Yuichi Motai

Hand–Eye Calibration Applied to Viewpoint Selection for Robotic Vision

Viewpoint calibration is a method to manipulate hand-eye for generating calibration parameters for active viewpoint control and object grasping. In robot vision applications, accurate vision sensor calibration and robust vision-based robot control are essential for developing an intelligent and autonomous robotic system. This paper presents a new approach to hand-eye robotic calibration for vision-based object modeling and grasping. Our method provides a 1.0-pixel level of image registration accuracy when a standard Puma/Kawasaki robot generates an arbitrary viewpoint. To attain this accuracy, our new formalism of hand-eye calibration deals with a lens distortion model of a vision sensor. Our most distinguished approach of optimizing intrinsic parameters is to utilize a new parameter estimation algorithm using an extended Kalman filter. Most previous approaches did not even consider the optimal estimates of the intrinsic and extrinsic camera parameters, or chose one of the estimates obtained from multiple solutions, which caused a large amount of estimation error in hand-eye calibration. We demonstrate the power of this new method for: (1) generating 3-D object models using an interactive 3-D modeling editor; (2) recognizing 3-D objects using stereovision systems; and (3) grasping 3-D objects using a manipulator. Experimental results using Puma and Kawasaki robots are shown.

Human tracking from a mobile agent: Optical flow and Kalman filter arbitration

Tracking moving objects is one of the most important but problematic features of motion analysis and understanding. The Kalman filter (KF) has commonly been used for estimation and prediction of the target position in succeeding frames. In this paper, we propose a novel and efficient method of tracking, which performs well even when the target takes a sudden turn during its motion. The proposed method arbitrates between KF and Optical flow (OF) to improve the tracking performance. Our system utilizes a laser to measure the distance to the nearest obstacle and an infrared camera to find the target. The relative data is then fused with the Arbitrate OFKF filter to perform real-time tracking. Experimental results show our suggested approach is very effective and reliable for estimating and tracking moving objects.

Improving Estimation of Vehicle's Trajectory Using the Latest Global Positioning System With Kalman Filtering

This paper proposes several extensive methods to predict the future location of an automobile. The goals of this paper are to find a more accurate way to predict the future location of an automobile by 3 s ahead, so that the prediction error can be greatly reduced with the innovative idea of merging global-positioning-system (GPS) data with geographic-information-system (GIS) data. The improvement starts by applying existing techniques to extrapolate the current GPS location. Comprehensive Kalman filters (KFs) are implemented to deal with inaccuracy in the different identified possible states an automobile could be found in, which are identified as constant locations, constant velocity, constant acceleration, and constant jerks. Then, the KFs are set up to be part of a interacting-multiple-model (IMM) system that provides the predicted future location of the automobile. To reduce the prediction error of the IMM setup, this paper imports an iterated geometrical error-detection method based on GIS data. The assumption that the automobile will remain on the road is made; therefore, the predictions of future locations that fall outside are corrected accordingly, making a great reduction to the prediction error. The actual experimental results validate our proposed system by reducing the prediction error to around half of what it would be without the use of GIS data.

Multiple model framework of adaptive extended kalman filtering for predicting vehicle location

A multiple-model framework of adaptive extended Kalman filters (EKF) for predicting vehicle position with the aid of Global Positioning System (GPS) data is proposed to improve existing collision avoidance systems. A better prediction model for vehicle positions provides more accurate collision warnings in situations that current systems can not handle correctly. The multiple model adaptive estimation system (MMAE) algorithm is applied to the integration of GPS measurements to improve the efficiency and performance. This paper evaluates the multiple-model system in different scenarios and compares it to other systems before discussing possible improvements by combining it with other systems for predicting vehicle location

Predictive fuzzy control for a mobile robot with nonholonomic constraints

Automatic control of a mobile robot with nonholonomic constraints normally depends on complex signal processing mechanisms, e.g. predictive control. This paper presents a new trajectory tracking method for a mobile robot by combining predictive control and fuzzy control. To overcome the time delay caused by the slow response of the sensor, the algorithm employs predictive control to predict the position and orientation of the robot. In addition, fuzzy control is adopted to deal with nonlinear characteristic of the system. The advantages of this predictive fuzzy controller include high reliability for a slow sensor response, small error of absolute tracking, availability of a linearized predictive model and simplified fuzzy rules, which reduce the computing complexity. In our experiments, we applied this control method to the soccer robot. Accuracy and convergent performance was compared with a traditional PID controller, as well as with a conventional fuzzy controller. The experiment results demonstrated the feasibility and advantages of this predictive fuzzy control on the trajectory tracking of a mobile robot

