Miracle-dz/UniFAR-FTUs huggingface dataset:
An Experimental Optical Three-axis Tactile Sensor Featured with Hemispherical Surface

We are developing an optical three-axis tactile sensor capable of acquiring normal and shearing force to mount on a robotic finger. The tactile sensor is based on the principle of an optical waveguide-type tactile sensor, which is composed of an acrylic hemispherical dome, a light source, an array of rubber sensing elements, and a CCD camera. The sensing element of the silicone rubber comprises one columnar feeler and eight conical feelers. The contact areas of the conical feelers, which maintain contact with the acrylic dome, detect the three-axis force applied to the tip of the sensing element. Normal and shearing forces are then calculated from integration and centroid displacement of the grayscale value derived from the conical feeler's contacts. To evaluate the present tactile sensor, we conducted a series of experiments using an x-z stage, a rotational stage, and a force gauge. Although we discovered that the relationship between the integrated grayscale value and normal force depends on the sensor's latitude on the hemispherical surface, it is easy to modify the sensitivity based on the latitude to make the centroid displacement of the grayscale value proportional to the shearing force. When we examined the repeatability of the present tactile sensor with 1,000 load/unload cycles, the error was 2%.
Vessel Classification Using A Regression Neural Network Approach*

Marine vessels are subject to high wear and tear due to the conditions they operate in. To reduce risk of failure during operation, vessels are inspected periodically every five years. These inspections are prone to high subjectiveness that makes them hard to reproduce for the shipping owners. The purpose of this paper is to present a regressor to a Faster R-CNN network that can help alleviate some of the subjective assessment currently performed by human surveyors by estimating the severity of a corroded area, autonomously using drones. A feature pyramid backbone is shared between the Faster R-CNN and the added regression head. The goal of the regressor is to introduce a more objective assessment of the vessel that gives a consistent output for a consistent input. The system is evaluated on a real dataset, acquired in ballast tanks and the experimental results indicate that our deep learning approach can be used to detect and quantify corroded areas during the inspection process of marine vessels.
Pallet detection and docking strategy for autonomous pallet truck AGV operation

Automated guided vehicles operation in human populated factory environments is a challenging task, especially when there is a demand to operate without following fixed paths defined by guide wires, magnetic tape, magnets, or transponders embedded in the floor. The paper at hand introduces a vision-based method enabling safe and autonomous operation of pallet moving vehicles that accommodate pallet detection, pose estimation, docking control and pallet pick up in such industrial environments. A dedicated perception topology relying on monocular vision and laser-based measurements has been applied and installed on-board a novel robotic pallet truck. Pallet detection and pose estimation are performed in two steps. Firstly, a deep neural network is used for the fast isolation of pallets' regions of interest and, secondly, model-based geometrical pattern matching on point cloud data is applied to extract the pallet pose. Robot alignment with candidate pallet is performed with a dedicated visual servoing controller. The developed method has been extensively evaluated both in simulated and real industrial environments with the pallet truck and proved to have real-time performance achieving increased accuracy in navigation, pallet detection and pick-up.
60 GHz Millimeter-Wave Antennas suitable for Optical OFDM Application

This paper presents, two 60 GHz millimeter- wave Yagi antennas and also discuss system architecture of optical OFDM system. When we are using an indoor or outdoor wireless optical channel for the transmission of optical OFDM signals, then an antenna system of 60 GHz freq. range is required. So for these particular applications of optical OFDM system, Yagi antennas are proposed. Its design considerations and the simulated results are given in this paper. In first design of Yagi horizontal feeding with lumped port excitation is used, while in second design vertical feeding using lumped port excitation with CPW feeding network has been used. HFSS is used for design simulation. Performance of the proposed antenna is analyzed in terms of the reflection loss and its radiation pattern. In this paper, comparison between two simulated design also been discussed.
Modeling Streambank Instability by Seepage Undercutting

Predicting bank collapse due to seepage erosion undercutting has not been fully studied or modeled, even though its role in streambank erosion may be important. The limitation originates from the lack of field measurements or laboratory experiments as well as the unavailability of discrete element models that can effectively simulate seepage erosion. The objective of this research was to demonstrate a procedure for incorporating seepage undercutting into two-dimensional variably-saturated flow and bank stability models and to investigate the role of seepage undercutting on bank instability. Integrated flow and bank stability models were used to simulate soil-water pressure variations and bank stability with and without seepage erosion with regard to input parameter uncertainty using Monte Carlo analysis. The percentage decrease in the mean factor of safety, Fs, ranged between 42 and 91% as the depth of undercutting increased, dependent upon the initial stability of the bank. For stable banks, the probability of failure reached 100% when the depth of the undercutting reached 30 to 50 mm. The propensity of streambanks to fail during the recession limb of hydrographs may be the combined result of seepage undercutting and reduced cohesion.
Fencing tracking and visualization system

