Practical work is one of the most important instructional tools in control engineering. To address concerns linked to the cost and space requirements of traditional hands-on laboratories, technology-enabled laboratory modes, such as virtual, remote, and take-home laboratory modes are proposed. Each of these alliterative laboratory modes has its own set of benefits and emphasizes a distinct learning goal. Furthermore, due to lockdown and physical proximity restrictions imposed by policies in response to the COVID-19 pandemic, the employment of these alliterative laboratory modes has been quickly increasing. The laboratories' development, operation, and maintenance become more fragmented as a result of these many possibilities. In this study, we propose "ReImagine Lab" as a framework for leveraging digital twins and extended reality technologies to streamline the development and operation of hands-on, virtual, and distant laboratories. By increasing the level of interaction, immersion, and collaboration in technologyenabled laboratory forms, this framework intends to boost student engagement. The benefits of this framework are demonstrated by examining several use cases, and a 37-person "system usability study" is conducted to assess the usability of virtual laboratories employing desktop computers and immersive virtual reality.
Hybrid mobile robots are able to function in a number of different modes of locomotion, which increases their capacity to overcome challenges and makes them appropriate for a wide range of applications. To be able to develop navigation techniques that make use of these improved capabilities, one must first have a solid grasp of the constraints imposed by each of those different modalities of locomotion. In this paper, we present a data-driven approach for evaluating the robots’ locomotion modes. To do this, we formalize the problem as a reinforcement learning task that is applied to a digital twin simulation of the mobile robot. The proposed method is demonstrated through the use of a case study that examines the capabilities of hybrid wheel-on-leg robot locomotion modes in terms of speed, slope ascent, and step obstacle climbing. First, a comprehensive explanation of the process of creating the digital twin of the mobile robot through the use of the Unity gaming engine is presented. Second, a description of the construction of three test environments is provided so that the aforementioned capabilities of the robot can be evaluated. In the end, Reinforcement Learning is used to evaluate the two types of locomotion that the mobile robot can utilize in each of these different environments. Corresponding simulations are conducted in the virtual environment and the results are analyzed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.