Respiratory Motion Estimation With Hybrid Implementation of Extended Kalman Filter

The extended Kalman filter (EKF) can be used for the purpose of training nonlinear neural networks to perform desired input-output mappings. To improve the computational requirements of the EKF, Puskorius proposed the decoupled EKF (DEKF) as a practical remedy for the proper management of computational resources. This approach, however, sacrifices computational accuracy of estimates because it ignores the interactions between the estimates of mutually exclusive weights. To overcome such a limitation, therefore, we proposed hybrid implementation based on EKF (HEKF) for respiratory motion estimation, which uses the channel number for the mutually exclusive groups and the coupling technique to compensate the computational accuracy. Moreover, the authors restricted to a DEKF algorithm in which the weights connecting the inputs to a node are grouped together. If there are multiple input training sequences with respect to the time stamp, the complexity can increase by the power of the input channel number. To improve the computational complexity, we split the complicated neural network into a couple of simple neural networks to adjust separate input channels. The experimental results validated that the prediction overshoot of the proposed HEKF was improved by 62.95% in the average prediction overshoot values. The proposed HEKF showed a better performance of 52.40% improvement in the average of the prediction time horizon. We have evaluated that the proposed HEKF can outperform DEKF by comparing the prediction overshoot values, the performance of the tracking estimation value, and the normalized root-mean-squared error.

Head Orientation Prediction: Delta Quaternions Versus Quaternions

Display lag in simulation environments with helmet-mounted displays causes a loss of immersion that degrades the value of virtual/augmented reality training simulators. Simulators use predictive tracking to compensate for display lag, preparing display updates based on the anticipated head motion. This paper proposes a new method for predicting head orientation using a delta quaternion (DQ)-based extended Kalman filter (EKF) and compares the performance to a quaternion EKF. The proposed framework operates on the change in quaternion between consecutive data frames (the DQ), which avoids the heavy computational burden of the quaternion motion equation. Head velocity is estimated from the DQ by an EKF and then used to predict future head orientation. We have tested the new framework with captured head motion data and compared it with the computationally expensive quaternion filter. Experimental results indicate that the proposed DQ method provides the accuracy of the quaternion method without the heavy computational burden.

Predictive fuzzy logic controller for trajectory tracking of a mobile robot

This paper presents a new tracking method for a mobile robot by combining predictive control and fuzzy logic control. Trajectory tracking of autonomous mobile robots usually has non-linear time-varying characteristics and is often perturbed by additive noise. To overcome the time delay caused by the slow response of the sensor, the algorithm uses predictive control, which predicts the position and orientation of the robot. In addition, fuzzy control is used to deal with the non-linear characteristics of the system. Experimental results demonstrate the feasibility and advantages of this predictive fuzzy control on the trajectory tracking of a mobile robot.

Kernel Association for Classification and Prediction: A Survey

Kernel association (KA) in statistical pattern recognition used for classification and prediction have recently emerged in a machine learning and signal processing context. This survey outlines the latest trends and innovations of a kernel framework for big data analysis. KA topics include offline learning, distributed database, online learning, and its prediction. The structural presentation and the comprehensive list of references are geared to provide a useful overview of this evolving field for both specialists and relevant scholars.

Principal Composite Kernel Feature Analysis: Data-Dependent Kernel Approach

Principal composite kernel feature analysis (PC-KFA) is presented to show kernel adaptations for nonlinear features of medical image data sets (MIDS) in computer-aided diagnosis (CAD). The proposed algorithm PC-KFA has extended the existing studies on kernel feature analysis (KFA), which extracts salient features from a sample of unclassified patterns by use of a kernel method. The principal composite process for PC-KFA herein has been applied to kernel principal component analysis [34] and to our previously developed accelerated kernel feature analysis [20]. Unlike other kernel-based feature selection algorithms, PC-KFA iteratively constructs a linear subspace of a high-dimensional feature space by maximizing a variance condition for the nonlinearly transformed samples, which we call data-dependent kernel approach. The resulting kernel subspace can be first chosen by principal component analysis, and then be processed for composite kernel subspace through the efficient combination representations used for further reconstruction and classification. Numerical experiments based on several MID feature spaces of cancer CAD data have shown that PC-KFA generates efficient and an effective feature representation, and has yielded a better classification performance for the proposed composite kernel subspace using a simple pattern classifier.