基于自适应系数卡尔曼滤波的农业移动机器人组合定位

Adaptive-coefficient Kalman Filter Based Combined Positioning Algorithm for Agricultural Mobile Robots

基于全球导航卫星系统(Global navigation satellite system,GNSS)的定位导航技术在半结构化、半开放式农业应用场景的部分区域,可能由于存在作物遮挡而导致GNSS接收信号出现短暂丢失的情况,进而影响机器人定位导航精度,甚至对作物和工作人员造成伤害。针对这一问题,本文开展了农业遮挡环境下的GNSS与惯性导航系统(Inertial navigation system,INS)的组合定位方法研究。搭建了用于多传感器定位导航实验的农业机器人系统,该系统由履带式移动平台、GNSS、INS等硬件和ROS(Robot operation system)操作系统、远程操控界面等软件构成。提出了引入自适应系数的GNSS/INS组合定位卡尔曼滤波算法,当GNSS无法进行差分定位或定位数据产生跃变时,通过自适应卡尔曼滤波能够切换到INS定位,从而实现机器人自身位置、姿态的最优估计。在典型农业遮挡场景(果园)进行了实地组合定位实验,并通过GNSS单通道定位、INS单通道定位、常规卡尔曼滤波融合定位、引入自适应系数的卡尔曼滤波定位等4种定位方法的对比,验证了本文提出算法的有效性。现场实验表明:定位过程中,当100 m×20 m的实验区域内出现30 m×6 m的高遮挡区域时,与GNSS定位信息测量方法、INS航迹推算定位方法以及常规卡尔曼滤波组合定位方法相比,自适应系数卡尔曼滤波组合定位方法定位精度分别提升62.1%、48.5%、47.7%。

GNSS-based positioning and navigation has been widely used for agricultural robots in open unmanned farms.However,for the applications of semi-structured and semi-open agricultural scenarios,there may be temporary loss of GNSS received signals caused by occlusion of canopies in some areas,which will affect the positioning and navigation accuracy of robots and even harm crops or farmers.To solve this problem,a combined positioning method of GNSS and INS under the occlusion environment of agriculture was studied.The main work consisted of three parts:a mobile agricultural robot system was build up for the experiments of multi-sensor-based positioning and navigation,which consisted of hardware(track-layer mobile platform,GNSS receivers and INS,etc.)and software(ROS,remote control interface,etc.);an adaptive-coefficient Kalman filter based combined positioning algorithm was proposed.When the GNSS signal was unstable or denied,the new algorithm can switch to INS positioning adaptively based on Kalman filter,which carried out the optimal estimation for the robots’location and gesture;experiments of the proposed combined positioning algorithm were conducted under practical scenes of agriculture,in which four different positioning methods(GNSS only,INS only,Kalman filter based combined positioning,and adaptive-coefficient Kalman filter based combined positioning)were compared to validate the effectiveness of the algorithm.Field experiments showed that in the process of combined positioning,compared with GNSS positioning,INS positioning and conventional Kalman filter fusion positioning,the positioning accuracy of adaptive-coefficient Kalman filter in the 30 m×6 m high shaded area of 100 m×20 m experimental area was improved by 62.1%,48.5%and 47.7%,respectively.