Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards an Open-Source Hardware Agnostic Framework for Robotic End-Effectors Control

Nowadays a wide range of industrial grippers are available on the market and usually their integration to robotics automation systems relies on dedicated software modules and interfaces specific for each gripper. During the past two decades, more sophisticated end-effector modules that target to provide additional functionality including dexterous manipulation skills as well as sensing capabilities have been developed. The integration of these new devices is usually not trivial, requiring the development of brand new, tailor-made software modules and interfaces, which is a time consuming and certainly not efficient activity. To address the above issue and facilitate the quick integration and validation of the new end-effectors, we developed the ROS End-Effector open-source framework, which provides a software infrastructure capable to accommodate a range of robotic end-effectors of different hardware characteristics (number of fingers, actuators, sensing modules and communication protocols) and capabilities (with different manipulation skills, such as grasping, pinching, or independent finger dexterity) effectively facilitating their integration through the development of hardware agnostic software modules, simulation tools and application programming interfaces (APIs). A key feature of the ROS End-Effector framework is that rather than controlling each end-effector in a different and customized way, following specific protocols and instructions data fields, it masks the physical hardware differences and limitations (e.g., kinematics and dynamic model, actuator, sensor, update frequency, etc.) and permits to command the end-effector using a set of high level grasping primitives. The framework capabilities and flexibility in supporting different robotics end-effectors are demonstrated both in a kinematic/dynamic simulation and in real hardware experiments.
Digital Transformation in Oil and Gas Industry: Developing an OSDU Third-Party Application

Data will become more diverse in terms of its type and the amount of data will be greater. Failure to manage existing and new data will result to failure to benefit the value of those data. Subsurface data in the oil and gas domain has been always managed in silos and hence result to bad data management. Major global oil and gas organisations through the establishment of a consortium called Open Subsurface Data Universe (OSDU) has developed the OSDU Data Platform to address this issue. DG, an existing data extraction third-party software is aimed to incorporate OSDU requirements into the software in order to leverage the OSDU Data Platform through APIs as well as to be an OSDU-compatible application. There are nine minimum requirements that has been identified from the OSDU Technical Standard Version 1.0. These requirements have been incorporated into the software through a software requirement specification and software architectural design processes to build a GUI prototype of the new version of the software; DG2.0. This project would definitely be a vital reference for other companies or organizations to develop a software product in leverage with OSDU Data Platform in the future.
Assessing the feasibility of monocular visual simultaneous localization and mapping for live sewer pipes: a field robotics study

Sewer pipes are important to inspect for damage and blockages. Mobile robots with cameras are a natural choice for inspecting sewers, and indeed CCTV inspection using tethered mobile platforms is a well-established commercial approach. It therefore makes sense to also explore the use of camera data for localising defects for targeting subsequent repair. Visual odometry (VO) methods have been researched for robot localisation in pipes but the full visual simultaneous localisation and mapping (vSLAM) problem has received little attention. Whilst VO focuses on estimating the current pose of the robot, vSLAM focuses on building a map, as well as pose estimation, which should increase accuracy and robustness - both important for the future use of autonomous robots in sewer inspection. In particular, it is not known if one crucial element of vSLAM - loop closing using appearance-recognition methods - works effectively in sewer pipes due to problems of perceptual aliasing - where the high degree of visual similarity in image frames can lead to incorrect loop closures causing the vSLAM system to fail. The aim of this paper is to assess the feasibility of vSLAM for sewer pipes using real world data. The results demonstrate that whilst perceptual aliasing is a problem, appearance-recognition using bag-of-words methods can be used effectively. Demonstrating for the first time that vSLAM systems are potentially useful for sewer pipe environments.
MM-Loc: Cross-sensor Indoor Smartphone Location Tracking using Multimodal Deep Neural Networks

Indoor positioning systems have been explored for decades to facilitate universal location-based services. However, complex environment conditions and sensing imperfections continue to be limiting factors to their large scale adoption. Rather than customising more ingenious solutions to handle corner cases in complex environments, we believe that a more efficient solution is to learn entirely from data with minimal engineering effort. We develop neural network based solutions for two positioning approaches, modelling Dead Reckoning with recurrent neural networks and WiFi Fingerprinting with deep neural networks. We propose a multimodal deep neural network architecture (MM-Loc) that bridges the features extracted by the modality-specific complements (sensor based and WiFi based) to join the two perspectives. We observe that this multimodal approach is better than single-modality models, and elegantly trains directly from raw data with minimal intervention.
Hierarchical Graph Classification Method Based on Graph Pool Topology Learning

