4,952 research outputs found
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Improving the safety and efficiency of rail yard operations using robotics
textSignificant efforts have been expended by the railroad industry to make operations safer and more efficient through the intelligent use of sensor data. This work proposes to take the technology one step further to use this data for the control of physical systems designed to automate hazardous railroad operations, particularly those that require humans to interact with moving trains. To accomplish this, application specific requirements must be established to design self-contained machine vision and robotic solutions to eliminate the risks associated with existing manual operations. Present-day rail yard operations have been identified as good candidates to begin development. Manual uncoupling, in particular, of rolling stock in classification yards has been investigated. To automate this process, an intelligent robotic system must be able to detect, track, approach, contact, and manipulate constrained objects on equipment in motion. This work presents multiple prototypes capable of autonomously uncoupling full-scale freight cars using feedback from its surrounding environment. Geometric image processing algorithms and machine learning techniques were implemented to accurately identify cylindrical objects in point clouds generated in real-vi time. Unique methods fusing velocity and vision data were developed to synchronize a pair of moving rigid bodies in real-time. Multiple custom end-effectors with in-built compliance and fault tolerance were designed, fabricated, and tested for grasping and manipulating cylindrical objects. Finally, an event-driven robotic control application was developed to safely and reliably uncouple freight cars using data from 3D cameras, velocity sensors, force/torque transducers, and intelligent end-effector tooling. Experimental results in a lab setting confirm that modern robotic and sensing hardware can be used to reliably separate pairs of rolling stock up to two miles per hour. Additionally, subcomponents of the autonomous pin-pulling system (APPS) were designed to be modular to the point where they could be used to automate other hazardous, labor-intensive tasks found in U.S. classification yards. Overall, this work supports the deployment of autonomous robotic systems in semi-unstructured yard environments to increase the safety and efficiency of rail operations.Mechanical Engineerin
A novel optical apparatus for the study of rolling contact wear/fatigue based on a high-speed camera and multiple-source laser illumination
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
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
Retinal images and accomodation control mechanism under conditions of space flight stres
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Standardization for intelligent detection and autonomous operation of non-structured hardware, and its application on railcar brake release operation
textThis thesis introduces a standard framework for evaluating and planning for desired autonomous (or semi-autonomous) operations, then applies the framework, in detail, to the task of automating emergency brake release before rail-car decoupling. A significant hurdle to be accounted for is the lack of standardization of much of the hardware of interest in industry. Non-standardized rail car components must be formally structured as fully as possible to improve the reliability of the robotic automation. This brake release task requires either pushing or pulling a “bleed rod” that protrudes from the side of each rail car. The requirements for each step of the evaluation and planning process will be laid out in this thesis, as an example of how it should be applied to future automation tasks.Mechanical Engineerin
Effects of rapid decompression and exposure to bright light on visual function in black rockfish (Sebastes melanops) and Pacific halibut (Hippoglossus stenolepis)
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
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
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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