Miracle-dz/UniFAR-FTUs huggingface dataset:
3D LiDAR Map Compression Using Deep Neural Network

3D LiDAR based maps provide basic foundation for localization and navigation of mobile robots. However, in outdoor scenarios, too many map points bring a huge computational burden to the processor, resulting in both slow calculation speed and poor real-time performance. In this paper, we propose a deep learning method to reduce the point cloud maps by applying and modifying the structure of PointNet. The recent PointNet architecture proposes a new approach to process 3D point clouds in map, which has made great performance in many tasks. In addition, we conduct experiments on self-collected dataset and evaluate the localization performance of compressing maps. The final results show that our model can implement map compression while keeping the localization error within an acceptable range.
A Multi-task Learning Convolutional Neural Network for Object Pose Estimation⋆

Estimating 6D poses of objects from RGB images is very crucial for robots to interact with the surrounding environment and to cooperate with humans. It is a challenging problem due to the various shapes of objects, the occlusions among objects, as well as the complexity of the scene. In this study, we present a new multi-task convolutional neural network for 6D object pose estimation, which also learns the object region in the image to further improve the estimation result. A weighted loss computed from the training samples is adopted to guarantee that the components of the estimated pose are equally accurate. Additionally, we sythesize new virtual data based on the existing training data so as to overcome the possible adverse situation caused by insufficiency of training data. Experiments on the YCB-video dataset are undertaken to validate the effectiveness of the proposed method.
A Cooperative Aerial Inspection System with Continuable Charging Strategy*

Compared to a single flying robot, the swarm can significantly enhance robustness and capability in real-world missions. However, the overall flight duration of such system is still heavily constrained by the energy system, typically composed of lithium batteries. To cope with this issue, this paper develops an autonomous mobile charging station for the aerial swarm system to improve the endurance so that the system satisfies the need for the long-term mission such as surveillance and monitoring. The mobile charging station has the ability of environment perception and obstacle avoidance. On this basis, a charging module with multiple contact points is developed, and a motion tracking algorithm is proposed accordingly to support precise landing on the charging module. To further improve the flexibility in on-going real-world missions, a charging schedule and an area coverage policy are also proposed. A prototype system is built to verify the effectiveness of this system and experiments demonstrate the system could cover an area of interest completely and efficiently.
SuperDock: A Deep Learning-Based Automated Floating Trash Monitoring System

Floating trash in the river presents a major environmental problem that endangers the lives of river inhabitants. However, conventional trash monitoring relies on labor-intensive manual inspection by dispatching inspectors to the filed, which is highly cost-ineffective. Despite that Unmanned Aerial Vehicles (UAVs) have been widely proposed for many real-time monitoring applications, limited power supply remains the most challenging bottleneck for such UAV-assisted monitoring applications. In this work, we propose an automated river trash monitoring system called SuperDock. SuperDock consists of a remote processing unit, a docking station and a UAV. SuperDock lands the UAV precisely onto the docking station followed by performing automated battery replacement. As a result, the UAV can continue the monitoring task after the battery replacement. Furthermore, SuperDock includes a deep learning-assisted river trash detection module based on YOLOv3 that runs much faster than the conventional Convolution Neural Networks (CNN). In addition, a data set has been generated for training and testing the deep learning network, specifically for the floating trash detection application. Finally, SuperDock enables the UAV to communicate wirelessly with a remote computer in a real-time manner. Experimental and simulation results show that SuperDock is highly effective in monitoring the floating trash in the river.
Magnetic Hysteresis Under Compressive Stress: A Multiscale-Jiles–Atherton Approach

