主动万向脚轮式全向移动机器人的关节空间多传感器信息融合算法
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摘要
鉴于主动万向脚轮式全向移动机器人运动学矩阵与其脚轮转向关节的位置有关,因此脚轮的运动误差会同时降低机器人的里程计和速度控制精度.因传统的工作空间信息融合算法无法有效提高脚轮运动精度,本文提出一种基于卡尔曼滤波的关节空间多传感器信息融合算法.新算法首先预测机器人的工作空间状态,然后通过逆向运动学将其解算到关节空间,随之在关节空间内进行数据融合,以提高脚轮运动精度,最后利用脚轮运动的最优估计来更新各关节角度和机器人里程计信息.仿真实验验证了该算法可有效减少脚轮运动的误差,使机器人的运动学矩阵更接近其真实状态,从而提升了机器人的里程计和速度控制精度.
The kinematics parameters of the omni-directional mobile robot based on powered caster is related to the position of the caster steering joint. Thus, the error of the caster motion will reduce the accuracy of the odometry and the velocity control of robot simultaneously. Since the traditional data fusion algorithm cannot improve the accuracy of caster motion, a joint space multi-sensor information fusion algorithm based on Kalman filter is proposed. Firstly, the algorithm predicts the state of the robot in work space, then solves the joint space by inverse kinematics, and performs data fusion in joint space to improve the accuracy of the caster motion.Finally, the joint angle and the odometry are updated by the optimal estimation of the caster motion. The simulation results prove that the present method can reduce the error of the caster motion and make the kinematics parameters of the robot closer to its real state, thus improve the accuracy of the odometry and the velocity control of robot.
The kinematics parameters of the omni-directional mobile robot based on powered caster is related to the position of the caster steering joint. Thus, the error of the caster motion will reduce the accuracy of the odometry and the velocity control of robot simultaneously. Since the traditional data fusion algorithm cannot improve the accuracy of caster motion, a joint space multi-sensor information fusion algorithm based on Kalman filter is proposed. Firstly, the algorithm predicts the state of the robot in work space, then solves the joint space by inverse kinematics, and performs data fusion in joint space to improve the accuracy of the caster motion.Finally, the joint angle and the odometry are updated by the optimal estimation of the caster motion. The simulation results prove that the present method can reduce the error of the caster motion and make the kinematics parameters of the robot closer to its real state, thus improve the accuracy of the odometry and the velocity control of robot.
引文
[1]曲乃恒,杨桂林,郑天江.基于解耦式主动万向脚轮的全向移动机器人设计[J].中国机械工程,2015,26(19):2601-2605.
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[5]Suliman C,Moldoveanu F.Unscented Kalman filter position estimation for an autonomous mobile robot[J].Bulletin of the Transilvania University of Bra?ov,2010,3(52):299-304.
[6]Fuke Y,Krotkov E.Dead reckoning for a lunar rover on uneven terrain[C].IEEE International Conference on Robotics and Automation,1996:411-416.
[7]Lee T,Shin J,Cho D.Position estimation for mobile robot using in-plane 3-axis IMU and active beacon[C].IEEE International Symposium on Industrial Electronics,2009:1956-1961.
[8]Tamas L,Lazea G,Robotin R,et al.State estimation based on Kalman filtering techniques in navigation[C].IEEE International Conference on Automation,Quality and Testing,Robotics,2008:147-152.
[9]Felipe E,Carlos S,Marta M R,et al.Odometry and laser scanner fusion based on a discrete extended Kalman filter for robotic platooning guidance[J].Sensors,2011,11(9):8339-8357.
[10]胡佳佳,周翟和,沈超,等.一种自适应残差补偿算法在移动机器人姿态估计中的应用研究[J].传感技术学报,2015,28(3):363-366.
[11]Kalman R E.A new approach to linear filtering and prediction problems[J].Journal of Basic Engineering,1960,82(1):35-45.
[12]Welch G.An introduction to the Kalman filter[J].Course Notes of ACM Siggraph,1995,8(7):127-132.
[2]Yin J,Yang G,Zhao F,et al.Motion planning implemented in ROS for omni-directional wheeled mobile robot[C].IEEE International Conference on Information and Automation,2015:2695-2700.
[3]Yang G L,LI Y P,Lim T M.Decoupled powered caster wheel for omnidirectional mobile platforms[C].IEEEInternational Conference on Industrial Electronics and Applications(ICIEA),Xi’an,China,2014:954-959.
[4]El-Shenawy A K,Wellenreuther A,Badreddin E.Practical evaluation for two different holonomic wheeled mobile robots[C].IEEE International Conference on Systems,Man and Cybernetics,2008:3102-3107.
[5]Suliman C,Moldoveanu F.Unscented Kalman filter position estimation for an autonomous mobile robot[J].Bulletin of the Transilvania University of Bra?ov,2010,3(52):299-304.
[6]Fuke Y,Krotkov E.Dead reckoning for a lunar rover on uneven terrain[C].IEEE International Conference on Robotics and Automation,1996:411-416.
[7]Lee T,Shin J,Cho D.Position estimation for mobile robot using in-plane 3-axis IMU and active beacon[C].IEEE International Symposium on Industrial Electronics,2009:1956-1961.
[8]Tamas L,Lazea G,Robotin R,et al.State estimation based on Kalman filtering techniques in navigation[C].IEEE International Conference on Automation,Quality and Testing,Robotics,2008:147-152.
[9]Felipe E,Carlos S,Marta M R,et al.Odometry and laser scanner fusion based on a discrete extended Kalman filter for robotic platooning guidance[J].Sensors,2011,11(9):8339-8357.
[10]胡佳佳,周翟和,沈超,等.一种自适应残差补偿算法在移动机器人姿态估计中的应用研究[J].传感技术学报,2015,28(3):363-366.
[11]Kalman R E.A new approach to linear filtering and prediction problems[J].Journal of Basic Engineering,1960,82(1):35-45.
[12]Welch G.An introduction to the Kalman filter[J].Course Notes of ACM Siggraph,1995,8(7):127-132.
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