首页|Research Results from Hubei University of Technology Update Understanding of Robotics (Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer)
Research Results from Hubei University of Technology Update Understanding of Robotics (Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Mdpi
Researchers detail new data in robotics. According to news originating from Wuhan, People’s Republic of China, by NewsRx correspondents, research stated, “This paper proposes a golden sine grey wolf optimizer (GSGWO) that can be adapted to the obstacle-crossing function to solve the path planning problem of obstacle-crossable robot.” Financial supporters for this research include National Natural Science Foundation of China. Our news correspondents obtained a quote from the research from Hubei University of Technology: “GSGWO has been improved from the gray wolf optimizer (GWO), which provide slow convergence speed and easy to fall into local optimum, especially without obstacle-crossing function. Firstly, aiming at the defects of GWO, the chaotic map is introduced to enrich the initial population and improve the convergence factor curve. Then, the convergence strategy of the golden sine optimizer is introduced to improve the shortcomings of GWO, such as insufficient convergence speed in the later stage and the ease with which it falls into the local optimum. Finally, by adjusting the working environment model, path generation method and fitness function, the path-planning problem of the obstacle-crossing robot is adapted. In order to verify the feasibility of the algorithm, four standard test functions and three different scale environment models are selected for simulation experiments. The results show that in the performance test of the algorithm, the GSGWO has higher convergence speed and accuracy than the GWO under different test functions.”
Hubei University of TechnologyWuhanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningRobotRobotics
NETL
NSTL
Mdpi