Based on multiscale modeling of the anhysteretic magnetization considering mechanical stress and crystallographic texture effects, an extension of the Jiles–Atherton (J-A) hysteresis model is proposed. The magnetization and the volume fractions given by the multiscale approach are advantageously used in the J-A model to modify the anhysteretic magnetization and the pinning parameter. The parameters of the proposed model are identified in order to fit with the characterization results under compressive stress using two sets of experimental data. Using the same optimized parameters, the model is tested for the representation of loops under other stress conditions to evaluate its prediction capabilities.
Multi-Frequency Millimeter-Wave Large-Scale Path Loss Characterization for Indoor Environment

Millimeter-Wave (mmWave) band provides abundant spectrum to meet the anticipated demands of system capacity in the fifth generation (5G) wireless networks. This paper presents a measurement-based analysis of large-scale propagation characteristics in conference room across four 5G mmWave candidate bands (i.e., 25.5, 28, 37.5, and 39.5 GHz). Using the flexible mmWave channel sounder, the effect of receiver locations (e.g., on the ground and desktop) is investigated with a focus on path loss, shadow fading, and cross-polarization ratio (XPR). Omnidirectional channel sounding data using the same measurement configurations reveals that path loss exponents in general increase with increasing carrier frequency for copolarization channels, while XPRs probably seen to be frequency- independent at 25.5-39.5 GHz band.
An ultra-broadband double-layer AMC surface for RCS reduction

A novel artificial magnetic conductor (AMC) surface is designed for radar cross section (RCS) reduction in ultra-wide band. The proposed AMC structure is composed of two different double-layer AMC units, resulting in destructive reflection phase difference (180°±37°) in the band from 5.8 to 16.35 GHz (95.26%). Each AMC unit cell is combined by a metal patch etched on a substrate and a PEC layer, in between of which is a 2-mm-thick air gap. One of the metal patches is composed of an octagonal ring and a Union Jack cross, and the other is a circle patch. With chessboard arrangement by the two units, the RCS of the proposed surface can achieve an 91.7% (6.2-16.7 GHz) 10 dB RCS reduction bandwidth for x polarization and an 93% (6.1-16.7 GHz) bandwidth for y polarization, which demonstrates that the presented AMC surface may be found potential applications on low-RCS platforms in an ultra-wide band.
A Cognitive Radar Testbed for Outdoor Experiments

We present a cognitive radar testbed with an adaptive X-band radar sensor and a controller with a perception-action cycle to optimize waveform and processing parameter in real-time. The sensor hardware is described and the processing and optimization algorithms are explained. In the second part of this article, we present results from the outdoor experiments in various environments and with several optimization goals, such as tracking accuracy optimization, bandwidth usage minimization, time effort minimization, and jamming mitigation.
Visual Completion Of 3D Object Shapes From A Single View For Robotic Tasks

The goal of this paper is to predict 3D object shape to improve the visual perception of robots in grasping and manipulation tasks. The planning of image-based robotic manipulation tasks depends on the recognition of the object’s shape. Mostly, the manipulator robots usually use a camera with configuration eye-in-hand. This fact limits the calculation of the grip on the visible part of the object. In this paper, we present a 3D Deep Convolutional Neural Network to predict the hidden parts of objects from a single-view and to accomplish recovering the complete shape of them. We have tested our proposal with both previously seen objects and novel objects from a well-known dataset.
A Cascaded Deep Learning Framework for Real-time and Robust Grasp Planning*

Robotic grasping has been regarded as an essential way for robots to interact with the environment, which makes it a research hotspot. To our knowledge, current work on grasp planning could rarely take both robustness and time efficiency into account. To solve this problem, we propose a cascaded deep learning framework, which consists of a regression CNN and a refined CNN. Taking depth image as input, the first network regresses an optimal grasp region. The second network samples and sorts grasp candidates in the region and finally outputs a series of grasp poses with high quality. This architecture ensures real-time performance and acquisition of adequate candidates synchronously, which is especially appropriate for unstructured environment manipulation. We used Gazebo simulation and DART physics engines to build a grasp data generation environment. Comparing with manually annotating or robot collecting, our method makes data generation less expensive, more dynamic, and easier to generalize into the real world. The method proposed is evaluated in both simulation and physical experiments, verifying that our method is able to find multiple robuster grasp poses at a faster rate than the state of the art.
Development of an extensible, wireless framework for personalized muscle rehabilitation

