摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-研究人员详细介绍了机器人学的新数据。根据NewsRx Edito RS在中华人民共和国上海的新闻报道，研究表明，"机械手在动态环境中的运动规划任务相对复杂"。本研究的资金来源包括国家重点研究开发项目。本文采自上海海洋大学的一篇研究论文：“本文采用改进的具有最大熵优势的软演员批评算法(SAC)作为基准算法，实现了机械手的运动规划，解决了机械手在动态环境中鲁棒性不足、难以适应环境变化的问题，”为了解决动态环境下输入状态不稳定、不确定、不能充分表达状态信息的问题,提出了一种融合长短期记忆(LSTM)的注意网络来改进SAC算法。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in robotics. Accordin g to news reporting out of Shanghai, People’s Republic of China, by NewsRx edito rs, research stated, “The motion planning task of the manipulator in a dynamic e nvironment is relatively complex.” Financial supporters for this research include National Key Research And Develop ment Program of China. Our news correspondents obtained a quote from the research from Shanghai Ocean U niversity: “This paper uses the improved Soft Actor Critic Algorithm (SAC) with the maximum entropy advantage as the benchmark algorithm to implement the motion planning of the manipulator. In order to solve the problem of insufficient robu stness in dynamic environments and difficulty in adapting to environmental chang es, it is proposed to combine Euclidean distance and distance difference to impr ove the accuracy of approaching the target. In addition, in order to solve the p roblem of non-stability and uncertainty of the input state in the dynamic enviro nment, which leads to the inability to fully express the state information, we p ropose an attention network fused with Long Short-Term Memory (LSTM) to improve the SAC algorithm.”
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