融合物联网技术云计算技术提出智能传感激光SLAM(Simultaneous localization and mapping)算法,首先基于成分分析法相邻帧的点云计算矩阵进行粗配准,再使用改良后算法提供的改进点到线迭代的最近配准算法来弥补传统算法精度较低的问题...融合物联网技术云计算技术提出智能传感激光SLAM(Simultaneous localization and mapping)算法,首先基于成分分析法相邻帧的点云计算矩阵进行粗配准,再使用改良后算法提供的改进点到线迭代的最近配准算法来弥补传统算法精度较低的问题。采用了多重采样的算法在多次复制大权重例子集合的背景下利用小权重粒子集合来提升移动机器人路径定位精准度。最后将改良后的算法运用于AI移动机器人,实验结果表明,改进后的SLAM算法对移动机器人的路径设计的定位精准度有了较大提升,AI机器人可以具备优良的避障功能,对于已知环境或者非完全已知环境中存在的障碍物都具有良好的适应能力。展开更多
In today’s digital era,algorithms have become an indispensable part of our daily lives and work.Algorithm education plays a crucial role in computer science and software engineering,aiming to cultivate students’prob...In today’s digital era,algorithms have become an indispensable part of our daily lives and work.Algorithm education plays a crucial role in computer science and software engineering,aiming to cultivate students’problem-solving skills and computational thinking.However,traditional algorithm education often requires significant time and efforts from teachers,lacks interactivity,and provides limited examples.The rapid advancement of AI technology,particularly generative models,and large language models(LLMs),has the potential to revolutionize computer education.Models like OpenAI’s GPT-4 and ChatGPT have conversational capabilities and contribute to various aspects of computer education.GPT-3.5,as an assistant in algorithm education,assists teachers in automatically generating explanations and algorithmic examples to enhance students’understanding of algorithms.While existing research has certain limitations,such as focusing on specific scenarios and lacking comprehensive benchmark testing,this paper explores the role of ChatGPT(GPT-3.5)in algorithm education.By refining prompts and evaluating generative capabilities,the study demonstrates that GPT-3.5 holds significant potential as a teaching aid.With an average accuracy of 0.81.GPT-3.5 can generate explanations,code examples,and visualizations of the corresponding algorithms.Other tests including algorithm problem-solving and examples giving also prove the practicability of GPT-3.5 in algorithm education.展开更多
文摘融合物联网技术云计算技术提出智能传感激光SLAM(Simultaneous localization and mapping)算法,首先基于成分分析法相邻帧的点云计算矩阵进行粗配准,再使用改良后算法提供的改进点到线迭代的最近配准算法来弥补传统算法精度较低的问题。采用了多重采样的算法在多次复制大权重例子集合的背景下利用小权重粒子集合来提升移动机器人路径定位精准度。最后将改良后的算法运用于AI移动机器人,实验结果表明,改进后的SLAM算法对移动机器人的路径设计的定位精准度有了较大提升,AI机器人可以具备优良的避障功能,对于已知环境或者非完全已知环境中存在的障碍物都具有良好的适应能力。
基金funded by the Double First Class Graduate Quality Curriculum Construction Project of Shanghai Jiao Tong University。
文摘In today’s digital era,algorithms have become an indispensable part of our daily lives and work.Algorithm education plays a crucial role in computer science and software engineering,aiming to cultivate students’problem-solving skills and computational thinking.However,traditional algorithm education often requires significant time and efforts from teachers,lacks interactivity,and provides limited examples.The rapid advancement of AI technology,particularly generative models,and large language models(LLMs),has the potential to revolutionize computer education.Models like OpenAI’s GPT-4 and ChatGPT have conversational capabilities and contribute to various aspects of computer education.GPT-3.5,as an assistant in algorithm education,assists teachers in automatically generating explanations and algorithmic examples to enhance students’understanding of algorithms.While existing research has certain limitations,such as focusing on specific scenarios and lacking comprehensive benchmark testing,this paper explores the role of ChatGPT(GPT-3.5)in algorithm education.By refining prompts and evaluating generative capabilities,the study demonstrates that GPT-3.5 holds significant potential as a teaching aid.With an average accuracy of 0.81.GPT-3.5 can generate explanations,code examples,and visualizations of the corresponding algorithms.Other tests including algorithm problem-solving and examples giving also prove the practicability of GPT-3.5 in algorithm education.
