many of these would benefit greatly from specific warfighter enhancements both in software and in hardware. Thus, a COTS+ innovation cycle should be explored. A short exemplar of this design loop is explored in this presentation as applied to blue-force UAS use downrange.
  • Publication
    Artificial Intelligence (AI) Driven Coastal Change Detection of Littoral Waters
    (2018-04-18) Orescanin, Mara; Young, Walt; Herrmann, Dave; Consortium for Robotics and Unmanned Systems Education and Research (CRUSER)
    Unmanned aerial vehicles (UAVs) are cost-effective platforms that can be used to quantify large-scale coastal change caused by extreme events, such as beach breaching, typically caused by significant storm events. The U.S. Navy and Marine Corps often conduct exercises and real world operations in littoral waters and the accompanying coastal landscape. Damage to DoD assets resulting from the passage of such events is difficult to predict and therefore developing quick and accurate methods to assess change, both to the coastal landscape and to coastal infrastructure, is essential. This project uses digital aerial imagery (visual and IR) from UAVs in conjunction with in-situ measurements of water/sand properties to develop a techniques to monitor and quantify coastal morphological and water quality response with the passage of extreme weather events. Specifically, UAV surveys were conducted at Carmel River State Beach, Carmel, CA, that has undergone significant morphological change due to mechanical and natural river breaching events. The Carmel River is an ephemeral river, characterized by periods of beach closure during dry months and periods of direct connection between the river and coastal ocean during wet months. During the seasonal transition from dry to wet, the River undergoes a series of breaching and closure events. These events are unpredictable, and are similar morphologically to breaches caused by storms on barrier beaches. Given that the River and Beach morphology are constantly changing, this location provides the opportunity to test UAV monitoring techniques. Large area images of the beach are compiled from UAV surveys to provide digital elevation maps (DEMs) of the beach. These DEMs are currently being analyzed to determine the amount of sediment transport during the breach-closure cycle. In addition, a deep learning computer neural net is being developed and trained on a coastal dataset with the intention of creating a change detection algorithm that will detect areas of greatest change from pre-storm to post-storm morphologies.
  • Publication
    Generalized Optimal Control for Networked Autonomous Vehicles in Uncertain Domains [video]
    (2017-04-12) Kragelund, Sean; Consortium for Robotics and Unmanned Systems Education and Research (CRUSER); Mechanical and Aerospace Engineering (MAE)
    Networked autonomous vehicles have great potential in a wide range of littoral sensing applications, including mine countermeasures (MCM), undersea warfare (USW), and intelligence, surveillance, and reconnaissance (ISR) missions. Motion planning algorithms which consider the capabilities and limitations of individual vehicle/sensor configurations are a key enabler for optimal employment of dissimilar vehicles to accomplish a given sensing objective. Optimal control is a model-based framework for solving motion planning problems with multiple vehicles, dynamic constraints, and complex performance objectives; although they usually require numerical solutions and deterministic formulations. Recent research at NPS, however, has produced a general mathematical and computational framework for numerically solving these problems, even in the face of parameter uncertainty: Generalized Optimal Control (GenOC). GenOC has been used to solve complex motion planning problems with multi-agent interactions, in applications ranging from optimal search to swarm defense and target herding behaviors. This presentation describes recent CRUSER supported research which utilized the GenOC framework to generate optimal search trajectories for multiple, dissimilar vehicles conducting MCM with different sonar systems. The resulting trajectories have been shown to outperform traditional lawnmower coverage patterns when detecting mines under time or resource constraints. Moreover, the ability to rapidly solve optimal search problems in this framework can establish performance benchmarks and provide important insights into optimal sensor and vehicle employment strategies. These capabilities are being developed into an experimental mission planning and analysis tool for the MCM community. We will also highlight how GenOC will be used to generate optimal vehicle trajectories for aerial, surface, and underwater vehicles during the upcoming CRUSER/JIFX multi-threaded experiment (MTX) at San Clemente Island in August, 2017. ScanEagle UAV trajectories will be devised to provide persistent aerial surveillance and mesh network connectivity in support of blue team operations, while also guarding against potential red team incursions. Meanwhile, SeaFox USV trajectories will implement search patterns to detect surface threats with radar, while relaying communications between ScanEagle aircraft and REMUS UUVs executing optimal sonar search patterns.
  • Publication
    2017_04 CRUSER Technical Continuum (TechCon) 2017
    (Monterey, California: Naval Postgraduate School, 2017-04) Consortium for Robotics and Unmanned Systems Education and Research (CRUSER); Naval Postgraduate School (U.S.)