Graph neural network extends deep neural network to graph structured data, and realizes many tasks related to graphs. However, most of the existing models focus on design graph convolution operations, and the important role of graph pools in hierarchical learning representation is often ignored. In addition, the existing Top-K graph pooling method will cause the feature information of unselected nodes lost, which inevitably leads to the loss of a large amount of graph topology feature information. In order to solve the above problems, improved “intra-layer” & “inter-layer” joint feature extraction structure is proposed, which introduces a structural feature learning mechanism, combines the graph adaptive pooling operator with the existing GNN architecture. To mitigate the problem of insufficient feature extraction by LSTM, BiLSTM is used to extract the inter-layer feature. Experiment results show that our method can achieves better performance on benchmark data sets by using a reasonable number of parameters.
DDPG based self-learning active and model-constrained semi-active suspension control

In classical suspension control, parameter calibration is one of the most critical and time-consuming progress in algorithm development. One set of parameters is hardly possible to adapt to different working conditions. The ideal algorithm should be adaptive, cope with any road conditions and learn to apply the most appropriate control. Reinforcement learning is well suited to this scenario, giving adapted output in interaction with the environment. The DDPG network is adopted to implement active and semi-active suspension control. In active suspension control, the output forces of the network are used to exert control directly, while in semi-active suspension control, experimentally obtained damper constraints are introduced. The algorithm can reduce the RMS of acceleration by 66% in active suspension control and 15% in constrained semi-active control, adapted to various road conditions, vehicle velocities and system parameters.
Simulation, Design and Control of a Soft Robotic Arm with Integrated Bending Sensing

The utilization of soft robotic arms/fingers, which are inherently safe and passively compliant, has been an active research area in robotics. This paper studies a rigid robotic arm and compliant soft gripper with integrated sensing and control for use in object manipulation tasks. A brief outline of the method used to implement the approximate soft mechanics of a compliant gripper within the rigid-body simulation of Gazebo [1] is described, followed by the implementation of the embedded strain sensors required for the sensing of the soft gripper within ROS [2]. A further method of soft gripper pose estimation, making use of the embedded sensor readings and a model-based approach, is then described, and implemented, within ROS and evaluated to gain an approximate understanding of how the method performs within a simulation environment and possibly in real use cases. Many avenues for further research may stem from this project, including further evaluation across wider use cases, additional systems built upon the pose estimations acquired and whether the method is feasible in real use cases when used in conjunction with a physical gripper.
Improvement of Biophysical Micromixer Design

In a passive micro-scale mixer, the complete mixing of two or more fluids within a reasonable time period plays an important role. Microfluidic transportation and effective mixing are of importance and require examination. The biophysical micromixer addressed by authors would be utilized because of its excellent ability to enlarge the interface and reduce the diffusion length during the mixing process. Here, the mixing efficiency, pressure drop and Aspect ratio (AR) at an optimal inlet Reynolds ratio will be studied. Some useful results will be addressed: First, the dimensions of A(subscript X), G(subscript X), G(subscript Y), F(subscript X), H(subscript X), H(subscript Y) and J would be effective factors when mixing the biophysical micromixer. Second, the recirculation region would play an important role in the mixing with an increment of 33.03% with respect to the original prototype because it would escalate the two-flow interaction. Lastly, the side wall effect will influence both the mixing performance and the pressure drop. An aspect ratio of AR=10 will be suggested because it exhibits the highest mixing coefficient of 0.907 and the lowest pressure drop than previous studies cases. These findings will show the greater feasibility of the biophysical micromixer being mixed in a limited space. Additionally, these results would be useful for the mixing improvement of passive micromixer.
Robust Channel Estimation for 5G mm Wave Massive MIMO Relay Communication Systems

Channel estimation is one of the major challenges for 5th Generation (5G) millimeter-wave (mmWave) massive multiple input multiple output (MIMO) relay communication systems. In this paper, we propose a robust channel estimation scheme for 5G mmWave massive MIMO relay communication systems. The proposed scheme takes advantage of the sparsity of the channel ofmmWave systems to further improve the accuracy of channel estimation. With the employment of a gradient descent based iterative algorithm, the channel parameters containing angle of arrival (AoA), angle of departure (AoD) and the fading coefficient can be estimated accurately. The proposed scheme can effectively estimate the channel parameters with a small number of training. In addition, it has better and more stable channel estimation performance compared with the conventional algorithms. Simulations verify the effectiveness of the proposed channel estimation scheme.
RGB-D Sensors Extrinsic Calibration In Controlled Environment

