Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast and Precise Detection of Object Grasping Positions with Eigenvalue Templates

Fast Graspability Evaluation (FGE) has been proposed as a method for detecting grasping positions on objects and is now being used for industrial robots. FGE uses convolution of hand templates with regions on the target object to estimate the optimum grasping posture. However, the hand opening width and rotation angles must be set with high resolution to achieve highly accurate results and the computational load is high. To address that issue, we propose a method in which hand templates are represented in compact form for faster processing by using singular value decomposition. Applying singular value decomposition enables hand templates to be represented as linear combinations of a small number of eigenvalue templates and eigenfunctions. Eigenfunctions take discrete values, but response values can be calculated with arbitrary parameters by fitting a continuous function. Experimental results show that the proposed method reduces computation time by two thirds while maintaining the same detection accuracy as conventional FGE for both parallel hands and three-finger hands.
D2D-Enabled Collaborative Edge Caching and Processing with Adaptive Mobile Video Streaming

Multi-access edge computing (MEC) enables placing video content at the edge of a mobile network with the aim of reducing data traffic in the backhaul network. Direct device-to-device (D2D) communication can further alleviate load from the backhaul network. Both MEC and D2D have already been examined by prior work, but their combination applied to adaptive video streaming have not yet been explored in detail. In this paper, we analyze how enabling D2D jointly with edge computing affects the quality of experience (QoE) of video streaming clients and contributes to reducing the backhaul traffic. To this end, we formulate the problem of jointly maximizing the QoE of the clients and minimizing the backhaul traffic and edge processing as an integer non-linear programming (INLP) optimization model and propose a low-complexity algorithm using self-parameterization technique to solve the problem. The main takeaway from simulation results is that enabling D2D with edge computing reduces the backhaul traffic by approximately 18% and edge processing by 30% on average while maintaining roughly the same average video bitrate per client compared to edge computing without D2D. Our results provide a guideline for system designers to judge the effectiveness of enabling D2D into MEC in the next generation of 5G mobile networks.
RCM-SLAM: Visual localisation and mapping under remote centre of motion constraints

In robotic surgery the motion of instruments and the laparoscopic camera is constrained by their insertion ports, i. e. a remote centre of motion (RCM). We propose a Simultaneous Localisation and Mapping (SLAM) approach that estimates laparoscopic camera motion under RCM constraints. To achieve this we derive a minimal solver for the absolute camera pose given two 2D-3D point correspondences (RCM-PnP) and also a bundle adjustment optimiser that refines camera poses within an RCM-constrained parameterisation. These two methods are used together with previous work on relative pose estimation under RCM [1] to assemble a SLAM pipeline suitable for robotic surgery. Our simulations show that RCM-PnP outperforms conventional PnP for a wide noise range in the RCM position. Results with video footage from a robotic prostatectomy show that RCM constraints significantly improve camera pose estimation.
Multi-Task Template Matching for Object Detection, Segmentation and Pose Estimation Using Depth Images

Template matching has been shown to accurately estimate the pose of a new object given a limited number of samples. However, pose estimation of occluded objects is still challenging. Furthermore, many robot application domains encounter texture-less objects for which depth images are more suitable than color images. In this paper, we propose a novel framework, Multi-Task Template Matching (MTTM), that finds the nearest template of a target object from a depth image while predicting segmentation masks and a pose transformation between the template and a detected object in the scene using the same feature map of the object region. The proposed feature comparison network computes segmentation masks and pose predictions by comparing feature maps of templates and cropped features of a scene. The segmentation result from this network improves the robustness of the pose estimation by excluding points that do not belong to the object. Experimental results show that MTTM outperforms baseline methods for segmentation and pose estimation of occluded objects despite using only depth images.
A Novel Robotic Suturing System for Flexible Endoscopic Surgery

Perforations in flexible endoscopy are life-threatening. Defect closure or suturing in flexible endoscopy has long been a critical challenge due to the confined space of the access routes and surgical sites, high dexterity and force demands of suturing tasks, as well as critical size and strength requirements of wound closure. This paper introduces a novel robotic suturing system for flexible endoscopic surgery. This system features a flexible, through-the-scope, five-degree-of-freedom robotic suturing instrument. This instrument allows the surgeon to endoscopically manipulate a needle via a master console to create running stitches and knots in flexible endoscopy, which is not possible with existing devices. Successful ex-vivo trials were conducted inside porcine colons to show how surgical stitches and knots can be endoscopically created and secured in a completely new way. This new technology will change the way how surgeons close defects or perforations in flexible endoscopic surgery.
Online Multilayered Motion Planning with Dynamic Constraints for Autonomous Underwater Vehicles

