Position estimation for manipulators based on multisensor fusion

Maximizing the tracking performance of an industrial manipulators requires an accurate position estimation of the end-effector. By using the motor position from the encoder, the accuracy of end-effector position estimation is affected by the gear mechanisms employed by industrial manipulators. An accelerometer is fixed on the end-effector to get measurements that reflect the actual end-effector motion and the effect of unmodelled dynamics. In the paper a multisensor fusion method is presented to achieve good estimates of the position of the end-effector fusing the measurements from an accelerometer, a gyroscope and the encoders of joints' motors. Since the robot dynamics and measurements are highly nonlinear and the measurement noise is non-Gaussian, the particle filter provides a solution to the sensor fusion problem. Simulation research results show an improvement in position accuracy using proposed method.

[1]  Dan S. Necsulescu,et al.  Extended Kalman filter-based sensor fusion for operational space control of a robot arm , 2002, IEEE Trans. Instrum. Meas..

[2]  Anders Robertsson,et al.  Sensor Fusion for Compliant Robot Motion Control , 2008, IEEE Transactions on Robotics.

[3]  Huosheng Hu,et al.  Integration of Vision and Inertial Sensors for 3D Arm Motion Tracking in Home-based Rehabilitation , 2007, Int. J. Robotics Res..

[4]  Friedrich M. Wahl,et al.  6D Force and Acceleration Sensor Fusion for Compliant Manipulation Control , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Gerasimos G. Rigatos,et al.  Particle Filtering for State Estimation in Nonlinear Industrial Systems , 2009, IEEE Transactions on Instrumentation and Measurement.

[6]  Anders Robertsson,et al.  Force and Acceleration Sensor Fusion for Compliant Robot Motion Control , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[7]  Sarat C. Dass,et al.  Particle-Filter-Based Multisensor Fusion for Solving Low-Frequency Electromagnetic NDE Inverse Problems , 2011, IEEE Transactions on Instrumentation and Measurement.

[8]  Pascal Vasseur,et al.  Introduction to Multisensor Data Fusion , 2005, The Industrial Information Technology Handbook.

[9]  Huiying Chen,et al.  Data fusion for three-dimensional tracking using particle techniques , 2008 .

[10]  Soo Jeon,et al.  Kinematic Kalman Filter (KKF) for Robot End-Effector Sensing , 2009 .

[11]  Shu-Li Sun,et al.  Multi-sensor optimal information fusion Kalman filter , 2004, Autom..

[12]  Kristine L. Bell,et al.  A Tutorial on Particle Filters for Online Nonlinear/NonGaussian Bayesian Tracking , 2007 .

[13]  Chris Lightcap,et al.  An Extended Kalman Filter for Real-Time Estimation and Control of a Rigid-Link Flexible-Joint Manipulator , 2010, IEEE Transactions on Control Systems Technology.