We developed the "Fencing tracking and visualization system. " It detects the tips of sabers (fencing swords) to visualize the trajectory of the sabers in real time, which doesn't require any markers but works only with the input of the images from cameras. This is the only fencing visualization technology that has been used in actual international fencing matches, such as the H.I. H. Prince Takamado Trophy JAL Presents Fencing World Cup 2019. Fencing sabre, especially its tip, moves quite fast, and its flexibility results in a large distortion in its shape. Additionally the tip is the size of only a few pixels when captured even by a 4K camera so that it is too small to detect with image recognition techniques. We developed a multi-stage deep learning network for general object detection based on YOLO v3 [Redmon and Farhadi 2017, 2018], starting from the hardware selection of a camera for analysis. Since a single camera can only cover about 8 meters, we eventually installed 24 4K cameras on the both sides of the piste to cover the entire match area and improved the robustness of the sabre tip detection. We also developed a system to estimate the 3D position of the tips from the detection results of multiple cameras. (Rhizomatiks) Planning, Creative Direction : Daito Manabe (Rhizomatiks) Planning, Technical Direction, Hardware Engineering : Motoi Ishibashi (Rhizomatiks) Software Engineering : Kyle Mc-Donald (IYOIYO), anno lab (Kisaku Tanaka, Sadam Fujioka, Nariaki Iwatani, Fumiya Funatsu), Kye Shimizu Dataset System Engineering: Tatsuya Ishii (Rhizomatiks), ZIKU Technologies, Inc. (Yoshihisa Hashimoto, Hideyuki Kasuga, Seiji Nanase, Daisetsu Ido) Dataset System Engineering : Ignis Imageworks Corp. (Tetsuya Kobayashi, Katsunori Kiuchi, Kanako Saito, Hayato Abe, Ryosuke Akazawa, Yuya Nagura, Shigeru Ohata, Ayano Takimoto, Kanami Kawamura, Yoko Konno) Visual Programming : Satoshi Horii, Futa Kera (Rhizomatiks) Videographer : Muryo Homma (Rhizomatiks) Hardware Engineering & Videographer Support : Toshitaka Mochizuki (Rhizomatiks) Hardware Engineering : Yuta Asai, Kyohei Mouri, Saki Ishikawa (Rhizomatiks) Technical Support : Shintaro Kamijyo (Rhizomatiks) Project Management : Kahori Takemura (Rhizomatiks) Project Management, Produce : Takao Inoue (Rhizomatiks) This work was conducted with assistance from Dentsu Lab Tokyo.
Clustering of Physiological Signals by Emotional State, Race, and Sex

In this work, we explore the emotional responses to ten stimuli reflecting real-world experiences captured via physiological signals from 140 individuals. We employ the DBSCAN clustering algorithm to these data, and show that blood pressure and electrodermal activity may be indicative of race, and blood pressure of sex and emotional state. These findings could lead to important innovations, particularly those valuable for certain demographic groups, including, for example, culturally relevant robotics and cultural awareness in education by improving real-time measurements of stress and cognitive load.
The Point of Action where Cognitive Behavioral Therapy is Effective

Cognitive behavioral therapy (CBT) is a treatment method that eases feelings and reduces stress by working on the way of thinking and receiving (cognition) of things, and also on some behavior [1]. This is one of the effective psychotherapy performed for depression and anxiety disorders in the actual clinical situation of psychiatry. CBT includes cognitive reconstruction, behavioral activation, and problem-solving techniques. What is the point of action of CBT as a treatment? Creatures gather various information from their surroundings and recognize them, that is, process them and connect them to appropriate actions. In the process of the evolution, in order to obtain rapid action, we acquired feelings like an instant “signal system” that indicate the result of cognition, and put the process up to cognition below consciousness. In other words, the “signal system” i.e. feelings lights up instantly, and the actions that accompany it can be swiftly taken. However, if the information gathering that leads to cognition is not successful, it is assumed that cognition would be false and that it would lead to false “signals,” that is, false feelings. It is assumed that information is narrowed in depression and cause a depressed mood. By optimizing this information gathering process, CBT guides correct cognition and optimizes feelings. This is the point of action of CBT. Our research team is working on the development of automated CBT using AI based on the point of action of this CBT [2, 3].
Accord: application-driven networking in the datacenter

