3 research outputs found

    Webots Simulator for Lyapunov-based Cooperative Omnidirectional Mobile Robots Evaluation

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    A multi-robot system is a set of robots that share a common objective and collaborate to achieve it. Multi-robot systems can address distributed and complicated real-world issues more effectively in various industries, including logistics, transportation, and industrial manufacturing. The higher system performance because of the collaborative efforts of numerous robots provides a substantial possible benefit of using a multi- robot system rather than a single robot. The Lyapunov control approach is one advanced approach to coordinating multiple robots in warehouse logistics applications. The method has been successfully simulated on point masses. However, to be physically implemented in robotic devices still requires several processes that are not simple. The case study focuses on finding the possibility of a distributed cooperative mobile robot using a LiDAR sensor to recognize the ’friend’ robot and the obstacle. This paper shows that each robot can recognize well between its neighbor or obstacles and maintain its formation of 1 m during the trip with obstacle presence. The simulations were performed in Webots to verify the proposed algorithms. The mathematical analysis and the experiment prove the system’s stability by seeing the robots’ velocity

    Recent updates on the Maser Monitoring Organisation

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    The Maser Monitoring Organisation (M2O) is a research community of telescope operators, astronomy researchers and maser theoreticians pursuing a joint goal of reaching a deeper understanding of maser emission and exploring its variety of uses as tracers of astrophysical events. These proceedings detail the origin, motivations and current status of the M2O, as was introduced at the 2021 EVN symposium

    Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

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    In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security
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