首页|Research from University of Louisiana Has Provided New Data on Robotics (Investigating Suitable Combinations of Dynamic Models And Control Techniques For Offline Reinforcement Learning Based Navigation: Application Universal Omni-wheeled Robots)
Research from University of Louisiana Has Provided New Data on Robotics (Investigating Suitable Combinations of Dynamic Models And Control Techniques For Offline Reinforcement Learning Based Navigation: Application Universal Omni-wheeled Robots)
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Fresh data on robotics are presented in a new report. According to news reporting from the University of Louisiana by NewsRx journalists, research stated, “Omni-directional locomotion provides Wheeled Mobile Robots (WMR) with better maneuverability, and flexibility, which enhances their energy efficiency and dexterity.” Our news correspondents obtained a quote from the research from University of Louisiana: “Universal Omni-Wheels are one of the best categories of wheels that can be used to develop a WMR Amarasiri et. al., [1]. We study dynamic modeling and controllers for mobile robots to train in a Reinforcement Learning (RL) based navigation algorithm. RL tasks require copious amounts of learning iteration episodes, which makes training very time-consuming. The choice of dynamic model and controller have significant impact on training time. In this paper, we compare a traditional Kane’s equations model to a non-holonomic canonical momenta model [2]. We implemented four controllers: Proportional Integral Derivative (PID), Linear Quadratic Regulator with Integral action (LQI), pole placement, and a full nonlinear Sliding Mode Controller (SMC).”
University of LouisianaEmerging TechnologiesMachine LearningNano-robotReinforcement LearningRobotics
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