Resource optimization algorithms in the cloud are ever more data-driven and decision-making has become reliant on more and more data flowing from different cloud components. Applications and the network control layer on the other hand mainly operate in isolation without direct communication. Recently, increased integration between the network and application has been advocated to benefit both the application and the network but the information exchange has mostly been limited to flow level information. We argue that in the realm of datacenter networks, sharing additional information such as the function processing times and deployment data for planning jobs and tasks can result in major optimization benefits for the network. In this study we present Accord as a Network Application Integration solution to achieve a holistic network-application management solution. We propose a protocol as an API between the network and application then we build a system that uses the processing and networking data from the application to perform network scheduling and routing optimizations. We demonstrate that for a sample distributed learning application, an Accord enhanced solution that uses the application processing information can yield up to 27.8% reduction in Job Completion Time (JCT). In addition, we show how Accord can yield better results for routing decisions through a reinforcement learning algorithm that outperforms first shortest path first by %13.
Self-critical Learning of Influencing Factors for Trajectory Prediction using Gated Graph Convolutional Network

Forecasting future trajectories of multiple pedestrians in a crowded environment is a challenging problem due to the complex interactions among the pedestrians. The interactions can be asymmetric and their influences may vary over time. Moreover, each pedestrian can exhibit different behavior at any given time and context and thus they may have multiple future possible trajectories. In this work, we present a Gated Graph Convolutional Network (GatedGCN) based trajectory prediction model that explicitly deal with the asymmetric influences among the adjacent pedestrians through edge-wise gating mechanism. Through GatedGCN only, an overall average improvement of 16% and 18% was achieved on the two performance metrics over the state-of-the-art trajectory forecasting methods. Next, we tackle the problem of learning multi-modal distributions of each pedestrian trajectory using variational auto-encoders (VAEs). However, trajectories sampled from the learned distribution usually ignore the factors affecting the pedestrian motion such as collision avoidance and the target destination. While many of the existing approaches focus on learning such factors during the trajectory encoding process, we proposed a novel self-critical learning approach based on Actor-Critic framework to learn such factors during the trajectory generation process. We empirically show that our method creates fewer number of collisions than the existing methods on popular trajectory forecasting benchmarks.
A Robust Data-Driven Approach for Dynamics Model Identification in Trajectory Planning

In this paper, we propose a data-driven modelling framework using a sparse regression technique to find the governing equations of dynamics systems. With this approach, the prior knowledge of features from simple structures can be used to deduce which on complex structures. The prior knowledge of single-pendulums, double-pendulums, and spherical pendulum enlightens the guess of the feature library for a 3-DOF manipulator. The feature library is sparsifled with a fully autonomous machine learning algorithm composited of the L1-regularization and proportional filter. The training dataset with non-zero-mean Gaussian noise simulates real-world conditions and proves the system's robustness to the noise. Compared with the neural-network-based system identification method, this paper's technique can be promptly applied in dynamic trajectory planning. A simulation of the optimal trajectory planning for the obstacle-avoidance on the Lynxmotion robot is accomplished by optimizing the objective function constructed with energy and penalty function. Results of the simulation support that the estimated model works correctly. Comparison between the data-driven and the closed-form model evidences the reliability and robustness of this identification technique.
A Soft Assistive Device for Elbow Effort-Compensation

The use of assistive technologies in industrial environments to improve human ergonomics and comfort in repetitive and high effort tasks have increased considerably in the last decade. Predominantly, the goal is to provide additional physical support through lightweight and wearable devices, without posing major constraints to the human body movements. Towards achieving this objective, in this work we present a novel actuation mechanism for a soft assistive device, by taking into account the human elbow torque-angle profile. The proposed design integrates a single motor coupled with an elastic bungee and a cam-spool mechanism to enable energy exchange during the elbow flexion movement, while allowing for free-motions during the extension of the joint. A cable-driven transmission with passive elastic attachments is employed to implement compliant couplings with the wearer and to achieve easy donning/doffing. Experiments are conducted on two 3D printed functional prototypes. Results suggest that the assistive elbow torque is effectively transmitted with an average 90% success for balancing a 5N payload, and the free-motion range of 108° is measured for both flexion and extension.
Coordinated Control of Doubly Fed Variable Speed Pumped Storage and Flywheel Energy Storage Participating in Frequency Regulation

The combination of doubly fed variable speed pumped storage (DFVSPS) and flywheel energy storage (FES) can make full use of different technical advantages of different types of energy storage, and participate in frequency regulation in the whole stage of grid frequency fluctuation. Based on the frequency fluctuation characteristic of power grid, a coordinated control method of DFVSPS and FES participating in frequency regulation is proposed in this paper. With the FES adopting virtual inertia control and droop control, and the DFVSPS adopting droop control, the frequency fluctuation is suppressed during the inertial response period. Moreover, by reducing the power of the FES, and increasing the power of the DFVSPS to compensate the reduced output of the FES, the energy storage system can provide persistent energy to support the power grid during the frequency recovery period and improve the ability of the FES continuously participating in frequency regulation. Simulations are performed to verify the effectiveness of the proposed method.
Optimization of Resource and Energy for D2D enable Communication System using Constraint's- based Function

