首页|New Robotics Findings Has Been Reported by Investigators at Shanghai University of Engineering Science [Dynamic Path Planning for Mobile Robo ts Based On Artificial Potential Field Enhanced Improved Multiobjective Snake Op timization (Apf-imoso)]
New Robotics Findings Has Been Reported by Investigators at Shanghai University of Engineering Science [Dynamic Path Planning for Mobile Robo ts Based On Artificial Potential Field Enhanced Improved Multiobjective Snake Op timization (Apf-imoso)]
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
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Fresh data on Robotics are presented i n a new report. According to news reporting originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “With the widespre ad adoption of mobile robots, effective path planning has become increasingly cr itical. Although traditional search methods have been extensively utilized, meta -heuristic algorithms have gained popularity owing to their efficiency and probl em-specific heuristics.” Our news editors obtained a quote from the research from the Shanghai University of Engineering Science, “However, challenges remain in terms of premature conve rgence and lack of solution diversity. To address these issues, this paper propo ses a novel artificial potential field enhanced improved multiobjective snake op timization algorithm (APF-IMOSO). This paper presents four key enhancements to t he snake optimizer to significantly improve its performance. Additionally, it in troduces four fitness functions focused on optimizing path length, safety (evalu ated via artificial potential field method), energy consumption, and time effici ency. The results of simulation and experiment in four scenarios including stati c and dynamic highlight APF-IMOSO’s advantages, delivering improvements of 8.02% , 7.61%, 50.71%, and 12.74% in path leng th, safety, energy efficiency, and time-savings, respectively, over the original snake optimization algorithm. Compared with other advanced meta-heuristics, APF -IMOSO also excels in these indexes. Real robot experiments show an average path length error of 1.19% across four scenarios.”
ShanghaiPeople’s Republic of ChinaAs iaEmerging TechnologiesMachine LearningNano-robotRobotRoboticsShangh ai University of Engineering Science
NETL
NSTL
