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Browsing by Author "Sebastian, Bijo"

Now showing 1 - 3 of 3
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    A Collision Avoidance Method Based on Deep Reinforcement Learning
    Feng, Shumin; Sebastian, Bijo; Ben-Tzvi, Pinhas (MDPI, 2021-05-19)
    This paper set out to investigate the usefulness of solving collision avoidance problems with the help of deep reinforcement learning in an unknown environment, especially in compact spaces, such as a narrow corridor. This research aims to determine whether a deep reinforcement learning-based collision avoidance method is superior to the traditional methods, such as potential field-based methods and dynamic window approach. Besides, the proposed obstacle avoidance method was developed as one of the capabilities to enable each robot in a novel robotic system, namely the Self-reconfigurable and Transformable Omni-Directional Robotic Modules (STORM), to navigate intelligently and safely in an unknown environment. A well-conceived hardware and software architecture with features that enable further expansion and parallel development designed for the ongoing STORM projects is also presented in this work. A virtual STORM module with skid-steer kinematics was simulated in Gazebo to reduce the gap between the simulations and the real-world implementations. Moreover, comparisons among multiple training runs of the neural networks with different parameters related to balance the exploitation and exploration during the training process, as well as tests and experiments conducted in both simulation and real-world, are presented in detail. Directions for future research are also provided in the paper.
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    A Robotic Head Stabilization Device for Medical Transport
    Williams, Adam; Sebastian, Bijo; Ben-Tzvi, Pinhas (MDPI, 2019-03-25)
    In this paper, the design and control of a robotic device intended to stabilize the head and neck of a trauma patient during transport are presented. When transporting a patient who has suffered a traumatic head injury, the first action performed by paramedics is typically to restrain and stabilize the head and cervical spine of a patient. The proposed device would drastically reduce the time required to perform this action while also freeing a first responder to perform other possibly lifesaving actions. The applications for robotic casualty extraction are additionally explored. The design and construction are described, followed by control simulations demonstrating the improved behavior of the chosen controller paradigm, linear active disturbance rejection control (LADRC). Finally, experimental validation is presented, followed by future work and directions for the research.
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    Traversability Estimation Techniques for Improved Navigation of Tracked Mobile Robots
    Sebastian, Bijo (Virginia Tech, 2019-10-17)
    The focus of this dissertation is to improve autonomous navigation in unstructured terrain conditions, with specific application to unmanned casualty extraction in disaster scenarios. Robotic systems are being widely employed for search and rescue applications, especially in disaster scenarios. But a majority of these are focused solely on the search aspect of the problem. This dissertation proposes a conceptual design of a Semi-Autonomous Victim Extraction Robot (SAVER) capable of safe and effective unmanned casualty extraction, thereby reducing the risk to the lives of first responders. In addition, the proposed design addresses the limitations of existing state-of-the-art rescue robots specifically in the aspect of head and neck stabilization as well as fast and safe evacuation. One of the primary capabilities needed for effective casualty extraction is reliable navigation in unstructured terrain conditions. Autonomous navigation in unstructured terrain, particularly for systems with tracked locomotion mode involves unique challenges in path planning and trajectory tracking. The dynamics of robot-terrain interaction, along with additional factors such as slip experienced by the vehicle, slope of the terrain, and actuator limitations of the robotic system, need to be taken into consideration. To realize these capabilities, this dissertation proposes a hybrid navigation architecture that employs a physics engine to perform fast and accurate state expansion inside a graph-based planner. Tracked skid-steer systems experience significant slip, especially while turning. This greatly affects the trajectory tracking accuracy of the robot. In order to enable efficient trajectory tracking in varying terrain conditions, this dissertation proposes the use of an active disturbance rejection controller. The proposed controller is capable of estimating and counter acting the effects of slip in real-time to improve trajectory tracking. As an extension of the above application, this dissertation also proposes the use of support vector machine architecture to perform terrain identification, solely based on the estimated slip parameters. Combining all of the above techniques, an overall architecture is proposed to assist and inform tele-operation of tracked robotic systems in unstructured terrain conditions. All of the above proposed techniques have been validated through simulations and experiments in indoor and simple outdoor terrain conditions.