The process of resource optimization enhances the capacity of device-to-device communication. The utility and diversity of device-to-device communication influence next-generation wireless communication. The sharing of resources in wireless combination treats as assets of communication. The limitation of assets is spectrum allocation and energy efficiency. The allocation process faces a problem of interference in different variants such as cochannel interference, intercell interference and some others. This paper proposed the constraints-based function for the optimization of energy and resource sharing. The proposed methods focus on the total users of a single-cell network and utilize all resources in the best selection mode. The pairing factors of D2D users and CU users satisfy the defined constraints and got optimal resources of energy and spectrum. The proposed algorithm simulated in MATLAB environments and set different parameters for the validation. The proposed optimization algorithm compares with two algorithms, particle swarm optimization and ant colony optimization.
Microspine-rubber composite for high friction on smooth, rough, and wet surfaces

As robotic technologies advance and robots move out of factories and labs into the real world, grip on a variety of surfaces (e.g. smooth or rough) in a variety of conditions (e.g. dry or wet) becomes increasingly important. Bioinspired "microspines" have been previously explored, but primarily for vertical climbing applications or for small-scale robots applying low forces (less than 1 N). Further, these works primarily focused on rough surfaces. To advance this area of research, we present a composite material comprising high- friction rubber and compliant nitinol microspines which can passively retract below the surface of the rubber. We show that the composite can support large loads (greater than 75 N) with a high coefficient of friction on both smooth and rough surfaces (p > 1.1), outperforming microspines alone on smooth surfaces and rubber alone on rough surfaces, especially when wet and oily. Further, due to the retraction of the microspines, the composite does not damage relatively soft, smooth surfaces, like wood flooring. We also test durability, and show that it is improved by microspine compliance, and test the effects of varying microspine diameter, angle, and tip shape. Finally, we demonstrate that a small RC car can climb steeper slopes and stop more quickly in wet conditions with microspines.
Acoustic Particle Manipulation Along Three Orthogonal Directions in Laser Engraved Microfluidic Channels

Acoustofluidic particle manipulation in three orthogonal directions was performed in simple and low-budget microfluidic chips that were laser-engraved and laser-cut out of polymethylmethacrylate sheets. Besides the focusing and separation of particles orthogonal to the channel axis, which can be used in so-called flow through separators, we were also able to accumulate the particles at acoustic vibration nodes along the channel axis and to lift them up in vertical direction in the same experimental configuration. An application of the latter effect is demonstrated, by considering particles that are slowly dragged along the bottom of a microfluidic channel by the streaming fluid and which move significantly faster as they are lifted upwards.
SnakeRaven: Teleoperation of a 3D Printed Snake-like Manipulator Integrated to the RAVEN II Surgical Robot

Telerobotic systems combined with miniaturised snake-like or elephant-trunk robotic arms can improve the ergonomics and accessibility in minimally invasive surgical tasks such as knee arthroscopy. Such systems, however, are usually designed in a specific and integral approach, making it expensive to adapt to various procedures or patient anatomies. 3D printed instruments with a detachable design can bring the benefits of patient-specific customisation, affordability, and adaptability to new clinical scenarios. However, the integration of such snake-like instruments to standard telerobotic systems can be challenging in terms of design and control. In this study, a teleoperation system is developed to control and steer the pose of SnakeRaven: a 3D printed, customisable snake-like end-effector attached to the RAVEN II platform for the application of fibre-optic knee arthroscopy. Algorithms for the parametric inverse kinematics and mapping between the RAVEN II joint space to the coupled tendon-driven rolling joints are developed. The controller is tested and validated on the physical prototype interfacing with the RAVEN II platform in a teleoperation experiment. A video demonstrating the main results of this paper can be found via https://youtu.be/ApJjR853kIQ
Robust Initialization of Multi-camera SLAM with Limited View Overlaps and Inaccurate Extrinsic Calibration

This paper proposes a robust initialization method for a multi-camera visual SLAM system where cameras have only a limited common field of views and inaccurate extrinsic calibration. The limited common field of views leads to only a few common features that can be matched between cameras. Inaccurate extrinsic poses, caused by vibrations or misplacement of cameras after offline calibration, make it even harder for triangulating the seed 3D points to initialize the SLAM system successfully. Instead of taking the extrinsic parameters as constants for feature matching and 3D point triangulation as most multi-camera systems did, we propose to take the inaccurate extrinsic poses as soft constraints to accommodate the calibration errors. Our initialization method consists of two stages by matching across different cameras and between two key frames. Both stages involve optimizing the cost functions that contain the extrinsic pose priors from inaccurate calibration parameters. By incorporating those soft pose constraints, we may avoid false feature matching and triangulation caused by inaccurate extrinsic parameters, while keeping solution space limited when only a few feature correspondences exist. The results in real-world tests show that such a simple solution can improve the success rate of SLAM initialization notably, even when the pose priors from the offline calibration differ significantly from the real ones.