计算机应用 ›› 2015, Vol. 35 ›› Issue (12): 3581-3585.DOI: 10.11772/j.issn.1001-9081.2015.12.3581

• 行业与领域应用 • 上一篇    下一篇

移动在线水质监测平台动态避障方法

劳家骏, 杨江, 祝武明   

  1. 浙江大学控制科学与工程学院, 杭州 310027
  • 收稿日期:2015-06-04 修回日期:2015-08-24 出版日期:2015-12-10 发布日期:2015-12-10
  • 通讯作者: 劳家骏(1990-),男,浙江兰溪人,硕士研究生,主要研究方向:水质检测技术
  • 作者简介:杨江(1966-),男,浙江绍兴人,副教授,博士研究生,主要研究方向:水质检测技术、嵌入式系统;祝武明(1990-),男,江西鹰潭人,硕士研究生,主要研究方向:信号处理、多相流检测。
  • 基金资助:
    国家科技重大专项(2008ZX07420-004)。

Dynamic obstacle avoidance method of mobile online water quality monitoring platform

LAO Jiajun, YANG Jiang, ZHU Wuming   

  1. College of Control Science and Engineering, Zhejiang University, Hangzhou Zhejiang 310027, China
  • Received:2015-06-04 Revised:2015-08-24 Online:2015-12-10 Published:2015-12-10

摘要: 针对移动水质监测平台在自主导航中遇到移动障碍物的问题,提出了一种将障碍物运动状态预测模型结合速度避障碰撞模型的动态避障新方法。首先,通过移动水质监测平台上的超声波测距模块和图像采集模块测量移动水质监测平台与障碍物的距离和相对方位角,采用坐标系转换的方法计算出障碍物速度和运动方向;其次,利用极大似然估计法建立障碍物运动状态预测模型,通过该模型得到下一个采样时刻障碍物速度和运动方向范围;最后,利用速度避障的碰撞模型,计算出下一时刻的移动水质监测平台的航向角。实验结果证明,所提的避障方法能够规划出一条更为真实的较优路径。与无障碍物运动状态预测模型的避障方法相比,该避障方法能提高动态避障的成功率。

关键词: 自主导航, 移动障碍物, 运动状态预测模型, 极大似然估计, 速度避障碰撞模型

Abstract: Focusing on the problem of encountering the moving obstacle for the water quality monitoring platform in the autonomous navigation,a new dynamic obstacle avoidance method based on the obstacle motion prediction model and the velocity obstacle avoidance model was proposed. Firstly, the ultrasonic ranging module and image acquisition module were used to measure the distance and the azimuth angle between the obstacle and the platform, and then the obstacle's speed and direction were calculated using the coordinate transformation method. Secondly, obstacle motion prediction model was built based on maximum likelihood estimation method, and obstacle speed and direction of the next sampling instant were obtained by this model. Finally, the platform's course angle of the next sampling instant was calculated using the velocity obstacle avoidance model. The experimental results prove that the proposed obstacle avoidance method can plan out a more realistic and optimal path. Compared with the obstacle avoidance method without the obstacle motion prediction model, the proposed obstacle avoidance method could improve the success rate of obstacle avoidance.

Key words: autonomous navigation, moving obstacle, motion prediction model, maximum likelihood estimation, velocity obstacle avoidance model

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