首页|Recent Studies from Russian University of Transport Add New Data to Robotics (Ki nematic analysis, methods and experimental studies of robot navigator movements on mecanum wheels)
Recent Studies from Russian University of Transport Add New Data to Robotics (Ki nematic analysis, methods and experimental studies of robot navigator movements on mecanum wheels)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Investigators publish new report on robotics. Acc ording to news reporting out of Russian University of Transport by NewsRx editor s, research stated, “Machine learning and artificial intelligence, capable of ma king decisions on their own, have become indispensable tools for optimizing ware house operations. The use of lifting equipment on a mobile platform on mechanica l wheels in warehouses has a high degree of practical implementation, since the platform in question can move in any direction without U-turns.” The news correspondents obtained a quote from the research from Russian Universi ty of Transport: “Kinematic studies of the movement of the robotic arm on the pl atform under consideration make it possible to opti-mize its movement and behavi or in conditions of movement in a confined space with movable and fixed obstacle s, which, from the standpoint of creating a mathematical model of platform contr ol, are input parameters that determine the operating conditions of the platform . The methodology for conducting experimental studies and their analysis allow y ou to optimize the management and behavior of the platform in practice. The plat form movement algorithm is developed taking into account its features and is des igned to select the optimal route and navigation accuracy under changing space c onditions (moving and stationary objects). In order to verify and evaluate the e ffectiveness of the localization and path planning algorithm in order to improve the control system of the mobile platform on mecanum wheels, modeling and simul ation of the presented model were carried out, which made it possible to assess the kinematic parameters of the platform to achieve better results. The kinemati c analysis made it possible to carry out a qualitative and quantitative assessme nt of the features of the movement of the platform behavior in space with variou s input data. Modeling and simulation of the path localization and planning algo rithm made it possible to test its effectiveness in various operating conditions .”
Russian University of TransportAlgorit hmsEmerging TechnologiesMachine LearningRobotRobotics
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