Our team members presented research publications and one of them won best paper finalist during IEEE ROBIO 2018 (International Conference on Robotics and Biomimetics).
OrumBot: Origami-based Deformable Robot Inspired By An Umbrella Structure, best paper finalist.
Hritwick Banerjee, Sakshi Kakde, Hongliang Ren
Finger Movement Classification from Myoelectric Signals Using Convolutional Neural Networks
Venkatesh Bharadwaj S, Mobarakol Islam, Wei Zhang, Hongliang Ren
Preliminary Design and Performance Test of Tendon-Driven Origami-Inspired Soft PeristalticRobot
HRITWICK BANERJEE, Neha Pusalkar, Hongliang Ren
Our team members were presenting research publications and doing lab demos during IEEE ICARM 2018 (The IEEE International Conference on Advanced Robotics and Mechatronics).
|Sri Sai Krishna Suraj Narapareddi, Vineeth Muppalla, Parita Sanghani and Hongliang Ren||Comparative Study of Unsupervised Segmentation Algorithms for Delineating Glioblastoma Multiforme Tumour|
|Avi Srivastava, Hongliang Ren and Liang Qiu||Preoperative-Image Guided Neurosurgical Navigation Procedures with Electromagnetic Tracking: An Effective Pipeline and A Cadaver Study|
|Abhishek Bamotra and Hongliang Ren||Characterization and Fabrication of Novel Soft Compliant Robotic End-Effectors with Negative Pressure and Mechanical Advantages|
|Hritwick Banerjee, Oh Yao Wei Aaron, Bok Seng Yeow and Hongliang Ren||Fabrication and Initial Cadaveric Trials of Bi-directional Soft Hydrogel Robotic Benders Aiming for Biocompatible Robot-Tissue Interactions|
|Shradha Singhvi and Hongliang Ren||Comparative Study of Motion Recognition with Temporal Modelling of Electromyography for Thumb and Index Finger Movements aiming for Wearable Robotic Finger Exercises|
|Wenjun Xu and Hongliang Ren||Human Palpation Behavior Modeling with Mixture Models: Towards Autonomous Robotic Palpation|
In this paper, a novel constrained tendon-driven serpentine manipulator (CTSM) suited for minimally invasive surgery is presented. It comprises a flexible backbone, a set of controlling tendons and a constraint. In the CTSM not only the curvature of the bending section can be controlled but also the length. Specifically, the curvature is controlled by the tendons, and the length is controlled by a constraint tube, which is translational and is concentric with the flexible backbone. The kinematic model of the CTSM is developed based on the piecewise constant curvature assumption. An analysis shows that by introducing the translational constraint both the workspace and dexterity of the manipulator are improved. The stiffer the constraint the larger the workspace expansion and the smaller the dexterity enhancement. A prototype is developed and the experimental results validate the design idea and analysis.
In this paper, we present a novel flexible endoscope which is well suited to minimally invasive cardiac surgery (MICS). It is named ‘the Cardioscope’. The Cardioscope is composed of a handle, a rigid shaft, a flexible section and the imaging system. The flexible section is composed of an elastic tube, a number of spacing discs, a constraint tube, and four tendons. Compared with other flexible endoscopes, the Cardioscope is much more dexterous. The maximum bending angle of the Cardioscope is 190°. Ex-vivo tests show that the cardioscope is well suited to (MICS), it provides the much larger scope of vision than rigid endoscopes and provides good manipulation inside the confined environment. In the test, the Cardioscope successfully explores the full heart through a single hole, which shows the design is promising. Despite it was designed for MICS, the Cardioscope can also be applied to other minimally invasive surgeries, such as laparoscopy, neurosurgery, transnasal and transoral surgery. Copyright © 2015 by ASME Country-Specific Mortality and Growth Failure in Infancy and Young Children and Association With Material Stature Use interactive graphics and maps to view and sort country-specific infant and early childhood mortality and growth failure data and their association with maternal
With the advancement of minimally invasive surgery, there has been increasing interest from both industry and academia in developing flexible serpentine manipulators for surgical robotic systems. Due to internal friction and external disturbance when interacting with complex environment, position and shape of the flexile manipulator cannot be estimated solely by kinematic models. Hence, shape tracking of such manipulator is crucial to close the control loop and to study the robot models. In this paper, we propose a marker-based method with stereo vision for tracking the shape of the flexible manipulator by extracting position information of each independent joint. The performance of the algorithm was validated by experiments and was compared with that of electromagnetics tracking method.
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.
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.
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
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.