Underwater robots are subject to complex hydro-dynamic forces. These forces define how the vehicle moves, so it is important to consider them when planning trajectories. However, performing motion planning considering the dynamics on the robot’s onboard computer is challenging due to the limited computational resources available. In this paper an efficient motion planning framework for autonomous underwater vehicles (AUVs) is presented. By introducing a loosely coupled multilayered planning design, our framework is able to generate dynamically feasible trajectories while keeping the planning time low enough for online planning. First, a fast path planner operating in a lower-dimensional projected space computes a lead path from the start to the goal configuration. Then, the lead path is used to bias the sampling of a second motion planner, which takes into account all the dynamic constraints. Furthermore, we propose a strategy for online planning that saves computational resources by generating the final trajectory only up to a finite horizon. By using the finite horizon strategy together with the multilayered approach, the sampling of the second planner focuses on regions where good quality solutions are more likely to be found, significantly reducing the planning time. To provide strong safety guarantees our framework also incorporates the conservative approximations of inevitable collision states (icss). finally, we present simulations and experiments using a real underwater robot to demonstrate the capabilities of our framework.
A Broadband and High-Gain Endfire Antenna Array Fed by Air-Substrate Parallel Strip Line

A broadband and high-gain endfire antenna array fed by an air-substrate parallel strip line is proposed in this communication. It has a simple and planar structure consisting of multiple dipole radiating elements and one parallel strip line. By utilizing the center-fed dipole working between the first and third resonances as the radiating element, the antenna array could obtain stable radiating capability over a broad bandwidth. Fed by an air-substrate parallel strip line working on the TEM mode, the antenna array achieves a moderate phase constant, which is helpful for realizing stable endfire radiation. The radiating element separation is optimized to eliminate the high-order spatial harmonics, ensuring single beam radiation. Fabricated by thin copper plates, the merits of the antenna array include lightweight, low cost, and easy fabrication. According to the measured results, the antenna array has a good match and a stable endfire radiation pattern over the operating band from 4 to 10.5 GHz with the measured gain varying from 10.1 to 13.5 dBi.
A Modified Current Differencing Buffered Amplifier and Its Application

This paper proposes the modified current differencing buffered amplifier (MCDBA) with is synthesized by adding input $V_{y}$ into a conventional current differencing buffered amplifier. Therefore, the proposed circuit is more flexible as voltage input used especially. Simulation results using PSpice program at supply of ±0.8V are satisfying. In addition, its application in four quadrant analog multiplier circuit.
Design of a Switched-Capacitor DC-DC Converter with a Multiple Up/Down Ratio

The characteristics of electric energy obtained from the energy harvesting devices is unstable unlike that obtained from a battery or a traditional external power source. This work proposes a DC-DC converter design based on switched-capacitor voltage converter, which constrain unstable output from energy harvester to a certain level. The converter provides ten conversion ratio includes 1/4, 1/3, 1/2, 2/3, 3/4, 4/3, 3/2, 2/1, 3/1, and 4/1 to achieve this operation. The implemented hardware consists of 16 commercial analog switches and four 1uF capacitors. FPGA based controller also implemented for generating PWM signals to control switches. The averaged conversion efficiency is 79% through all conversion ratio for a fixed input voltage of 1V and a fixed output current of 1 m.A.
Efficient Implementation of Strassen's Algorithm for Memory Allocation using AVX Intrinsic on Multi-core Architecture

Strassen's algorithm has fascinated as a popular recursive algorithm for square matrix multiplication with the complexity of $O(n^{2.807})$ in many scientific applications since 1969. However, the Strassen's algorithm has the expense of memory allocation as the critical due to the recursive overhead for creating additional temporary storage of submatrices are required. In this paper, the efficient implementation for Strassen's algorithm is proposed by using Advanced Vector Extension (AVX) to optimize the memory allocation with the minimum level of recursive steps suitable for on a multi-core architecture. The experimental results show that Strassen's algorithm can be improved by using AVX and will be effectively multi-threaded by applying OpenMP to boost the performance. The requirement of a huge memory temporary storage for Strassen algorithm is solved and the number of cache hits is increased. Moreover, the performance of Strassen's algorithm utilizing AVX-256 and AVX-512 are respectively 1.8 and 1. 7 times faster than the open source library, named Eigen. Our proposed implementation can be applied to all matrix multiplication on the shared memory computers that use Strassen's algorithm at the system level for high performance computing.
Environment Driven Underwater Camera-IMU Calibration for Monocular Visual-Inertial SLAM