Hemiparesis is progressive muscle weakness in one half of the body, commonly observed in traumatic brain injury or stroke patients. Usually, physiotherapists design in-person assistive exercises to restore muscle strength and range of motion for affected side of the patient. Alternatively, functional electrical stimulation (FES) devices have also been used, though the equipment is expensive, non-portable, and provides limited configurability for personalized rehabilitation. This paper presents a low-cost, open architecture framework that enables: a) Remote programming of muscle stimulation routine by therapist; b) Delivery of FES sequence to patient wirelessly, and c) Provide quantitative feedback (through Electromyogram (EMG)) on dynamics of exercised muscle with progressive rehabilitation of the patient. The extensible framework overcomes the major drawback of expensive, closed commercial systems, thereby allowing integration of multiple sensors to monitor muscle function. Rehabilitation efficacy feedback will allow an opportunity to personalize rehabilitation and increase patient compliance.
Pattern-Reconfigurable Antenna With Seven Switchable Beams in Elevation Plane

In this paper, a pattern-reconfigurable antenna is proposed that provides multiple switchable beams. By reconfiguring parasitic striplines placed around a radiating dipole and reflector under the dipole using p-i-n diodes, the antenna main beam can be switched to seven directions in the elevation plane, which are 0,30,55,120. The proposed antenna operates from 2.4Ghz to 2.5Ghz with dimensions of about 0.69λ×0.69 λ×0.69 λ. The prototype of the antenna was simulated and optimized in HFSS. For all directional beams, the simulated S11 values are below -10dB, and the simulated realized gains range from 7.5 to 8.3dBi.
A Model-Free Method-Based Shape Reconstruction for Cable-Driven Continuum Manipulator Using Artificial Neural Network

The robot-assisted natural orifice transluminal surgery (NOTES) typically involves applying the lengthy and slender continuum instruments/manipulators to get access to the target lesion sites, and then performs complex operations. However, due to the strong-nonlinearity of continuum robotic manipulators and the confined and tortuous anatomical paths, it’s difficult to establish accurate inverse kinematics (IK) model to achieve precise motion control and real-time shape sensing for accurately modeling their shapes. To tackle such difficulties, a model-free method based on neural network have been proposed to solve the IK problem and reconstruct the shape of a continuum manipulator at the same time using the training results from the electromagnetic (EM) tracking approach. For the IK problem, the relationship between the tip position and the corresponding cable lengths can be learned and for the shape estimation problem, the mapping from the cable lengths to the shape of the continuum robot can be established. A dataset of 500 random continuum manipulator postures was used to train the neural network, with recorded EM sensors-tracked positions logged synchronously to the cable lengths. Experiment results show the proposed model-free method could achieve high accuracy and reliable inverse kinematics and shape reconstruction outcomes.
An Ultra-Broad-Band Low-Distortion High-Efficiency Class-D Power Amplifier in 130nm CMOS Technology

This paper presents a novel design of an ultra-broad-band class-D power amplifier (PA). The proposed architecture is based on the concept of the ripple buck converter after employing some modifications to facilitate supporting bandwidths up to tens of MHz. The input frequency range covered using the proposed class-D PA is from 0.5MHz up to 30MHz. A wide-band transformer has been used to convert the differential outputs of the class-D PA to single-ended load. The design has been implemented and fabricated using a CMOS 0. 13µm technology. It occupies an active area of 0.836mm2 and operates from a single 1.2V supply. The measurement results validates the functionality of the proposed class-D PA with good linearity and peak efficiency of 58% at 140mW of output power. This has been achieved using few off-chip passive components.
Task-oriented Hierarchical Control for a Quadruped Robot

