126,359 research outputs found

    AWARE: Platform for Autonomous self-deploying and operation of Wireless sensor-actuator networks cooperating with unmanned AeRial vehiclEs

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    This paper presents the AWARE platform that seeks to enable the cooperation of autonomous aerial vehicles with ground wireless sensor-actuator networks comprising both static and mobile nodes carried by vehicles or people. Particularly, the paper presents the middleware, the wireless sensor network, the node deployment by means of an autonomous helicopter, and the surveillance and tracking functionalities of the platform. Furthermore, the paper presents the first general experiments of the AWARE project that took place in March 2007 with the assistance of the Seville fire brigades

    Towards a Smart World: Hazard Levels for Monitoring of Autonomous Vehicles’ Swarms

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    This work explores the creation of quantifiable indices to monitor the safe operations and movement of families of autonomous vehicles (AV) in restricted highway-like environments. Specifically, this work will explore the creation of ad-hoc rules for monitoring lateral and longitudinal movement of multiple AVs based on behavior that mimics swarm and flock movement (or particle swarm motion). This exploratory work is sponsored by the Emerging Leader Seed grant program of the Mineta Transportation Institute and aims at investigating feasibility of adaptation of particle swarm motion to control families of autonomous vehicles. Specifically, it explores how particle swarm approaches can be augmented by setting safety thresholds and fail-safe mechanisms to avoid collisions in off-nominal situations. This concept leverages the integration of the notion of hazard and danger levels (i.e., measures of the “closeness” to a given accident scenario, typically used in robotics) with the concept of safety distance and separation/collision avoidance for ground vehicles. A draft of implementation of four hazard level functions indicates that safety thresholds can be set up to autonomously trigger lateral and longitudinal motion control based on three main rules respectively based on speed, heading, and braking distance to steer the vehicle and maintain separation/avoid collisions in families of autonomous vehicles. The concepts here presented can be used to set up a high-level framework for developing artificial intelligence algorithms that can serve as back-up to standard machine learning approaches for control and steering of autonomous vehicles. Although there are no constraints on the concept’s implementation, it is expected that this work would be most relevant for highly-automated Level 4 and Level 5 vehicles, capable of communicating with each other and in the presence of a monitoring ground control center for the operations of the swarm

    A mosaic of eyes

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    Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties

    A Control Framework for Autonomous Smart Grids for Space Power Applications

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    With the National Aeronautics and Space Administration's (NASA) rising interest in lunar surface operations and deep space exploration, there is a growing need to move from traditional ground-based mission operations to more autonomous vehicle level operations. In lunar surface operations, there are periods of time where communications with ground-based mission control could not occur, forcing vehicles and a lunar base to completely operate independent of the ground. For deep space exploration missions, communication latency times increase to greater than 15 minutes making real-time control of critical systems difficult, if not near impossible. These challenges are driving the need for an autonomous power control system that has the capability to manage power and energy. This will ensure that critical loads have the necessary power to support life systems and carry out critical mission objectives. This paper presents a flexible, hierarchical, distributed control methodology that enables autonomous operation of smart grids and can integrate into a higher level autonomous architecture

    Navigation Doppler Lidar for Autonomous Ground, Aerial, and Space Vehicles

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    A Doppler lidar instrument has been developed and demonstrated for providing critical vector velocity and altitude/range data for autonomous precision navigation. Utilizing advanced component technologies, this lidar can be adapted to different types of vehicles

    A GNSS Integrity Augmentation System for Ground Vehicle Operations

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    The employment of GNSS for the navigation of autonomous ground vehicles has so far been applied to mining operations in Australia. Autonomous systems enable the development of navigation strategies such as global path-planning and path optimization for vehicle fleets, thereby lowering overall carbon emissions. Furthermore, autonomous ground vehicle operations can significantly improve safety ratings by eliminating human error arising from stress, fatigue and boredom. Widespread use of GNSS-based autonomous vehicles for ground operations is presently hindered by stringent safety regulations. This places strict integrity requirements on GNSS receivers, which must be able to detect GNSS signal errors and faults, and alert the navigation system in a timely manner. An integrity augmentation system is presented in this paper that can detect GNSS error sources and faults, and alert the navigation system of an autonomous ground vehicle in a timely manner. The system is developed by modelling GNSS error sources like antenna masking, signal attenuation and multipath and assigning threshold values for generating integrity alerts. The performance of the system in terms of GNSS fault detection is validated through a realistic simulation in a 3-D virtual ground environment. Trajectories representing the paths followed by vehicles are generated using a dynamic model of a generic fourwheeled ground vehicle. The integrity augmentation system was demonstrated to successfully detect GNSS errors and respond by issuing predictive (caution flags) and reactive (warning flags) in a timely manner for a range of trajectories and maneuvers
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