4,952 research outputs found

    A novel optical apparatus for the study of rolling contact wear/fatigue based on a high-speed camera and multiple-source laser illumination

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    Rolling contact wear/fatigue tests on wheel/rail specimens are important to produce wheels and rails of new materials for improved lifetime and performance, which are able to operate in harsh environments and at high rolling speeds. This paper presents a novel non-invasive, all-optical system, based on a high-speed video camera and multiple laser illumination sources, which is able to continuously monitor the dynamics of the specimens used to test wheel and rail materials, in a laboratory test bench. 3D macro-topography and angular position of the specimen are simultaneously performed, together with the acquisition of surface micro-topography, at speeds up to 500 rpm, making use of a fast camera and image processing algorithms. Synthetic indexes for surface micro-topography classification are defined, the 3D macro-topography is measured with a standard uncertainty down to 0.019 mm, and the angular position is measured on a purposely developed analog encoder with a standard uncertainty of 2.9°. The very small camera exposure time enables to obtain blur-free images with excellent definition. The system will be described with the aid of end-cycle specimens, as well as of in-test specimens

    Collaborative SLAM using a swarm intelligence-inspired exploration method

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    Master's thesis in Mechatronics (MAS500)Efficient exploration in multi-robot SLAM is a challenging task. This thesis describes the design of algorithms that would enable Loomo robots to collaboratively explore an unknown environment. A pose graph-based SLAM algorithm using the on-board sensors of the Loomo was developed from scratch. A YOLOv3-tiny neural network has been trained to recognize other Loomos, and an exploration simulation has been developed to test exploration methods. The bots in the simulation are controlled using swarm intelligence inspired rules. The system is not finished, and further workis needed to combine the work done in the thesis into a collaborative SLAM system that runs on the Loomo robots

    Vision during manned booster operation Final report

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    Retinal images and accomodation control mechanism under conditions of space flight stres

    Effects of rapid decompression and exposure to bright light on visual function in black rockfish (Sebastes melanops) and Pacific halibut (Hippoglossus stenolepis)

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    Demersal fishes hauled up from depth experience rapid decompression. In physoclists, this can cause overexpansion of the swim bladder and resultant injuries to multiple organs (barotrauma), including severe exophthalmia (“pop-eye”). Before release, fishes can also be subjected to asphyxia and exposure to direct sunlight. Little is known, however, about possible sensory deficits resulting from the events accompanying capture. To address this issue, electroretinography was used to measure the changes in retinal light sensitivity, flicker fusion frequency, and spectral sensitivity in black rockfish (Sebastes melanops) subjected to rapid decompression (from 4 atmospheres absolute [ATA] to 1 ATA) and Pacific halibut (Hippoglossus stenolepis) exposed to 15 minutes of simulated sunlight. Rapid decompression had no measurable influence on retinal function in black rockfish. In contrast, exposure to bright light significantly reduced retinal light sensitivity of Pacific halibut, predominately by affecting the photopigment which absorbs the green wavelengths of light (≈520–580 nm) most strongly. This detriment is likely to have severe consequences for postrelease foraging success in green-wavelength-dominated coastal waters. The visual system of Pacific halibut has characteristics typical of species adapted to low light environments, and these characteristics may underlie their vulnerability to injury from exposure to bright light

    Intelligent mobile sensor system for drum inspection and monitoring: Topical report, October 1, 1993--April 22, 1995

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    Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning

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    Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, the autonomous control of such robots is still a big open challenge due to the high number of actuators and the complex manoeuvres required. To address this problem, we investigate the usage of Deep Reinforcement Learning for achieving autonomous navigation of in-pipe robots in pipeline networks with complex topologies. Moreover, we introduce a hierarchical policy decomposition based on Hierarchical Reinforcement Learning to learn robust high-level navigation skills. We show that the hierarchical structure introduced in the policy is fundamental for solving the navigation task through pipes and necessary for achieving navigation performances superior to human-level control
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