Integrated vision-based system for efficient,semi-automated control of a robotic manipulator

作     者：Hairong Jiang Juan P.Wachs Bradley S.Duerstock 

作者机构：School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA Weldon School of Biomedical EngineeringSchool of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA 

出 版 物：《International Journal of Intelligent Computing and Cybernetics》 (智能计算与控制论国际期刊（英文）)

年 卷 期：2014年第7卷第3期

页      面：253-266页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Institute of General Medical Sciences(Grant No.1DP4GM096842-01) The authors are grateful for the assistance of Jamie Nolan from the Institute for Accessible Science and Mithun Jacob from the Intelligent System and Assistive Technology(ISAT)lab at Purdue University 

主　　题：Gesture recognition Object recognition Spinal cord injuries Wheelchair-mounted robotic arm 

摘      要：Purpose–The purpose of this paper is to develop an integrated,computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator(WMRM).In addition,a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient,hands-free WMRM ***/methodology/approach–Two Kinects cameras were used synergistically to perform a variety of simple object retrieval *** camera was used to interpret the hand gestures and locate the operator’s face for object positioning,and then send those as commands to control the *** other sensor was used to automatically recognize different daily living objects selected by the *** object recognition module employing the Speeded Up Robust Features algorithm was implemented and recognition results were sent as a commands for“coarse positioningof the robotic arm near the selected *** face detection was provided as a shortcut enabling the positing of the objects close by the subject’s ***–The gesture recognition interface incorporated hand detection,tracking and recognition algorithms,and yielded a recognition accuracy of 97.5 percent for an eight-gesture ***’completion time were conducted to compare manual(gestures only)and semi-manual(gestures,automatic face detection,and object recognition)WMRM control *** use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living ***/value–Integration of three computer vision modules were used to construct an effective and hand-free interface for individuals with upper-limb mobility impairments to control a WMRM.