期刊文献+

采用Kinect的移动机器人目标跟踪与避障 被引量:9

Target tracking and obstacle avoidance of mobile robot using Kinect
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摘要 为实现移动机器人在目标跟踪的同时进行避障,采用Kinect代替传统的测距雷达和摄像机.针对Kinect在使用中存在盲区和噪声的问题,提出一种基于统计的局部地图更新方法,利用动态更新的局部地图保存可能影响机器人运动的障碍物信息,并通过统计信息来消除测距噪声的影响,确保障碍物信息的有效性.同时使用增加安全区域的人工势场法去除对移动机器人运动无干扰的障碍物信息,改善了传统人工势场法通过狭窄通道的能力.在差动驱动移动机器人的实验证实了此系统能够很好地完成跟踪与避障任务,结果表明,使用Kinect可以代替传统测距传感器. In order to get a better understanding of the obstacle avoidance of a mobile robot when it is tracking a tar -get, we used Kinect to take the place of the traditional range radar and camera .Because of the existence of a blind area and noise when using Kinect , a kind of local map updating method based on statistical theory was proposed , through the utilization of a dynamically updated local map , the information of an obstacle possibly affecting the mo-tion of the robot was maintained , in addition , by collecting information of statics , the influence of the range noise was eliminated , so to assure the validity of the obstacle information .Simultaneously , the artificial potential field method increasing the safe area was applied to remove the information of an obstacle not disturbing the motion of the robot, so as to improve the ability of the mobile robot to pass through a narrow passage by the traditional artificial potential field method .The experiment used on a mobile robot with differential drive shows that , this system may re-alize target tracking and obstacle avoidance in a proper manner; the Kinect may take the place of the traditional range sensor .
出处 《智能系统学报》 CSCD 北大核心 2013年第5期426-432,共7页 CAAI Transactions on Intelligent Systems
基金 国家自然科学基金资助项目(61075027)
关键词 移动机器人 KINECT 人工势场 避障 目标跟踪 mobile robot Kinect artificial potential field obstacle avoidance target tracking
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参考文献10

  • 1STEPHANEDES Y J, KWON E, TZAFESTAS S G, et al. Optimal control of nonlinear dynamic transportation systems[C]//Proceedings of the 29th IEEE Conference on Decision and Control. Honolulu, USA, 1990: 1641-1645.
  • 2MICHAEL B, KAI N, BRYAN R. Large-scale multi-agent simulations for transportation applications[J]//Journal of Intelligent Transportation Systems, 2004, 8(4): 205-221.
  • 3高峰,王江锋,施绍友,王健.基于模糊神经网络的车辆避撞预警算法[J].江苏大学学报(自然科学版),2006,27(3):211-215. 被引量:9
  • 4DAVID C. Count down to greater safey[J]. ITS International, 2005, 11(2): 1-3.
  • 5张祺,杨宜民.基于改进人工势场法的足球机器人避碰控制[J].机器人,2002,24(1):12-15. 被引量:47
  • 6李人厚.自主移动机器人导论\[M\].西安:西安交通大学出版社, 2006: 276-279.
  • 7BORENSTEIN J, KOREN Y. Real-time obstacle avoidance for fast mobile robots\[J\]. IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(5): 1179-1187..
  • 8CAO Qixin, HUANG Yanwen, ZHOU Jingliang. An evolutionary artificial potential field algorithm for dynamic path planning of mobile robot\[C\]//2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing, China, 2006: 3331-3336.
  • 9夏轩,刘华平,许伟明,孙富春.基于DSP的主动视觉系统[J].机器人,2012,34(3):354-362. 被引量:5
  • 10庄严,战洪斌,王伟,王珂.基于加权颜色直方图和粒子滤波的彩色物体跟踪[J].控制与决策,2006,21(8):868-872. 被引量:20

