DailyEnglish(20140120)

本文讲述了在一个包含64个用户故事的项目中,团队如何通过增加冲刺次数来确保项目的质量,并分享了在测试过程中遇到的问题及解决方案。强调了正确态度对于克服困难的重要性。
I pray for my mom, god bless my mom please.


The topic for today is the importance of attitude.


There is a saying tells that your attitude decides what you will get at the end.


Our project has send to test group for sprint testing today, there are totally four sprints for this project, actually it is three at the beginning, then we evaluate how long we could finish this project based on the number of developers, after we finished the evaluation, we know that it is impossible for us to finish all parts of this project, we only have ten members, there are totally 64 user stories.


In order to guarantee the quality of our project, we communicate with SBE and customers and propose to add another sprint, our customer agreed this proposal at last.


Two tester test our project, there are more than ten defects has been found, one of them is from my user story, but it is not because I did not serious on my work, it because I do not know the mean of Korea words. the text they give us is in Korea, and there is no explanation in Chinese or English, so I kept the exact words they give us on the page. but tester tell me it is a defect, it should be the name of group which user input in the first step, I change my code and fixed that defect in the end.


What I want to say is this is not because it is too complex that I did it wrong, I made this mistake as I do not know the meaning of those Korean words but I did not ask for help, I will pay more attention on such thing and try my best to avoid such easy mistake in the future.

内容概要:本文围绕六自由度机械臂的人工神经网络(ANN)设计展开,重点研究了正向与逆向运动学求解、正向动力学控制以及基于拉格朗日-欧拉法推导逆向动力学方程,并通过Matlab代码实现相关算法。文章结合理论推导与仿真实践,利用人工神经网络对复杂的非线性关系进行建模与逼近,提升机械臂运动控制的精度与效率。同时涵盖了路径规划中的RRT算法与B样条优化方法,形成从运动学到动力学再到轨迹优化的完整技术链条。; 适合人群:具备一定机器人学、自动控制理论基础,熟悉Matlab编程,从事智能控制、机器人控制、运动学六自由度机械臂ANN人工神经网络设计:正向逆向运动学求解、正向动力学控制、拉格朗日-欧拉法推导逆向动力学方程(Matlab代码实现)建模等相关方向的研究生、科研人员及工程技术人员。; 使用场景及目标:①掌握机械臂正/逆运动学的数学建模与ANN求解方法;②理解拉格朗日-欧拉法在动力学建模中的应用;③实现基于神经网络的动力学补偿与高精度轨迹跟踪控制;④结合RRT与B样条完成平滑路径规划与优化。; 阅读建议:建议读者结合Matlab代码动手实践,先从运动学建模入手,逐步深入动力学分析与神经网络训练,注重理论推导与仿真实验的结合,以充分理解机械臂控制系统的设计流程与优化策略。
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