This study aims to build a standard framework that can be applied to accomplish different tasks for a quadruped robot. To achieve this goal, a task-oriented hierarchical control framework is proposed. The framework is composed of four layers, including the task, action, movement, and joint layer, where the action layer plays a crucial role. An action is defined as a group of movements that take the robot from an initial position/orientation to another position/orientation. All actions form the action library, which can be combined to accomplish different tasks. Based on the hierarchical control framework, two tasks including path following and ditch crossing are achieved in this paper. The advantages of the proposed hierarchical control framework are its universality and extendibility since it can be easily applied to other tasks by enriching the action library. Simulation results in V-REP are given to validate the effectiveness of the proposed method.
Shared wireless link co-design for implantable device with far-field wireless power transfer

Implantable devices are nowadays commonly used in medicine. However, these devices run mostly on batteries, leading to a finite lifetime due to the required battery replacement. Since wireless power is almost available everywhere, a suitable way to avoid battery replacement would be to recharge them wirelessly using wireless power transfer. As these implantable devices mostly feature a single antenna, it must be shared to communicate and to receive power. In this way, a circuit that turns off the communication blocks and other circuits while recharging is needed. In this paper, an RF CMOS frontend with far-field wireless powering for implantable devices with a power management module will be presented. This circuit is able to control the front-end circuits when the battery reaches predefined operating voltages.
Three dimensional fast factorized back projection for sub-sediment imaging sonars

Low frequency sonar has the capability of penetrating sediment and imaging buried objects, making it a candidate technology for the detection and classification of buried unexploded ordnance (UXO). To improve detection and classification, systems often use planar arrays: a real-aperture in the across-track dimension and a synthetic aperture in the along-track dimension. By using a synthetic aperture planar array three-dimensional volumetric imagery of the sub-sediment environment can be produced. Synthetic aperture processing improves resolution in the along-track dimension and also greatly improves the signal-to-noise level for structural acoustic response analysis. A difficulty introduced by synthetic aperture processing is the heavy computational burden associated with reconstructing a three dimensional image from a planar array. This is particularly true when applying a generalized beamforming algorithm, such as back projection, to make the signal processing robust to array distortions that may be caused by non-linear sonar platform movement. For the two-dimensional synthetic aperture beamforming case a variety of algorithms have been proposed that retain the attractive generality of back projection but leverage sub-aperture processing to reduce computational burden. Of these, the most well-known is fast-factorized back projection (FFBP). In this paper the FFBP algorithm is extended to the three-dimensional case by leveraging a series of transformations between sub-apertures represented in spherical coordinates. Additionally, an asymmetry correction is introduced to compensate for the fact that the source and receive arrays of the relevant sonars are rarely designed to be axisymmetric. The asymmetry correction allows the first sub-aperture stage to be constructed from two-dimensional real-aperture image frames that are very efficient to create.
Crowd Estimation Using Electromagnetic Wave Power-Level Measurements: A Proof of Concept

Current crowd density estimation technologies that leverage IR depth perception, video and image processing or WiFi/BLE-based sniffing and probing have privacy and deployment issues. This paper presents a novel method for non-intrusive crowd density estimation that monitors variation in EM radiation within an environment. The human body's electrical and magnetic characteristics can be correlated with variations in available EM energy. This allows for the determination of the number of people within a room. Simulations conducted using Comsol to analyse and measure electromagnetic energy levels inside a room containing human bodies. Experimental analysis provides validation of the simulation results by showing $\text{0.8}\;\text{dBm}$ drop on the average level of EM energy per person.
Inductive Locating Method to Locate Miniaturized Wireless Sensors within Inhomogeneous Dielectrics

