Mobile Robot Localization Using Fuzzy Neural Network Based Extended Kalman Filter

This paper proposes a novel approach to improve the performance of the extended Kalman filter (EKF) for the problem of mobile robot localization. A fuzzy logic system is employed to continuous-ly adjust the noise covariance matrices of the filter. A neural network is implemented to regulate the membership functions of the antecedent and consequent parts of the fuzzy rules. The aim is to gain the accuracy and avoid the divergence of the EKF when the noise covariance matrices are fixed or wrongly determined. Simulations and experiments have been conducted. The results show that the proposed filter is better than the EKF in localizing the mobile robot.

Recognition of 26 Degrees of Freedom of Hands Using Model-based approach and Depth-Color Images

In this study, we present an model-based approach to recognize full 26 degrees of freedom of a human hand. Input data include RGB-D images acquired from a Kinect camera and a 3D model of the hand constructed from its anatomy and graphical matrices. A cost function is then defined so that its minimum value is achieved when the model and observation images are matched. To solve the optimization problem in 26 dimensional space, the particle swarm optimization algorimth with improvements are used. In addition, parallel computation in graphical processing units (GPU) is utilized to handle computationally expensive tasks. Simulation and experimental results show that the system can recognize 26 degrees of freedom of hands with the processing time of 0.8 seconds per frame. The algorithm is robust to noise and the hardware requirement is simple with a single camera.

Using multiple sensors for autonomous mobile robot navigation

This paper presents the use of multi-sensor measurement system to guide autonomous mobile robot in the house. The system allows the 3D image acquisition to global mapping, and algorithms to reduce the dimensionality of images to 2D global map navigation, trajectory design approach using the Lyapunov function method and avoid obstacles by the potential energy can also be presented. Also, sensor integrated method based on extended Kalman filter allows us to identify the exact location and orientation of the robot in the presence of interference from the environment.

Stabilization control of networked mobile robot using past observation-based preditive filter

This paper addresses the stabilization control problem for networked mobile robot subject to communication delay. A new state estimation filter namely past observation-based predictive filter is developed. This filter enables the prediction of system state from delayed measurement. The state estimator combined with developed control laws ensures the asymptotic stability of the networked system. Simulations with parameters extracted from a real robot system were conducted and results confirmed the correctness as well as applicability of proposed approach.

Localization of Internet-based Mobile Robot

This paper presents a new optimal filter namely past observation-based extended Kalman filter for the problem of localization of Internet-based mobile robot in which the control input and the feedback measurement suffer from communication delay. The filter operates through two phases: the time update and the data correction. The time update predicts the robot position by reformulating the kinematics model to be non-memoryless. The correction step corrects the prediction by extrapolating the delayed measurement to the present and then incorporating it to the being estimate as there is no delay. The optimality of the incorporation is ensured by the derivation of a multiplier that reflects the relevance of past observations to the present. Simulations in MATLAB and experiments in a real networked robot system confirm the validity of the proposed approach.

Localization of networked robot systems subject to random delay and packet loss

This paper deals with the localization problem of mobile robot subject to communication delay and packet loss. The delay and loss may appear in a random fashion in both control inputs and observation measurements. A unified state-space representation is constructed to describe these mixed uncertainties. Based on it, the optimal linear estimator is developed. The main idea is the derivation of a relevance factor to incorporate delayed measurements to the being estimate. The estimator is then extended for nonlinear systems. The performance of this method is tested within the simulations in MATLAB and the experiments in a real robot system. The good localization results prove the efficiency of the method for the purpose of localization of networked mobile robot.

Localization of a unicycle-like mobile robot using LRF and omni-directional camera

This paper addresses the localization problem. The extended Kalman filter (EKF) is employed to localize a unicycle-like mobile robot equipped with a laser range finder (LRF) sensor and an omni-directional camera. The LRF is used to scan the environment which is described with line segments. The segments are extracted by a modified least square quadratic method in which a dynamic threshold is injected. The camera is employed to determine the robot's orientation. The prediction step of the EKF is performed by extracting parameters from the kinematic model and input signal of the robot. The correction step is conducted with the implementation of a line matching algorithm and the comparison between line's parameters of the local and global maps. In the line matching algorithm, a conversion matrix is introduced to reduce the computation cost. Experiments have been carried out in a real mobile robot system and the results prove the applicability of the method for the purpose of localization.