Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Discrete H/sub /spl infin// filter design with application to speech enhancement
ICASSP '95 Proceedings of the Acoustics, Speech, and Signal Processing, 1995. on International Conference - Volume 02
Terrain-based vehicle orientation estimation combining vision and inertial measurements
Journal of Field Robotics
Game theory approach to discrete H∞ filter design
IEEE Transactions on Signal Processing
Sensor data fusion for body state estimation in a hexapod robot with dynamical gaits
IEEE Transactions on Robotics
Brief Design and analysis of discrete-time robust Kalman filters
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
A biomimetic underwater microrobot with multifunctional locomotion
Robotics and Autonomous Systems
Hi-index | 0.00 |
This paper compares the performances of different sensor fusion algorithms in a shape memory alloy (SMA)-based hexapod biomimetic robot, SMABOT IV. The algorithms considered include a Kalman filter that minimizes the estimation error variance, an H"~ filter that minimizes the worst-case estimation error, and a robust mixed Kalman/H"~ filter that allows for uncertainties in both the system and measurement matrices. The sensors installed on the robot include an inertial measurement unit and an electric compass sensor for inertial guidance. In addition, a stride-length-estimation algorithm for an SMA-based legged robot was proposed to establish the legged odometry of the robot. Allan variance analysis is employed to identify the noise sources of inertial sensors, and the calculated variance values are used to design the parameters of the Kalman filter algorithm. Finally, experimental results of two-dimensional navigation are presented, and the performances of three sensor fusion algorithms are compared. The results indicate that after identifying the noise characteristics of inertial sensors, the Kalman filter provides the best performance.