72 research outputs found

    Centrifugal forming and mechanical properties of silicone-based elastomers for soft robotic actuators

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    This thesis describes the centrifugal forming and resulting mechanical properties of silicone-based elastomers for the manufacture of soft robotic actuators. This process is effective at removing bubbles that get entrapped within 3D-printed, enclosed molds. Conventional methods for rapid prototyping of soft robotic actuators to remove entrapped bubbles typically involve degassing under vacuum, with open-faced molds that limit the layout of formed parts to raised 2D geometries. As the functionality and complexity of soft robots increase, there is a need to mold complete 3D structures with controlled thicknesses or curvatures on multiples surfaces. In addition, characterization of the mechanical properties of common elastomers for these soft robots has lagged the development of new designs. As such, relationships between resulting material properties and processing parameters are virtually non-existent. One of the goals of this thesis is to provide guidelines and physical insights to relate the design, processing conditions, and resulting properties of soft robotic components to each other. Centrifugal forming with accelerations on the order of 100 g’s is capable of forming bubble-free, true 3D components for soft robotic actuators, and resulting demonstrations in this work include an aquatic locomotor, soft gripper, and an actuator that straightens when pressurized. Finally, this work shows that the measured mechanical properties of 3D geometries fabricated within enclosed molds through centrifugal forming possess comparable mechanical properties to vacuumed materials formed from open-faced molds with raised 2D features.M.S.Includes bibliographical referencesby Parth Kulkarn

    An Intelligent Versatile Pipeline for 6D Localization of Industrial Components in a Production Environment

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    Robots in manufacturing need a precise description of the position and orientation of a component to execute the planned tasks. Often, a component’s position is not fixed and cannot be directly taught to the robot. Its position and orientation must then be determined dynamically before starting the process. Solutions addressing this problem are mainly developed using traditional computer vision approaches and cannot be transferred to other use cases or even components in many cases. Using AI-assisted robotics, an intelligent and versatile pipeline is presented in this work. It can simplify the 6D localization task into smaller modular tasks which can be adapted to multiple use cases for any desired component

    Characterization and optimization of UAV power system for aerial and submersible multi-medium multirotor vehicle

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    Even as an emerging technology, Unmanned Aerial Vehicles (UAVs) have had a tremendous impact on the world. From the way wars are fought, to the way we take selfies, drones are well on their way to revolutionizing our daily lives. One of the most innovative applications of these vehicles in the Naviator submersible-UAV. This unique multirotor is capable of aerial flight and underwater operations with seamless Air-Water transitions. In this thesis, the power system of a multirotor UAS is characterized using standard performance models with the goal of designing and optimizing the systems of a new Naviator V5 prototype. Test beds were created to collect data on BLDC motors and propellers and their performance was assessed in air and water. Theoretical models using BEM theory and the 3-constant motor model were validated for their accuracy. Experiments found that RC air propellers are similarly efficient in air and water and BLDC motor performance is partially diminished due to the higher viscosity of water. The effects of input voltage, throttle, Kv rating, and motor size were also evaluated using motor torque curves. Using this data, an optimal power system for the Naviator V5 prototype was designed, tested, and evaluated.M.S.T.Includes bibliographical referencesby Parth V. Son

    Evacuation of Kobylnice village in extraordinary event

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    Cílem této práce je sestavení evakuačního plánu pro obec Kobylnice. Teoretická část přibližuje problematiku mimořádných událostí a možných následků, zpracovává dostupnou literaturu a uvádí zákony a vyhlášky spojené s evakuačním plánováním. V praktické části se autor zmiňuje o programech, které jsou použitelné při mimořádných a krizových situacích a sestavuje plán evakuace.The aim of this work is evacuation plan setup for Kobylnice Corporation. The theoretical parth explains issue of extraordinary events and possible consequences, processes available information and shows the laws and public notices are being used in evacuative planning. In the practical parth author presents programs, which are applicability to extraordinary events and crisis situations. She draws a plan of evacuation.Institut bezpečnostních technologiíobhájen

    Foundations of the law on industrial organisations in Russia and the former republics of the USSR: 1985-1990

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    The industrial organisations introduced into the law of the USSR from 1987, and thereafter into the law of the former republics, developed upon a foundation that was rooted in Soviet law and was constructed during the period from 1985 to mid-1990. While this study focuses on the industrial economy, certain aspects of the agrarian economy, and in particular the early history and structure of the collective farm, are considered where appropriate. The thesis presents an entirely new understanding both of the nature of these developments and of the significance of the law on ownership. The foundations of the law on industrial organisations are conceptualised within specific heuristic models which are elaborated in an attempt to consolidate and highlight the key steps in this history. It is argued that Soviet law did not contain a concept of the "generic owner" or a developed understanding of the ownership of a juridical person, in particular by multiple owners holding "ownership interests" of that juridical person; and that their absence critically impaired a rational and coherent structure for the foundations of the law on industrial organisations both within the Stalin economic settlement and the new economic constitution of 1990

    Synthetic data generation procedures for domain-specific environments in manufacturing

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    16681679Synthetic data is being used lately for training deep neural networks in computer vision applications such as object detection, object segmentation and 6D object pose estimation. Domain randomization hereby plays an important role in reducing the simulation to reality gap. However, this generalization might not always be effective in specialized domains like manufacturing that involve complex assemblies. Individual parts are integrated in much larger assemblies making them indistinguishable from their counterparts. Moreover, individual parts are often partially occluded in the scene. These situations give rise to wrong detections. Target domain knowledge is vital in these cases and if conceived effectively while generating synthetic data, can show a considerable improvement in bridging the simulation to reality gap. This paper validates synthetic data generation procedures through practical experimentation ensuring that experiments are both comprehensive and reproducible. After combining domain randomization and domain adaptation procedures for parts and assemblies used in manufacturing the model performance improves by up to 15% than the state-of-the-art domain randomization techniques. Reducing the simulation to reality gap in this way can unlock the true potential of robot-assisted production using artificial intelligence.25

    Synthetic data generation procedures for domain-specific environments in manufacturing

    No full text
    Synthetic data is being used lately for training deep neural networks in computer vision applications such as object detection, object segmentation and 6D object pose estimation. Domain randomization hereby plays an important role in reducing the simulation to reality gap. However, this generalization might not always be effective in specialized domains like manufacturing that involve complex assemblies. Individual parts are integrated in much larger assemblies making them indistinguishable from their counterparts. Moreover, individual parts are often partially occluded in the scene. These situations give rise to wrong detections. Target domain knowledge is vital in these cases and if conceived effectively while generating synthetic data, can show a considerable improvement in bridging the simulation to reality gap. This paper validates synthetic data generation procedures through practical experimentation ensuring that experiments are both comprehensive and reproducible. After combining domain randomization and domain adaptation procedures for parts and assemblies used in manufacturing the model performance improves by up to 15% than the state-of-the-art domain randomization techniques. Reducing the simulation to reality gap in this way can unlock the true potential of robot-assisted production using artificial intelligence
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