基于EKF的服务机器人目标跟踪定位研究
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摘要
针对服务机器人系统环境感知问题,设计了智能服务机器人系统的多传感器信息融合模块,采用扩展卡尔曼滤波算法(EKF)实现里程计与激光雷达融合的目标跟踪定位方法,建立基于EKF的定位模型,通过EKF将激光雷达的观测信息和增量式光电解码器状态的预测信息对机器人的状态进行更新,消除增量式光电解码器定位和激光雷达存在的累计误差,排除加速度的干扰得到位置的最优估计,并通过实验验证系统模块的可靠性与稳定性。
This paper designs a multi-sensor information fusion module of the intelligent service robot system,and uses the extended Kalman filter algorithm( EKF) to realize the target tracking and locating method of the odometry and LADAR,and establishes a location model based on the EKF. The state of the robot is updated by the observation information of the LADAR and the predicted information of the state of the incremental photoelectric decoder by EKF. The optimal estimation of the position is obtained by eliminating the cumulative error of the incremental photoelectric decoder location and the existence of the laser radar,and the reliability and stability of the system module are verified by experiments.
This paper designs a multi-sensor information fusion module of the intelligent service robot system,and uses the extended Kalman filter algorithm( EKF) to realize the target tracking and locating method of the odometry and LADAR,and establishes a location model based on the EKF. The state of the robot is updated by the observation information of the LADAR and the predicted information of the state of the incremental photoelectric decoder by EKF. The optimal estimation of the position is obtained by eliminating the cumulative error of the incremental photoelectric decoder location and the existence of the laser radar,and the reliability and stability of the system module are verified by experiments.
引文
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[11]Han Chong-Zhao,Zhu Hong-Yan,Duan Zhan-Sheng,Han De-Qiang,Liu Wei-Feng,Yu Xin. Multi-source Information Fusion(Second Ed Ition). Beijing:Tsinghua University Press,2010:252~256.
[12]Oates T. R. Siegwart,I. Nourbakhsh. Introduction to Autonomous Mobile Robots 2004 MIT Press[J]. Artificial Intelligence,2005,169(2):146~149.
[2]朱世强,刘松国.我国机器人产业化发展战略探讨[C]//全国先进制造装备与机器人技术高峰论坛,2008.
[3]唐兵.服务机器人的室内定位研究及实现[J].机械设计与制造,2017,4(253~255).
[4]Nasir N Z M,Zakaria M A,Razali S,etal. Autonomous mobile robot localization using Kalman filter[J]. 2016:01069.
[5]Jetto L,Longhi S,Vitali D. Localization of a wheeled mobile robot by sensor data fusion based on a fuzzy logic adapted Kalman filter[J]. Control Engineering Practice,1999,7(6):763~771.
[6]Meyer S J. Message formatting system to improve GPS and IMU positional reporting for a vehicle:US,US8352184[P]. 2013.
[7]GuoJ,Meng Q H,WuYX,etal. Hearing based relative locative localization for mobile robots in outdoor environments[C]//Robotic and Biomimetics(ROBIO),2012 IEEE International Conference on. IEEE,2012:2026~2131.
[8]Emter T,Lu A,etal. Multi-Sensor Fusion for Localization of a Mobile Robot IN Outdoor Environments[J]. Christian Frese,2010:1~6.
[9]郭彤颖,张辉.机器人传感器及其融合技术[M].北京:化学工业出版社,2016. 12:84,86.
[10]张恺渊,刘佩林.多传感器融合机器室内定位系统设计与实现[J].信息技术,2014(14),83~87.
[11]Han Chong-Zhao,Zhu Hong-Yan,Duan Zhan-Sheng,Han De-Qiang,Liu Wei-Feng,Yu Xin. Multi-source Information Fusion(Second Ed Ition). Beijing:Tsinghua University Press,2010:252~256.
[12]Oates T. R. Siegwart,I. Nourbakhsh. Introduction to Autonomous Mobile Robots 2004 MIT Press[J]. Artificial Intelligence,2005,169(2):146~149.