Category: Publications

Real-Time Shape Estimation for Wire-Driven Flexible Robots with Multiple Bending Sections Based on Quadratic Bezier Curves

Abstract

The wire-driven flexible robot with multiple bending sections is an efficient approach for the minimally invasive surgery and diagnosis. It can function properly in the complicated and restrained environment. One drawback of this technology is that the real time positional and shape information cannot be well estimated. In order to settle this limitation, we proposed a novel shape estimation method for a wire-driven flexible robot with multiple bending sections in this paper. Each bending section can be controlled independently to deform as an arc with different curvature. This method is based on the positional and directional information of limited specific joints on the robot, which can be estimated with an effective positioning method, such as electromagnetic tracking method. The number and position of these specific joints are only determined by the number of sections. Based on the positional and directional information, as well as the curve length information, the shape reconstruction algorithm can be carried out by fitting multiple quadratic B´ezier curves. Real time shape sensing platform is built to verify the proposed method. Experimental results show that the method works well and the mean position error is 1.7mm.

full text

Abstract

This study proposes a novel non-negative matrix factorisation (NMF) variant L1/2-NMF after visualisation and analysis of the process of target recognition via NMF for synthetic aperture radar (SAR) images. NMF has been applied to obtain pattern feature in SAR images. This study considers the intrinsic character and the physical meaning of NMF feature when applied for SAR automatic target recognition. At the base of obtaining the linear relationship between the sample to be recognised and the train samples, the whole recognition process is detailed and vividly visualised. Meanwhile, lots of researches have been done to improve NMF methods by enforcing sparse constraint with L1-norm, such as non-negative sparse coding (NNSC), local NMF and sparse NMF. Compared with L1-norm, L1/2-norm has been shown to have a more natural sparseness. In this study, a novel variant of NMF with L1/2 constraint, called L1/2-NMF is proposed, and is carried out a thorough study by applying it in SAR target recognition. Experimental results on MSTAR public database show that both the basis and coding matrices obtained by L1/2-NMF have higher sparseness than those obtained by NMF, NNSC and NMF with sparseness constraints (NMFsc). The recognition results demonstrate that the L1/2-NMF outperforms NNSC, NMFsc and non-smooth NMF.

full text

Abstract

Anomaly detection is an important research direction in the field of data mining and industrial dataset preprocess. The paper proposed a kernel neighbor densitydefinition with parallel computing mechanism for anomaly detection algorithm. The kernel neighbor density formula calculates the density of points in high dimensional space. In our definition, we adopt the median operation because the breakdown point of the median is the largest possible. So thedefinition could be a very robust estimate of the data location, and parallel computing mechanism is introduced to improve the efficiency of algorithms. We use two real datasets and three different kernel functions to evaluate the performance of algorithms. The experiment results confirm that the presenteddefinition of kernel neighbor density improves the performance of algorithms and the Gaussian kernel function has the best effect

full text

Accurate high-temperature measurement is very important for process monitor of the industrial system. Because the temperature of industrial hot object may be several thousand centigrade, for example, welding molten pool, the measurement range and the high price limit the application of traditional high-temperature measurement in the field. According to the colorimetric theory, we propose a low-cost contactless sensor fusion method for estimating the high-temperature of hot object. The proposed method adopts the ordinary camera and the filters to obtain the images of high temperature object at different wavelengths. Then, the nonlinear partial least squares is adopted to predict the temperature based on the gray values of the images. The proposed method maps the input space to the high dimensional space and uses the parameters of the prediction model are estimated by the iterative optimization. The proposed method could deal with the high correlation between inputs to ensure the generalization of prediction model. Since the temperature of the filament of a incandescent lamp can reach several thousand centigrade, the filament images at different voltage obtained in our test platform are used in the experiments. The results verify that the proposed method has higher effectiveness and can be applied for the high-temperature measurement correctly.

full text

Abstract

Mandibular mobility plays a significant role in human daily lives, enabling food intake, respiration, speaking, and other oral activities. However, as for the patients suffering from mandibular mobility disorders, their mandible functions are deteriorated, which severely affects their quality of life. In this paper, we present a new solution to recover mandibular mobility: A soft oral rehabilitation robot (SORR), which is actuated by a novel type of soft pneumatic actuator (SPA). After identifying the biometrics of the human mandible, we illustrate the application-oriented design of the robot and the SPA. A static model of the SPA was established to predict its behavior and eligibility for the application. In the experimental characterization, we measured the elongation and force output of the SPA, and the detailed results are presented. The comparison and analysis of the results provide physical insight into the mechanisms of the SPA. As the first application of soft robot inside human body, this work enlightens profound application potentials of the silicone-based SPAs.

full text

Abstract

Wire-driven flexible robots are efficient devices for minimally invasive surgery, since they can work well in complex and confined environments. However, the real-time position and shape information of the robot cannot be well estimated, especially when there is a payload on the end effector. In this paper, a novel tracking and shape reconstruction method for a wire-driven flexible robot is proposed. The proposed method utilizes the length of the robot as well as the positional and directional information measured by an electromagnetic sensor mounted at the distal end of the robot. A reconstruction algorithm, which is based on a three order Bézier curve, is then carried out utilizing the positional and directional information along with the length information of the robot. This method can provide good tracking results and high shape reconstruction accuracy with limited modification of the robot. Compared with other reconstruction methods, the proposed method does not rely on any mechanical model of the robot. Therefore, the method works well with an unknown payload. The feasibility of the proposed method is verified by both simulation and experimental results.

full text

Abstract

Central catadioptric omnidirectional images exhibit serious nonlinear distortions due to quadratic mirrors involved. Conventional visual features developed based on the perspective model are hard to achieve a satisfactory performance when directly applied to the distorted omnidirectional image. This paper presents a parameterized neighborhood model to efficiently calculate the adaptive neighborhood of an object based on the measurable radial distance in image plane. On the basis of the parameterized neighborhood model, a distortion invariant joint-feature framework implemented with contour-color fragment mixture model of Gaussian is proposed for visual tracking in catadioptric omnidirectional camera system. Under the framework of Gaussian Mixture Model, the problem of feature matching is converted into feature clustering. A weight contribution mechanism is presented to flexibly weight the fragments based on their responses, which makes the system robustly guided by limited visible fragments even when serious partial occlusion happens. The experiments validate the performance of the proposed algorithm.

full text

Abstract

In this paper, optimal remote control strategies to a novel master-slave flexible manipulator (FM) are presented. The system contains a constrained tendon-driven serpentine manipulator (CTSM), the base platform, a microcontroller, a PC and the Novint Falcon haptic input device. The bending of the CTSM backbone is controlled by the tendons and the length of the bending section is controlled by a constraint tube. Compared with other FMs, the CTSM has an improved workspace and dexterity. The CTSM is controlled using the Novint Falcon haptic input device, and two remote control modes are implemented, one is direct mapping and the other is incremental mode. Two optimal remote control strategies are proposed for the CTSM, i.e., highest stiffness and minimal movement. Experimental results show that the direct mapping is appropriated for fast movements and the incremental mode is adequate for accurate tasks. In combination with the optimal control strategies, a set of different applications for the CTSM are presented.

full text