For the measurement of process data in bioreactors, very small wireless sensors are currently under development to replace the conventional rod probes. The so-called Sens-o-Spheres measure the temperature and in future the oxygen content and the pH of fluids. In order to evaluate the distribution of the measured values within the process, it is necessary to locate the wireless sensors. Because of the small size of the sphere (diameter 8 mm), inhomogeneous ambient media and the size of the reactor (less than 2 m), an inductive locating by magnetic fields with a frequency of f = 13.56 MHz is necessary. Since the behaviour of the magnetic field is very different from that of the electromagnetic wave, new locating methods are required, which are presented in this paper.
A Deep-Reinforcement-Learning-Based Optimization Approach for Real-Time Scheduling in Cloud Manufacturing

Resource scheduling problems (RSPs) in cloud manufacturing (CMfg) often manifest as dynamic scheduling problems in which scheduling strategies depend on real-time environments and demands. Generally, multiple resources in the CMfg scheduling process cause difficulties in system modeling. To solve this problem, we propose Sharer, a deep reinforcement learning (DRL)-based method that converts scheduling problems with multiple resources into one learning target and learns effective strategies automatically. Our preliminary results show that Sharer is comparable to the latest heuristics, adapts to different conditions, converges quickly, and subsequently learns wise strategies.
Towards a Real Time Obstacle Detection System for Unmanned Surface Vehicles

Environment perception plays a vital role in autonomous systems. Different sensory channels are exploited to capture a rich and a dynamic grasp of the surrounding to enable safe and efficient navigation. In this work, scene understanding in maritime environments is tackled using an obstacle detection and tracking procedure based on a video camera and a LIDAR (range sensor). We rely on state-of-the-art object detection CNN (Convolutional neural network) YOLO to detect obstacles in the image plane and we propose an efficient object tracking procedure to track objects across frames. LIDAR point clouds are used to locate obstacles in the world reference frame and thus, to derive a scene map with the obstacles and their headings which is used as an input to a path planning algorithm. the procedure is tested using data acquired from an unmanned surface vessel and typical metrics in object detection and tracking communities are used to evaluate our method. The results demonstrate improvement we achieve over the object detector baseline and other similar online tracking methods.
A Learning-based Inverse Kinematics Solver for a Multi-Segment Continuum Robot in Robot-Independent Mapping

Inverse kinematics (IK) is one of the most fundamental problems in robotics, as it makes use of the kinematics equations to determine the joint configurations necessary to reach a desired end-effector pose. In the field of continuum robot, solving the IK is relatively challenging, owing to kinematic redundancy with infinite number of solutions.In this paper, we present a simplified model to represent a multi-segment continuum robot using virtual rigid links. Based on the model, its IK can be solved using a multilayer perceptron (MLP), a class of feedforward neural network (FNN). The transformation between virtual joint space to task space is described using Denavit-Hartenberg (D-H) convention. Using 20,000 established training data for supervised learning, the MLP reaches a mean squared error of 0.022 for a dual-segment continuum robot. The trained MLP is then used to find the joints for different end-effector positions, and the results show a mean relative error of 2.90% can be on the robot configuration. Hence, this simplified model and its MLP provide a simple method to evaluate the IK solution of a two-segment continuum robot, which can also be further generalized and implemented in multi-segment cases.
Realization of Complex Terrain and Disturbance Adaptation for Hydraulic Quadruped Robot under Flying trot Gait

To improve the dynamic motion capability of the bionic quadruped robot, a kind of flying trot gait control method is proposed. Planning the motion of the torso and mapping it to the foot, the robot can achieve a stable flying trot movement. The motion controller is designed based on the spring loaded inverted pendulum (SLIP) model to achieve self-recovery stability under the external force disturbance of the robot. The proposed method can achieve the transition between the trot and the flying trot gait, where the flying trot gait can improve the speed of the quadruped robot and trot gait motion has a better robust ability. The dynamic simulation results of Webots verify that the method can achieve three-dimensional stable and robust flying trot gait. The quadruped robot can effectively climb the slopes within 20◦ in flying trot gait and resist lateral impact forces. Experiments on the physical platform SCalf-III verify the effectiveness of the proposed method to achieve stable flying trot gait.