摘要
一位新闻记者兼机器人与机器学习的新闻编辑每日新闻-机器人的最新数据在一份新的报告中呈现。根据NewsRx记者从中华人民共和国上海发回的新闻报道，研究表明：“随着移动机器人的广泛应用，有效的路径规划变得越来越重要。尽管传统的搜索方法被广泛使用，但元启发式算法由于其效率和特定问题的启发式算法而越来越受欢迎。”为解决这些问题,本文提出了一种人工势场增强的多目标Snake优化算法(APF-IMOSO),并对Snake优化算法进行了4个关键改进,以显著提高其性能。介绍了APF-IMOSO在路径长度、安全性(人工势场法评价)、能耗和时间效率四个方面的适应度函数,在静态和动态四种场景下的仿真和实验结果表明,APF-IMOSO在路径长度、安全性、能效和时间效率方面分别提高了8.02%、7.61%、50.71%和12.74%。与其他先进的启发式算法相比，APF-IMOSO算法在这些指标上也有很好的表现，实际机器人实验表明，在四种情况下，平均路径长度误差为1.19%。
Abstract
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.”
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