Most state-of-the-art underwater vision systems are calibrated manually in shallow water and used in open seas without changing. However, the refractivity of the water is adaptively changed depending on the salinity, temperature, depth or other underwater environmental indexes, which inevitably generate the calibration errors and induces incorrectness e.g., for underwater Simultaneously Localization and Mapping (SLAM). To address this issue, in this paper, we propose a new underwater Camera-Inertial Measurement Unit (IMU) calibration model, which just needs to be calibrated once in the air, and then both the intrinsic parameters and extrinsic parameters between the camera and IMU could be automatically calculated depending on the environment indexes. To our best knowledge, this is the first work to consider the underwater Camera-IMU calibration via environmental indexes. We also build a verification platform to validate the effectiveness of our proposed method on real experiments, and use it for underwater monocular Visual-Inertial SLAM.
An Approach of Data Fusion for FuzzyART Based Visual Recognition

Visual Place recognition in robotics is motivated by the challenges of navigation and mapping. Given an image, a robot can map or recognize his environment. The ability to perform well is related to the quality of the information acquired and processed by the system. As the variation of the real world affects significantly the results, it can lead, in some cases, to a poor performance. The improvement is related to changes due to illumination, weather or season at outdoor with abundant features and textures for place recognition as in indoor environment, the robot will have less features or textures to process. In this paper, we present an approach of place recognition inspired by the FuzzyART. The input will be a Normalized Scanline Profile (NSP) and a pre-treatment will be performed by two fuzzy ART subsystems. The matching being the threshold for decision, we will fuse the matching output of the subsystems using a Sugeno-Takagi Fuzzy Inference System. The developed approach was tested on two different dataset to evaluate its ability to perform on outdoor and indoor environments and to limit the use of data storage. The results indicate that correct setting leads to optimal results.
Ex Vivo Breast Tumor Identification: Advances Toward a Silicon-Based Terahertz Near-Field Imaging Sensor

Terahertz waves cover photon energies that are orders of magnitude smaller (0.4-40 meV) than the visual spectrum. Therefore, they provide additional information on intrinsic condensed-matter properties, making them attractive for imaging applications in the life sciences [1], [2]. Furthermore, they do not have an ionizing effect and are considered biologically innocuous. The ever-present water in organic matter strongly absorbs terahertz waves, and subtle changes in the water concentration can be indicative of disease [3]. However, the waves? long wavelength (3mm-30 nm) severely limits their lateral resolution and creates challenges for high-resolution imaging of biological tissue on the cellular level, e.g., for tumor margin identification during cancer surgery.
Exact Modal Characterization of the Non Conservative Non Linear Radial Mass Spring System

Since the spread of robotic systems embedding in their mechanics purposefully designed elastic elements, the interest in characterizing and exploiting non-linear oscillatory behaviors has progressively grown. However, few works so far looked at the problem from the point of view of modal analysis. This is particularly surprising if considered the central role that modal theory had in the development of classic results in analysis and control of linear mechanical systems. With the aim of making a step toward translating and extending this powerful tool to the robotic field, we present the complete modal characterization of a simple yet representative non-linear elastic robot: the 2D planar mass-spring-damper system. Generic non-linear elastic forces and dissipative effects are considered. We provide here exact descriptions of the two non-linear normal modes of the system. We then extend the analysis to generic combinations of the modes in conservative case and for small damping. Simulations are provided to illustrate the theoretical results. This is one of the very firsts applications of normal mode theory to dynamically coupled non-linear systems, and the first exact result in the field.
Compound micromachines powered by acoustic streaming

This paper presents the design, fabrication, and operation of compound micromachines powered by acoustic streaming. The machine components were directly incorporated around pillars serving as shafts without further assembly steps using a single-step in situ polymerization process controlled by a programmable projector. Two strategies were presented for harvesting acoustic energy using sharp-edged structures. The first method is based on on-board pumping of fluids and the second method involves engineering of rotors. The implementation of these strategies resulted in the construction of microscale turbines and engines that can be coupled to gear trains for adaptable transmission of mechanical power. We provide a number of further improvements that may together lead to development of compact yet powerful robotic manipulation systems inside microfluidic devices.
Investigation of Novel Doubly Salient PM Variable Reluctance Resolvers

As rotor position sensors, the variable reluctance (VR) resolvers are playing critical roles in the industrial electric machine drivers. The VR resolvers have a simple structure, therefore, they are reliable and durable under rough operation scenarios. However, the dedicated decoding system requires the voltage signal injection with high-frequency (HF), which must be much higher than the machine operation frequency. The HF injection increases the decoding computation and system cost, especially for the high-speed applications. To avoid applying the HF injection, this paper proposes a new category of VR resolvers by adopting the concept of the doubly salient permanent magnet (PM) motor. The doubly salient PM machines have the magnets and windings on the stator side, thus, they can keep simple and reliable rotors as the VR resolvers. A significant benefit is that the stator-PM can produce back-EMF, which provides the rotor position information. By this way, the HF injection-based VR resolver solution is only serving for low-speeds and can be shut down as long as the PM back-EMF can be detected by the analog circuit. Besides, the proposed linear counterpart is also a novel option for the mover position sensor in the high speed and long distance linear drive system. The modeled examples are analyzed by finite-element analysis (FEA).
A Miniature Suction-Gripper With Passive and Active Microneedle Arrays to Manipulate Peripheral Nerves

