基于改进纯追踪模型的温室采摘运输自动跟随系统

基于Kinect体感感应技术,设计了一套温室果蔬采摘运输自动跟随平台。通过体感感应系统获取图片上像素点的深度信息,结合图像处理算法,逐行扫描确定人体图像并实时获取人体骨骼信息,计算了人体当前的三维坐标并记录人体走过的路径轨迹。系统采用自调整函数对路径进行优化,避免了剧烈转向行为,并对优化后的路径以模糊算法动态确定纯追踪模型的前视距离,从而实时调整转向和转角,实现了精准跟随和稳定跟随。试验结果表明,该跟随系统能在避免剧烈转向的前提下以较高的精度跟随,横向最大跟踪偏差不超过10.0 cm,最大深度偏差为5.5 cm,且系统性能不受光照条件影响,满足温室采摘运输要求。

Automatic Following System for Greenhouse Harvesting Transportation Based on Adaptive Pure Pursuit Model

Transportation for harvest and plant protection activities inside greenhouse calls for a lot of labor. The available transporting equipment has limited function which is not very flexible. While the widely used railway transportation vehicles inside greenhouse in many developed countries are hard to introduce to China due to variable construction styles and nonstandard layouts, some automatic following system is urgently in need. Thus a kind of autonomous transportation system for greenhouse harvesting activity was presented based on Kinect sensor developed by Microsoft Company. The Kinect sensor could acquire the depth data of each pixel on the picture, from which the skeleton of a workman could be analyzed by several image processing algorithms. Direction and heading angle was determined by the real-time coordinate of the center of gravity of the skeleton. At the same time, the 3D coordinate of the workman was kept and the tracking path was thus formed. To avoid dramatic turning, adaptive function was applied to optimize the path firstly. And then pure pursuit method was introduced to complete the tracking stably and precisely. The look-ahead distance was timely decided by fuzzy controller, with which the vehicle would track more flexibly according to different look head distances. The result showed that the system was able to turn freely and precisely while avoiding drastic turning. The largest lateral deviation of straight line tracking was smaller than 10.0cm, and the largest depth deviation was 5.5cm. The working function had nothing to do with the light condition, which met the demands of harvesting and goods transportation in greenhouse in different seasons.