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Artificial coordinating field and its application to motion planning of robots in uncertain dynamic environments

Science China(Technological sciences) 2004年第5期47卷 577-594页

作者： jing xingjian1. 2. wang yuechao1.& tan dalong1.1. robotics laboratory, shenyang institute of automation, Chinese {2. of sciences, shenyang 1.001., China 2. Graduate School, Chinese {2. of sciences, Beijing 100039, China Correspondence should be addressed to jing xingjian 1. robotics {1. Shenyang Institute of Automation Chinese Academy of Sciences 10016 Shenyang China 2. Graduate School Chinese Academy of Sciences 10039 Beijing China

Artificial coordinating fields (ACF) are proposed to deal with the motion planning problems of mobile robots in uncertain dynamic environments. An ACF around an obstacle can generate two orthogonal force vectors to a robot: one is called the coordinating force vector which is purposively designed in this paper, and the other is the repulsive force vector which is the same as that in a conventional artificial potential field. The ACF is designed according to the updated motion purpose and the relative states of the robot with respect to its local environment, and it also satisfies the robot’s dynamic constraints. The direction of the coordinating force can be determiend on line according to an optimal evaluation function. The ACF can effectively remove the local minima, and reduce the oscillation of the planned trajectory between multiple obstacles. Only local knowledge of the environments is needed in the ACF-based motion planning. The properties of the ACF such as controllability, adaptability, safety and reachability are studied and discussed in detail in this paper. Theoretical analysis and simulations are given to illustrate our main results.

关键词： artificial coordinating fields, artificial potential fields, mobile robots, motion planning, uncertain dynamic environments.

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Prediction about residual stress and microhardness of material subjected to multiple overlap laser shock processing using artificial neural network

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Journal of Central South University 2022年第10期29卷 3346-3360页

作者： WU Jia-jun HUANG Zheng QIAO Hong-chao WEI Bo-xin ZHAO Yong-jie LI jing-feng ZHAO Ji-bin State Key laboratory of {1. Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China institutes for {1. and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China institute of Metal Research Chinese Academy of SciencesShenyang 110016China School of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China Faculty of Science and Engineering University of HullHull HU67RXUnited Kingdom Department of Chemistry Tsinghua UniversityBeijing 100084China

In this work,the nickel-based powder metallurgy superalloy FGH95 was selected as experimental material,and the experimental parameters in multiple overlap laser shock processing(LSP)treatment were selected based on orthogonal experimental *** experimental data of residual stress and microhardness were measured in the same *** residual stress and microhardness laws were investigated and *** neural network(ANN)with four layers(4-N-(N-1.-2.was applied to predict the residual stress and microhardness of FGH95 subjected to multiple overlap *** experimental data were divided as training-testing sets in *** energy,overlap rate,shocked times and depth were set as inputs,while residual stress and microhardness were set as *** prediction performances with different network configuration of developed ANN models were compared and *** developed ANN model with network configuration of 4-7-6-2.showed the best predict *** predicted values showed a good agreement with the experimental *** addition,the correlation coefficients among all the parameters and the effect of LSP parameters on materials response were *** can be concluded that ANN is a useful method to predict residual stress and microhardness of material subjected to LSP when with limited experimental data.

关键词： laser shock processing residual stress microhardness artificial neural network

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基于Google Glass的移动机器人远程控制系统

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控制工程 2018年第6期25卷 1077-1085页

作者：宋玉闻玺李岩张进李伟金晶天津大学电气与自动化工程学院天津300072 中国科学院沈阳自动化研究所机器人学国家重点实验室沈阳110016 加州州立大学贝克斯菲尔德分校计算机科学与电子工程系加利福尼亚贝克斯菲尔德CA93311 华东理工大学信息科学与工程学院上海200237

针对现有机器人控制方法中需要占用操作者双手的问题,融合时下新兴的穿戴式智能设备,提出了基于Google Glass的穿戴式的移动机器人远程控制系统。首先,设计了人机交互界面（HMI）,将机器人摄像头实时拍摄的画面直接投影到操作者的视线中... 详细信息

针对现有机器人控制方法中需要占用操作者双手的问题,融合时下新兴的穿戴式智能设备,提出了基于Google Glass的穿戴式的移动机器人远程控制系统。首先,设计了人机交互界面（HMI）,将机器人摄像头实时拍摄的画面直接投影到操作者的视线中;而后搭建了穿戴式的机器人控制系统,操作者通过滑动与点击触控板控制机器人的前进、左转、右转、后退和暂停五种行为。虚拟环境和真实环境下的SRV-1.器人控制实验结果表明,操作者可以使用该系统快速而准确的完成避障、迷宫、跟踪预定轨迹等机器人控制任务。与基于平板电脑的控制方式相比,该系统在控制真实机器人完成任务时需要更少的指令执行时间,从而为操作者提供了一种全新的、更加灵活、准确的机器人远程控制方式。

关键词： Google Glass SRV-1.器人触控板穿戴式

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