基于多维尺度法和卡尔曼滤波的机器人传感器网络跟踪定位
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摘要
为解决当前工业机器人定位误差大,传统多维尺度(MDS)模型的精度受到环境噪声的影响,现有定位方法无法满足定位精度需求的现状,提出了一种将卡尔曼滤波和多目标跟踪相结合的方法来减小噪声对多源定位的影响.建立了高斯分布下加性噪声对距离测量噪声分布的预测模型,并据此建立了线性跟踪系统.实验数据表明,在不同噪声水平及不同传感器网络节点数量下,基于MDS法的传感器网络节点定位误差最高接近20%,基于EKL法的定位误差在7.46%～13.13%范围内变化,而本文提出的MDS和KL联合定位方法误差始终控制在8%以内.与现有常用的MDS和扩展卡尔曼滤波EKL定位方法相比,该算法有效地降低了实际环境中的噪声影响,具有很好解决移动传感器定位问题的潜力,满足了高精度定位的实际要求.同时,由于采用了线性滤波器进行简化,很好地适应了功率、内存和计算能力有限的小型嵌入式传感器,在机器人定位领域具有一定的参考价值.
The work aims to solve the problems that the positioning error of industrial robots is large,and the precision of the traditional multidimensional scale(MDS)model is affected by the environmental noise which cannot meet the requirement of positioning accuracy.A solution to attenuate noise effects to MDS by combining MDS with a Kalman filter was proposed.A model was built to predict the noise distribution with regard to additive noises to the distance measurements following the Gaussian distribution.From that,a linear tracking system was developed.The experimental data showed that,under the different noise level and sensor network node numbers,the location error based on MDS method is close to20%,and changes in the 7.46%～13.13%based on EKL,and always lower than8%based on multidimensional scaling coupled Kalman filtering.Compared with the current locating algorithms(MDS and EKL),the proposed algorithm had a higher positioning accuracy under different noise level and node number.It had a good potential to solve the positioning of mobile sensors.Besides,the linear filter is simplified,therefore it suits small and embedded sensors equipped with limited power,memory,and computational capacities well,which has certain reference value in the field of robot positioning.
The work aims to solve the problems that the positioning error of industrial robots is large,and the precision of the traditional multidimensional scale(MDS)model is affected by the environmental noise which cannot meet the requirement of positioning accuracy.A solution to attenuate noise effects to MDS by combining MDS with a Kalman filter was proposed.A model was built to predict the noise distribution with regard to additive noises to the distance measurements following the Gaussian distribution.From that,a linear tracking system was developed.The experimental data showed that,under the different noise level and sensor network node numbers,the location error based on MDS method is close to20%,and changes in the 7.46%～13.13%based on EKL,and always lower than8%based on multidimensional scaling coupled Kalman filtering.Compared with the current locating algorithms(MDS and EKL),the proposed algorithm had a higher positioning accuracy under different noise level and node number.It had a good potential to solve the positioning of mobile sensors.Besides,the linear filter is simplified,therefore it suits small and embedded sensors equipped with limited power,memory,and computational capacities well,which has certain reference value in the field of robot positioning.
引文
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[3]王志,朱世强,李月华,等.基于视觉里程计和自然信标融合的移动机器人定位算法[J].农业工程学报,2017,33(10):70-77.WANG Z,ZHU S Q,LI Y H,et al.Localization algorithm for mobile robot based on visual odometer and natural beacon fusion[J].Transactions of the Chinese Society of Agricultural Engineering,2017,33(10):70-77.
[4]张哲远.果园机器人自主导航关键技术研究[D].武汉:华中科技大学,2016.ZHANG Z Y.Research on the key technology of autonomous navigation for orchard mobile robot[D].Wuhan:Huazhong University of Science and Technology,2016.
[5]张文富,张明锋,林广发,等.基于分布式虚拟环境的航位推测改进算法[J].海南师范大学学报(自然科学版),2016,29(2):202-207.ZHANG W F,ZHANG M F,LIN G F,et al.One research of modified dead reckoning algorithm based on distributed virtual environments[J].Journal of Hainan Normal University(Natural Science),2016,29(2):202-207.
[6]赵建伟,王洪燕,唐兵,等.基于移动机器人的GPS轨迹生成及定位研究[J].工矿自动化,2016(1):17-19.ZHAO J W,WANG H Y,TANG B,et al.Research of GPS track generation and location based on mobile robot[J].Industry and Mine Automation,2016(1):17-19.
[7]谢德胜,徐友春,万剑,等.基于RTK-GPS的轮式移动机器人轨迹跟随控制[J].机器人,2017,39(2):221-229.XIE D S,XU Y C,WAN J,et al.Trajectory tracking control of wheeled mobile robots based on RTK-GPS[J].Robot,2017,39(2):221-229.
[8]周俊,张鹏.基于灰色预测的农业机器人GPS导航系统硬故障隔离[J].农业机械学报,2010,41(12):165-168.ZHOU J,ZHANG P.Hardfault isolation of GPS navigation system based on gray prediction for agricultural robot[J].Transactions of the Chinese SocietyofAgricultural Machinery,2010,41(12):165-168.
[9]焦亚冰,苑海波.WLAN及GPS环境下基于RFID军事物流MIS可视化方案研究[J].包装工程,2009,30(7):66-70.JIAO Y B,YUAN H B.Study of visualization project of military logistics MIS based on RFID in WLAN and GPS circumstances[J].Packaging Engineering,2009,30(7):66-70.
[10] TORGERSON W S.Theory and methods of scaling[M].New York:Wiley,1958.
[11] AGRAWAL R,PATEL B.Localization in wireless sensor network using MDS[J].International Journal of Smart and Ad Hoc Networks,2012,1(3):26-31.
[12] SHI Q,HUO H,FANG T,et al.A 3Dnode localization scheme for wireless sensor networks[J].IEICE Electronics Express,2009,6(3):167-172.
[13] FAN J,ZHANG B H,DAI G J.D3D-MDS:a distributed 3D localization scheme for an irregular wireless sensor network using multidimensional scaling[J].International Journal of Distributed Sensor Networks,2015,11(2):103564.
[14] XU Y,ZHUANG Y,GU J.An improved 3D localization algorithm for the wireless sensor network[J].International JournalofDistributedSensorNetworks, 2015, 11(6):315714.
[15] RISTIC B,ARULAMLALAM S,GORDON N.Beyond the Kalman filter:particle filters for tracking applications[M].[S.l.]:Artech House,2004.