Department of Mechanical Engineering

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https://hdl.handle.net/10919/23261
The Virginia Tech Mechanical Engineering Department serves its students, alumni, the Commonwealth of Virginia, and the nation through a variety of academic, research and service activities. Our missions are to: holistically educate our students for professional leadership as creative problem-solvers in a diverse society, conduct advanced research for societal advancement, train graduate students for scholarly inquiry, and engage with alumni, industry, government, and community partners through outreach activities. In order to produce engineers prepared for success across a range of career paths, our academic program integrates training in engineering principles, critical thinking, hands-on projects, open-ended problem solving, and the essential skills of teamwork, communication, and ethics.

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Browsing Department of Mechanical Engineering by Subject "0906 Electrical and Electronic Engineering"

Now showing 1 - 5 of 5
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    An Autonomous Task Assignment Paradigm for Autonomous Robotic In-Space Assembly
    Hildebrand, Robert; Komendera, Erik; Moser, Joshua; Hoffman, Julia (Frontiers, 2022-02-25)
    The development of autonomous robotic systems is a key component in the expansion of space exploration and the development of infrastructures for in-space applications. An important capability for these robotic systems is the ability to maintain and repair structures in the absence of human input by autonomously generating valid task sequences and task to robot allocations. To this end, a novel stochastic problem formulation paired with a mixed integer programming assembly schedule generator has been developed to articulate the elements, constraints, and state of an assembly project and solve for an optimal assembly schedule. The developed formulations were tested with a set of hardware experiments that included generating an optimal schedule for an assembly and rescheduling during an assembly to plan a repair. This formulation and validation work provides a path forward for future research in the development of an autonomous system capable of building and maintaining in-space infrastructures.
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    Comparative Analysis of Emergency Evasive Steering for Long Combination Vehicles
    Chen, Yang; Zhang, Zichen; Ahmadian, Mehdi (SAE International, 2020-10-10)
    This study provides a simulation-based comparative analysis of the distance and time needed for long combination vehicles (LCVs) - namely, A-doubles with 28-, 33-, and 48-ft trailers - to safely exercise an emergency, evasive steering maneuver such as required for obstacle avoidance. The results are also compared with conventional tractor-semitrailers with a single 53-ft trailer. A multi-body dynamic model for each vehicle combination is developed in TruckSim® with an attempt to assess the last point to steer (LPTS) and evasive time (ET) at various highway speeds under both dry and wet road conditions. The results indicate that the minimum avoidance distance and time required for the 28-ft doubles vary from 206 ft (60 mph) to 312 ft (80 mph) and 2.3 s to 2.6 s, respectively. The required LPTS represents a 6% to 31% increase when compared with 53-ft semitrucks. When driving below 76 mph on a dry road and below 75 mph on a wet road, the 28-ft doubles exhibit LPTS and ET that are larger than 33-ft doubles. In addition, the 33-ft doubles exhibit larger LPTS and ET than 48-ft doubles for the highway speeds considered. This is mainly attributed to the longer trailer wheelbase that causes smaller rear trailer amplifications. At speeds higher than 76 mph on dry roads and 75 mph on wet roads, however, an opposite trend is observed. As the trailer length increases, the distance and time needed to safely avoid an obstacle also increase. A comparison between dry and wet road conditions is also conducted, with the results indicating that more time and distance would be needed for obstacle avoidance on wet roads.
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    Design and Preliminary Evaluation of Two Tool Support Arm Exoskeletons for Gravity Compensation
    Hull, Joshua; Turner, Ranger; Asbeck, Alan T. (2022-03)
    We present two new arm exoskeletons based on a pantograph linkage. The exoskeletons can support a tool or load held in the hand of up to 9 kg. The pantograph linkage follows the geometry of a user’s upper arm and forearm, and supports the held mass with any arm orientation. The downward force of the load weight is supported by a fulcrum near the user’s shoulder and balanced by a countering downward force behind the user’s back. We present a Passive exoskeleton, which uses a gas spring mechanism to supply the downward force, and an Active exoskeleton that uses a motor. We analyze the forces produced by the gas spring mechanism as its geometry is varied, and explore different possible arrangements of the gas spring mechanism or motor relative to the pantograph linkage. We derive the equations governing the pantograph linkage, and simulate the forces resulting from the two exoskeletons. We conduct measurements of the forces produced by each exoskeleton, and compare them with the simulated forces.
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    Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences
    Bıyık, Erdem; Losey, Dylan P.; Palan, Malayandi; Landolfi, Nicholas C.; Shevchuk, Gleb; Sadigh, Dorsa (SAGE, 2022-01)
    Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers. Importantly, data from human teachers can be collected either passively or actively in a variety of forms: passive data sources include demonstrations (e.g., kinesthetic guidance), whereas preferences (e.g., comparative rankings) are actively elicited. Prior research has independently applied reward learning to these different data sources. However, there exist many domains where multiple sources are complementary and expressive. Motivated by this general problem, we present a framework to integrate multiple sources of information, which are either passively or actively collected from human users. In particular, we present an algorithm that first utilizes user demonstrations to initialize a belief about the reward function, and then actively probes the user with preference queries to zero-in on their true reward. This algorithm not only enables us combine multiple data sources, but it also informs the robot when it should leverage each type of information. Further, our approach accounts for the human’s ability to provide data: yielding user-friendly preference queries which are also theoretically optimal. Our extensive simulated experiments and user studies on a Fetch mobile manipulator demonstrate the superiority and the usability of our integrated framework.
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    Physical interaction as communication: Learning robot objectives online from human corrections
    Losey, Dylan P.; Bajcsy, Andrea; O'Malley, Marcia K.; Dragan, Anca D. (SAGE, 2021-10-25)
    When a robot performs a task next to a human, physical interaction is inevitable: the human might push, pull, twist, or guide the robot. The state of the art treats these interactions as disturbances that the robot should reject or avoid. At best, these robots respond safely while the human interacts; but after the human lets go, these robots simply return to their original behavior. We recognize that physical human–robot interaction (pHRI) is often intentional: the human intervenes on purpose because the robot is not doing the task correctly. In this article, we argue that when pHRI is intentional it is also informative: the robot can leverage interactions to learn how it should complete the rest of its current task even after the person lets go. We formalize pHRI as a dynamical system, where the human has in mind an objective function they want the robot to optimize, but the robot does not get direct access to the parameters of this objective: they are internal to the human. Within our proposed framework human interactions become observations about the true objective. We introduce approximations to learn from and respond to pHRI in real-time. We recognize that not all human corrections are perfect: often users interact with the robot noisily, and so we improve the efficiency of robot learning from pHRI by reducing unintended learning. Finally, we conduct simulations and user studies on a robotic manipulator to compare our proposed approach with the state of the art. Our results indicate that learning from pHRI leads to better task performance and improved human satisfaction.