首页|Findings from Shihezi University in Robotics Reported (Multiobjective Energy Con sumption Optimization of a Flying-Walking Power Transmission Line Inspection Rob ot during Flight Missions Using Improved NSGA-II)
Findings from Shihezi University in Robotics Reported (Multiobjective Energy Con sumption Optimization of a Flying-Walking Power Transmission Line Inspection Rob ot during Flight Missions Using Improved NSGA-II)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ro botics. According to news reporting from Shihezi, People's Republic of China, by NewsRx journalists, research stated, "In order to improve the flight efficiency of a flying-walking power transmission line inspection robot (FPTLIR) during fl ight missions, an accurate energy consumption model is constructed, and a multio bjective optimization approach using the improved NSGA-II is proposed to address the high energy consumption and long execution time." Financial supporters for this research include Financial Science And Technology Program of The Xpcc; National Natural Science Foundation of China. Our news editors obtained a quote from the research from Shihezi University: "Th e energy consumption model is derived from the FPTLIR kinematics to the motor dy namics, with the key parameters validated using a test platform. A multiobjectiv e optimization model is proposed that considers many constraints related to the FPTLIR during missions, offering a comprehensive analysis of the energy consumpt ion and execution time. The NSGA-II algorithm is improved by integrating the Cau chy variation operator and the simulated annealing algorithm, which is used to c onstruct the multiobjective optimization approach. Simulation and experimental r esults demonstrate that the proposed model accurately predicts the energy consum ption of the FPTLIR across different paths and flight conditions with an average relative error ranging from 0.76% to 3.24%. After op timization, energy savings of 5.33% and 5.01% are ac hieved for on-line and off-line missions, respectively, while maintaining the sh ortest execution time at the given energy level."
Shihezi UniversityShiheziPeople's Re public of ChinaAsiaEmerging TechnologiesMachine LearningRobotRobotics
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