RGB-D sensors provide rich information about the scene including color and 3D data. This information is valuable for the forthcoming analysis on the object of interest or the scene as a whole. Depending on the task, multiple sensors might be needed in order to provide details about process under observation. In this case, calibration between the sensors is necessary in order to provide consistent information related to the 3D world. A lot of work has been done related to RGB-D sensor calibration, since the introduction of Kinect in 2010, however it is still an active area of research. In this paper we propose an algorithm for RGB-D sensors extrinsic calibration, in a scenario where each sensor is located in closed and known environment. This knowledge of the environment helps to exploit its structure in the most optimal way for the calibration process. It includes planes locations and planes relative position. We propose two stage procedure for extrinsic calibration, in pairwise and global manner. The experimental results validate our approach for real applications.
Human Localization in Warehouse Environments based on a Wearable Camera Sensor Suite and Dynamic Ultra-Wide Band Nodes

Modern warehouses, equipped with an autonomous robots fleet, suffer from an efficiency deficit during human interventions in the shop floor area, since, during such interventions, all the robots must stop for safety reasons. This particularly affects large warehouses which could extremely benefit from a solution that would allow human-robot collaboration during such interventions. This solution should only stop the robots that are in the vicinity of the human and reroute the others, and it requires the following prerequisites: (i) reliable relative ranging of robots with respect to human workers that would ensure safety, and (ii) reliable localization of human workers in the warehouse. In this paper we propose a system for reliable human localization that is based on fusing cues from measured relative ranges with respect to moving robots and a camera system that is worn by the human worker. Since global locations of the robots in the warehouse are known, we use the relative ranges to robots in an indoor GPS-like fashion to provide global human location updates, while the wearable cameras provide visual odometry, i.e., the relative motion of the human, and another global cue by detecting ground fiducial markers used by robots. The fusion is performed within a graph-based optimization framework that also includes a visual odometry error model. We carried out an experimental analysis on three different datasets to validate the performance of the proposed approach and compared it to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results demonstrate that our approach yields reliable human location within warehouse like environments.
Applying Machine Learning in Designing Distributed Auction for Multi-agent Task Allocation with Budget Constraints

The multi-agent task allocation can be solved in a distributed manner using Consensus-Based Bundle Algorithm (CBBA). Under this distributed auction process, each agent greedily maximizes the global score, which is the difference of the reward and the cost, through an iterative bundle construction and conflict resolution procedure. The distributed algorithm has provable convergence and guarantees 50% optimality if the score function satisfies the condition of diminishing marginal gain (DMG). While the previous work focuses on unconstrained optimization of rewards, this paper aims at applying CBBA to task allocation with budget constraints. Several heuristics were proposed to build the bundle and calculate the bidding scores as improvements to the original CBBA algorithm. We then prove that some of the new score functions are DMG, and therefore guarantees the convergence of the distributed process. We also show that these heuristic extended CBBAs are Pareto efficient; using different heuristic extensions under different scenarios is more efficient than consistently using the same one. To decide which heuristic extension should be used for a given task allocation problem, a graph convolutional neural network (GCN) model is trained to extract and analyze the features of the constrained optimization problem as a graph, and predict the potential performance (i.e., global reward) of different heuristic extensions. Based on the prediction, the best heuristic extension will be selected. Experimental results show that the predicted reward has more than 0.98 correlation with the actual reward and for 70% of time the prediction guided selection picks the best heuristic extension for the budget constrained task allocation problem.
SmogNet: A Point Cloud Smog Segmentation Network for Unmanned Vehicles

LiDAR is a key sensor commonly used in unmanned vehicles. Smog is a trouble for vehicle-mounted LiDAR when unmanned vehicles operates in actual road environments. It leads to a significant reduction in the ability of LiDAR-based scene understanding for them. Thus, it is essential to recognize the smog existing in the road scene quickly and accurately. This paper proposes a fine-grained point cloud smog segmentation network (SmogNet) for unmanned vehicles. We adopt an effective graph convolution kernel based on attention to extract features layer by layer. The key of SmogNet is two manual features we design specially to characterize the geometric features of smog in point cloud. We evaluate SmogNet in challenging real road scenes with simulated smog. It performs better than competitive methods and it can be effectively generalized. We use the Focal Loss during the training of the SmogNet and improve the problems caused by the imbalance of sample categories effectively.
Periodic Extended Kalman Filter to Estimate Rowing Motion Indoors Using a Wearable Ultra-Wideband Ranging Positioning System