二级参考文献32

  • 1洪炳熔 刘新宇 等.基于多智能体的机器人足球比赛.中国2000年机器人学大会论文专辑[M].长沙,2000..
  • 2徐心和 佟国峰.机器人足球比赛是推动机器人技术研究的舞台.中国2000年机器人学大会论文专辑[M].长沙,2000..
  • 3Rasolzadeh B, Bj6rkman M, Huebner K, et al. An active vision system for detecting, fixating and manipulating objects in the real world[J]. International Journal of Robotics Research, 2010, 29(2/3): 133-154.
  • 4Boev A, Georgiev M, Gotchev A. Optimized visualization of stereo images on an OMAP platform with integrated parallax barrier auto-stereoscopic display[C]//17th European Signal Pro- cessing Conference. 2009: 490-494.
  • 5Ali S S A, Jamil K T, Muhammad E Real time object tracking in a video sequence using a fixed point DSP[C]//4th International Symposium on Visual Computing. Berlin, Germany: Springer- Verlag, 2008: 879-888.
  • 6Viola P, Jones M. Robust real-time object detection[C]//2nd In- ternational Workshop on Statistical and Computational Theo- ries of Vision- Modeling, Learning, Computing, and Sampling. 2002.
  • 7Yang M, Crenshaw J, Augustine B. AdaBoost-based face de- tection for embedded systems[J]. Computer Vision and Image Understanding, 2010, 144(11 ): 1116-1125.
  • 8Perez P, Hue C, Vermaak J, et al. Color-based probabilistic tracking[C]//7th European Conference on Computer Vision: Part I. London, UK: Springer-Verlag, 2002: 661-675.
  • 9Porikli F. Integral histogram: A fast way to extract histograms in Cartesian spaces[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2005: 829- 836.
  • 10Sizintsev M, Derpanis K G, Hogue A. Histogram-based search: A comparative study[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ, USA: IEEE, 2008: 1- 8.

共引文献77

同被引文献71

  • 1田国会,王家超,段朋.病房巡视机器人复杂环境下的避障技术研究[J].华中科技大学学报(自然科学版),2013,41(S1):312-315. 被引量:3
  • 2陈凤东,洪炳镕.基于动态阈值背景差分算法的目标检测方法[J].哈尔滨工业大学学报,2005,37(7):883-884. 被引量:43
  • 3肖晓明,胡华梅,蔡自兴,王璐.基于自适应分割和立体视觉的快速障碍检测[J].计算机应用研究,2007,24(9):182-184. 被引量:1
  • 4Discant A, Rogozan A, Rusu C, et al. Sensors for obstacle de- tection-a survey [ C ]//Proc of 30th int spring semin electron technol. E s. 1. ]:E s. n. ] ,2007:100-105.
  • 5Han J, Shao L, Xu D, et al. Enhanced computer vision with Microsoft Kinect sensor:a review[J].IEEE Trans on Cybem, 2013,43(5) :1318-1334.
  • 6Maier D, Stachniss C, Bennewitz M. Vision- based humanoid navigation using self- supervised obstacle detection [ J ] Int Journal of Humanoid Robotics ,2013,10 (2) : 1-28.
  • 7Wang Z,Liu H,Qian Y L. Real-time plane segmentation and obstacle detection of 3D point clouds for indoor scenes[ C ]// Proceedings of European conference on computer vision work- shops and demonstrations. Berlin:Springer,2012:22-31.
  • 8Choi J, Kim D, Yoo H, et al. Rear obstacle detection system based on depth from Kinect [ C ]//Proc of 15th international IEEE conference on intelligent transportation systems. [ s. 1. ] :IEEE,2012:98-101.
  • 9Khan A, Moideen F, Lopez J, et al. KinDectect: Kinect detec- ting objects [ C ]//Proc of ICCHP. Berlin : Springer, 2012 : 588 -595.
  • 10Labayrade R, Aubert D, Tarel J P. Real time obstacle detection in stereovision on non flat road geometry through "v-dispari- ty" representation[ C ]//Proc of IEEE intelligent vehicle sym- posium. [ s. 1. ] : IEEE,2002:646-651.

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