We develop a miniature suction-gripper with the goal to realize the novel robotic surgical instrument that can grip slippery and flexible peripheral nerves. In developing the instrument, we place a priority on devising the method that can robustly grip the nerve bundles during the surgical operation for the peripheral nerve. Also, we concentrate to investigate the working principle being able to minimize nerve damages that might be caused when manipulating the nerve. In this study, as the most suitable method to achieve the goal, we scheme to utilize the suction mechanism. Because it can non-invasively grip the nerve based on negative pressure, no external force is applied to the nervous tissues. Therefore the peripheral nerve can be manipulated without serious nerve damage (e.g. crush injury and stretch injury). To improve the gripping ability of the proposed suction gripper, two different types of microneedle arrays are applied to the suction-tips: passive-microneedle (PMN) arrays and active-microneedle (AMN) arrays. Since the most outer membrane of the nerve can be anchored by the penetrated PMN and AMN, the gripper can grip the nerve more robustly. The designed suction-gripper is fabricated as a functional prototype, and its working performances are assessed with in-vitro and in-vivo animal experiments. The experimental results well demonstrate the practical effectiveness of the proposed method and its applicability to the neurosurgical robot for the peripheral nerve.
Dynamic Hilbert Maps: Real-Time Occupancy Predictions in Changing Environments

This paper addresses the problem of learning instantaneous occupancy levels of dynamic environments and predicting future occupancy levels. Due to the complexity of most real environments, such as urban streets or crowded areas, the efficient and robust incorporation of temporal dependencies into otherwise static occupancy models remains a challenge. We propose a method to capture the uncertainty of moving objects and incorporate this uncertainty information into a continuous occupancy map represented in a rich high-dimensional feature space. This data-efficient model not only allows us to learn the occupancy states incrementally, but also makes predictions about what the future occupancy states will be. Experiments performed using 2D and 3D laser data collected from crowded unstructured outdoor environments show that the proposed methodology can accurately predict occupancy states for areas of around 1000 m2 at 10 Hz, making the proposed framework ideal for online applications under real-time constraints.
Soft tactile sensing: retrieving force, torque and contact point information from deformable surfaces

Intrinsic Tactile Sensing (ITS) is a well-established technique, relying on force/torque and geometric surface description to find contact centroids. The method works well for rigid surfaces. However, finding a solution for deformable surfaces is an open issue. This work presents two solutions to extend ITS to deformable surfaces, relying on force-deformation characteristics of the surface under exploration: (i) a closed-form approach that calculates the contact centroid using standard ITS, but on a shrunk geometry approximating the deformed surface; (ii) an iterative procedure that takes into account soft surface deformation, and force/torque equilibrium to minimize a cost function. We have tested both using ellipsoid silicone specimens, with different softness levels and indented along different directions. Both linear and quadratic fitting for the force-indentation behavior were employed. The two methods have distinct advantages and limitations. However, a combination of two methods, using one to produce the initial guess for the other, turns out to be very effective. Indeed, in our validation this solution showed convergence under 1ms, attaining errors lower than 1 mm. The proposed approaches were implemented in a ROS-based toolbox, integrating both solutions.
Embedding Big Data in Graph Convolutional Networks

Deep learning architectures and Convolutional Neural Networks (CNNs) have made a significant impact in learning embeddings of high-dimensional datasets. In some cases, and especially in the case of high-dimensional graph data, the interlinkage of data points may be hard to model.Previous approaches in applying the convolution function on graphs, namely the Graph Convolutional Networks (GCNs), presented neural networks architectures that encode information of individual nodes along with their connectivity. Nonetheless, these methods face the same issues as in traditional graph-based machine learning techniques i.e. the requirement of full matrix computations. This requirement bounds the applicability of the GCNs on the available computational resources. In this paper, the following assumption is evaluated: the training of a GCN with multiple subsets of the full data matrix is possible and converges to the full data matrix training scores, thus lifting the aforementioned limitation.Following this outcome, different subset selection methodologies are also examined to evaluate the impact of the learning curriculum in the performance of the trained model in small as well as very large scale graph datasets.
Crack Detection Method on Surface of Tunnel Lining

Crack detection on surface of tunnel lining is one of the most important tasks in concrete structure inspection field. Naked eye inspection method is widely used in general but it needs huge resources. To solve the issue, many methods have been proposed based on convolutional neural network but they show disconnected crack results with thin or blurred crack image. To overcome this problem, we propose a multiscale feature fusion method for crack detection. Experientially, results show that performance of our method was improved over the previous methods.