Rowing depends on weather conditions and athletes rely heavily on dryland training, specifically, on rowing machines. Past research implemented methods using inertial measuring systems to study the rower’s biomechanics and oar dynamics, mostly for outdoor rowing. Tracking the oar dynamics is of great significance to coaches and athletes because it is correlated to the rower’s technical skills and boat speed (on-water). Ultra- wideband (UWB) ranging as an indoor localization method is one of the most accurate and precise among available radio frequency technologies. UWB has been used in game and individual sports to track athletes and their performance during training and competition. This paper proposes an UWB indoor positioning system to track the periodic motion of a rowing machine’s handle at varying frequencies. Furthermore, it introduces a periodic extended Kalman filter to estimate the motion as a non-sinusoidal wave. The proposed UWB positioning system is based on the double sided two-way ranging time of arrival between two anchors and a single tag. The ranging measurements are processed using three mathematical models, trilateration, extended Kalman filter with constant velocity, and a periodic extended Kalman filter. The results are compared to a reference trajectory obtained from a Vicon 8 camera motion capture system. The periodic Kalman filter outperforms the other two models, however, the accuracy and precision of the predicted position of the handle depends on the accuracy of the ranging measurements. The proposed indoor rowing positioning system is able to effectively track rowing motion using UWB ranging and a periodic extended Kalman filter.
Floor Number Detection for Smartphone-based Pedestrian Dead Reckoning Applications

We present a new floor number detection algorithm for use in smartphone-based indoor localisation systems. It is designed to complement any pedestrian dead reckoning (PDR) algorithm able to detect steps and estimate a 2D trajectory from data of the smartphone’s inertial measurement unit.Our proposed method is based on the Viterbi algorithm, fusing data from an off-the-shelf smartphone’s accelerometer, barometer and wifi received signal strength (RSS) measurements. The accelerometer is used to detect accelerating elevators, while the barometer is used to detect stair climbing. This is combined with model-based wifi RSS fingerprinting, enabling accurate floor number detection. Our system is tested in an office environment with 7 41 m x 27 m floors, each of which has 2 pre-existing wifi access points. The algorithm is evaluated with a total of 116 minutes of recorded data, in which the floor number changed 76 times and a distance of 4.8 km was travelled. Since the Viterbi algorithm allows to easily correct past states (i.e. floor numbers) based on new information, it is evaluated in real-time and batch mode. Our proposed algorithm achieves a floor number detection accuracy of 99.1% (real-time) and 99.7% (batch), while using only RSS measurements resulted in 91% accuracy.
Fan-Out Embedded Bridge Solution in HPC Application

In recent year, the requirement of HPC and data center market growth with the features, highest processing rates, highest communication rates, lowest latencies, and highest capacities with extreme requirements. In parallel, Moore's law has been clearly ended in Si scaling, and the term chiplets is used to refer to either a part or the whole of a functional chip, which requires to be integrated in one package. So, the concept of more than Moore’s law is growing influence for packaging which address the high integration of die to die interconnection in homogeneous and heterogeneous package technology. It is well-known 2.5D with the whole piece of interposer provided the matured platform to achieve the multiple dies integration. Alternatively, the opportunity of fan-out technology can provide the flexible design in RDL routing and cost effectiveness in the comparison of 2.5D. In this paper, it is demonstrated that fan-out technology with bridge die is to provide the localized high density wiring through the use of embedded silicon bridge, called FO-EB. The partial bridge interposer is applied in the specific area of the high I/O density interconnection for HBM dies integration in comparison to 2. 5D with the whole piece of Si interposer. SPIL’s FO-EB development integrates chips by 1 ASIC die and 4 High Bandwidth Memory dies (HBM) with 4 embedded bridge dies on mold-based interposer, 1 layers of RDL are presented. The embedded bridge dies are fabricated using 65nm node with 0.56 $\mu$m Cu line space. In the comparison of the warpage of a 2. 5D package with similar packages size, FO-EB chip module has similar warpage shape to substrate, and final package assembly with substrate, the warpage value is identical to 2. 5D package. Thus, it has much lower stress residues as compare to 2. 5D. Owing to this reason, the FO-EB package has been validated with 2x longer temperature cycle without a single failure as compared to 2. 5D. This package allows us to scale the package to much higher I/O density, finer L/S, higher RDL layers, and number of dies. In order to well fit to Moore’s Law trends for packaging, one of next generation of FO-EB platform development implemented silicon bridge die with TSV, namely as FO-EB-T. Not only the existed advantages of FOEB are preserved, but FO-EB-T provides the better electrical performance with direct signal and power transmission through TSV in silicon bridge die. The simulation data indicated the power consumption can be reduced 55% against FO-EB without TSV. As such, an overview of Fan-out integration solution associated with silicon bridge die embedding will be presented in this paper.