Non Linear State Estimation of a Multi Axis Surgical Robot Srikrishnan Ramadurai Chair of the Supervisory Committee: Professor Blake Hannaford Electrical Engineering Minimally invasive surgical robots often have cable driven power transmission mechanisms. An example is the RAVEN surgical robot developed at the Biorobotics Laboratory, University of Washington for research on robotic surgery. The...

The main objective of the introduced study is to design an adaptive Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) tightly-coupled integration system that can provide more reliable navigation solutions by making full use of an adaptive Kalman filter (AKF) and satellite selection algorithm. To achieve this goal, we develop a novel redundant measurement noise covariance ...

The evolution of performance degradation has become a major obstacle to the long-life operation Solid Oxide Fuel Cell (SOFC) system. feasibility employing resistance assess State Health (SOH) is proposed and verified. In addition, real-time Unscented Kalman Filter (UKF) based SOH estimation method further eliminate disturbance calculating directly utilizing measurement electric balance model. r...

The unscented Kalman filter (UKF) is formulated for the continuous-discrete state space model. The exact moment equations are solved approximately by using the unscented transform (UT) and the measurement update is obtained by computing the normal correlation, again using the UT. In contrast to the usual treatment, the system and measurement noise sequences are included from the start and are n...

In-motion alignment of a SDINS/GPS integrated system, determining the angular relationship between the navigation frame and the body frame is a necessary process. This paper describes the in-motion alignment procedure for a low-cost GPS/INS integrated system using the Unscented Kalman Filter (UKF) and Central Difference Kalman Filter (CDKF). The UKF and CDKF are used to implement the in-motion ...

In this paper, an unscented Kalman filter (UKF) for curvilinear motions in an interacting multiple model (IMM) algorithm to track a maneuvering vehicle on a road is investigated. Driving patterns of vehicles on a road are modeled as stochastic hybrid systems. In order to track the maneuvering vehicles, two kinematic models are derived: A constant velocity model for linear motions and a constant...

This paper proposes a parameter estimation method for improving the navigation accuracy of an unmanned underwater vehicle, which is equipped with a doppler, a gyro, a depth sensor and a GPS, using an Unscented Kalman Filter (UKF). The error sources are analyzed, and then a kinematical model is constructed accordingly. Using an UKF, the doppler scaling factor and the rotational alignment offset ...

People tracking is an essential part for modern service robots. In this paper we compare three different Bayesian estimators to perform such task: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) Particle Filter. We give a brief explanation of each technique and describe the system implemented to perform people tracking with a mobile robot usi...

An accurate and low-cost level measurement method based on acoustic resonance is presented. The method is useful in the cases that measurements are noisy which might be due to environment noise or due to low-cost instruments used in the measurement process. An iterative nonlinear filtering algorithm, called the “Unscented Kalman filter” (UKF) has been employed to obtain a good estimate of the n...