Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Gait Planning for Walking Assistance Lower Limb Exoskeletons in Slope Scenarios

Lower-limb exoskeleton has gained considerable interests in walking assistance applications for paraplegic patients. In walking assistance of paraplegic patients, the exoskeleton should have the ability to help patients to walk over different terrains in the daily life, such as slope terrains. One critical issue is how to plan the stepping locations on slopes with different gradients, and generate stable and human-like gaits for patients. This paper proposed an adaptive gait planning approach which can generate gait trajectories adapt to slopes with different gradients for lower-limb walking assistance exoskeletons. We modeled the human-exoskeleton system as a 2D Linear Inverted Pendulum Model (2D-LIPM) with an external force in the two-dimensional sagittal plane, and proposed a Dynamic Gait Generator (DGG) based on an extension of the conventional Capture Point (CP) theory and Dynamic Movement Primitives (DMPs). The proposed approach can dynamically generate reference foot locations for each step on slopes, and human-like adaptive gait trajectories can be reproduced after the learning from demonstrated trajectories that sampled from level ground walking of normal healthy human. We demonstrated the efficiency of the proposed approach on both the Gazebo simulation platform and an exoskeleton named AIDER. Experimental results indicate that the proposed approach is able to provide the ability for exoskeletons to generate appropriate gaits adapt to slopes with different gradients.
Road Detection through CRF based LiDAR-Camera Fusion

In this paper, we propose a road detection method with LiDAR-camera fusion in a novel conditional random field (CRF) framework to exploit both range and color information. In the LiDAR based part, a fast height-difference based scanning strategy is applied in the 2D LiDAR range-image domain and a dense road detection result in camera image domain can be obtained through geometric upsampling given the LiDAR-camera calibration parameters. In the camera based part, a fully convolutional network is applied in the camera image domain. Finally, we fuse the dense and binary road detection results from both LiDAR and camera in a single CRF framework. Experiments show that using a single thread of CPU, the proposed LiDAR based part can operate at a frequency of over 250Hz with sparse output in range image and 40Hz with dense result in camera image for the 64-beam Velodyne scanner. Our CRF fusion method achieves very promising road detection performance on the KITTI-Road dataset.
Central Pattern Generators Control of Momentum Driven Compliant Structures

We introduce the concept of Momentum Driven Structures (MDS) made of inertially actuated units linked together by compliant elements as a potential solution for rough environments exploration. We propose a control method for MDS based on the bio-inspired concept of Central Pattern Generator (CPG) and study in simulation the impact of compliance distribution on locomotion performance using population based optimization techniques. Our results suggest that compliant structures outperform their rigid counterparts in terms of distance traveled. In addition, we show that co-evolved structures perform only marginally better than their control-only optimized equivalent, highlighting the fact that compliance modulation may not be a significant asset in such experiments, considering the related hardware complexity it introduces.
Reference Architecture framework for enhanced social media data analytics for Predictive Maintenance models

Social Media data contains a lot of hidden information which is currently rarely used in the manner of service topics on product level. However, a deep analysis of existing predictive maintenance models shows, that the combined use of social media data with already existing data from products or internal service data can improve existing and new analytical models for an enhancing predictive maintenance. Therefore, this framework paper describes an approach how to gather, process and analyze Social Media data related to products of a power tool producer. The defined processes are executed with the Azure Machine Learning Studio and are visualized with Power Bi. The main result of this paper is the Reference Architecture which combines several processes combine heterogenous data sources and enable the "First time to Incident" algorithm which helps companies to to increase the precision of Predictive Maintenance.
Detecting Human Movement during Vital Sign Measurement using Heterogeneous Sensors

Research on the detection of human movement by using accelerometers, gyroscopes, etc. is steadily progressing. These sensors function by setting a specific threshold value for a given movement. However, it is difficult to measure the distance travelled by humans or their position with the sensor alone. This paper explores a method of detecting human movement using machine learning on a UWB impulse radar and information from a AHRS sensor while measuring respiration, thereby offering a method that simultaneously uses data from two heterogeneous sensors during the measurement of respiration.
Depth Generation Network: Estimating Real World Depth from Stereo and Depth Images*

In this work, we propose the Depth Generation Network (DGN) to address the problem of dense depth estimation by exploiting the variational method and the deep-learning technique. In particular, we focus on improving the feasibility of depth estimation under complex scenarios given stereo RGB images, where the stereo pairs and/or depth ground-truth captured by real sensors may be deteriorated; the stereo setting parameters may be unavailable or unreliable, hence hamper efforts to establish the correspondence between image pairs via supervision learning or epipolar geometric cues. Instead of relying on real data, we supervise the training of our model using synthetic depth maps generated by the simulator, which deliver complex scenes and reliable data with ease. Two non-trivial challenges, i.e., (i) attaining reasonable amount yet realistic samples for training, and (ii) developing a model that adapts to both synthetic and real scenes arise, whereas in this work we mainly deal with the later one yet leveraging state-of-the-art Falling Things (FAT) dataset to overcome the first. Experiments on FAT and KITTI datasets demonstrate that our model estimates relative dense depth in fine details, potentially generalizable to real scenes without knowing the stereo geometric and optic settings.
A Multi-Vehicle Trajectories Generator to Simulate Vehicle-to-Vehicle Encountering Scenarios

Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing. This paper introduces a multi-vehicle trajectory generator (MTG) that can encode multi-vehicle interaction scenarios (called driving encounters) into an interpretable representation from which new driving encounter scenarios are generated by sampling. The MTG consists of a bi-directional encoder and a multi-branch decoder. A new disentanglement metric is then developed for model analyses and comparisons in terms of model robustness and the independence of the latent codes. Comparison of our proposed MTG with $\beta$-VAE and InfoGAN demonstrates that the MTG has stronger capability to purposely generate rational vehicle-to-vehicle encounters through operating the disentangled latent codes. Thus the MTG could provide more data for engineers and researchers to develop testing and evaluation scenarios for autonomous vehicles.
Online Utility-Optimal Trajectory Design for Time-Varying Ocean Environments

This paper considers the problem of online optimal trajectory design under time-varying environments. Of particular interest is the design of energy-efficient trajectories under strong and uncertain disturbances in ocean environments and time-varying goal location. We formulate the problem within the constrained online convex optimization formalism, and a modified online gradient descent algorithm is motivated. The mobility constraints are met using a carefully chosen stepsize, and the proposed algorithm is shown to incur sublinear regret. Different from the state-of-the-art algorithms that entail planning and re-planning the full trajectory using forecast data at each time instant, the proposed algorithm is entirely online and relies mostly on the current ocean velocity measurements at the vehicle locations. The trade-off between excess delay incurred in reaching the goal and the overall energy consumption is examined via numerical tests carried out on real data obtained from the regional ocean modelling system. As compared to the state-of-the-art algorithms, the proposed algorithm is not only energy-efficient but also several orders of magnitude computationally efficient.
Path Following Controller for Differentially Driven Planar Robots with Limited Torques and Uncertain and Changing Dynamics

This paper presents a path following controller that is suitable for asymmetrical planar robots with significant mass and limited motor torques. the controller is resistant against environmental forces, and inaccurate estimates of robot’s inertia, by estimating their effects with unscented kalman filter. the controller outputs wheel torque commands which take in account the motor torque limits and given relative priority of internal control elements. the method presented is thoroughly explained and the simulation results demonstrate the performance of the controller.
Multimodal Spatio-Temporal Information in End-to-End Networks for Automotive Steering Prediction

We study the end-to-end steering problem using visual input data from an onboard vehicle camera. An empirical comparison between spatial, spatio-temporal and multimodal models is performed assessing each concept’s performance from two points of evaluation. First, how close the model is in predicting and imitating a real-life driver’s behavior, second, the smoothness of the predicted steering command. The latter is a newly proposed metric. Building on our results, we propose a new recurrent multimodal model. The suggested model has been tested on a custom dataset recorded by BMW, as well as the public dataset provided by Udacity. Results show that it outperforms previously released scores. Further, a steering correction concept from off-lane driving through the inclusion of correction frames is presented. We show that our suggestion leads to promising results empirically.
Dentronics: Review, First Concepts and Pilot Study of a New Application Domain for Collaborative Robots in Dental Assistance

In this paper we introduce dentronics as a new emerging application domain for collaborative lightweight robots in the dental context backed up by a user survey supporting the clear need. Specifically, we developed a multi-modal interaction framework, applied this framework to a specific dental use-case, and conducted a preliminary user-study for evaluation. Our results demonstrate usability and feasibility beyond a controlled experimental setup. We conclude that dentronics is indeed within reach given today’s technology and deserves further investigation towards clinical use.
Dynamic Modeling and Gait Analysis for Miniature Robots in the Absence of Foot Placement Control

The study of animals and insects have led to realization that animals select their gaits, patterns of leg movement, according to speed. For proper gait planning, the legs must be controlled for proper foot placement with respect to the body motion and ground interactions. However, in small scale robotic platforms gait planning through foot placement control is neither cost effective nor easily attainable due to a lack of available sensors. Thus, even though a desired gait is envisioned at the design phase, it is not known whether the gait is optimum. In this work, we present the comprehensive dynamic model of the miniature foldable robot, MinIAQ-II, which has four independently actuated legs. Dynamic model is used to perform gait analysis, to investigate the difference between the intended gait and the achieved gait in the absence of foot placement control. The model is verified through slow speed walking experiments on flat terrain. The work presented can be modified for different miniature robots with passive legs to predict their locomotion under no foot placement control.
Integral Backstepping Position Control for Quadrotors in Tunnel-Like Confined Environments

There are many potential applications that require flying robots to navigate through tunnel-like environments, such as inspections of small railway culverts and mineral mappings of mining tunnels. Nevertheless, those environments present many challenges for quadrotors to navigate through. The aerodynamic disturbances created from the fluid interaction between the propellers’ downwash and the surrounding surfaces of the environment, as well as longitudinal wind gusts, add hardship in stabilising the vehicle while the restricted narrow space increases the risk of collision. Furthermore, poor visibility and dust blown by the downwash make vision-based localisation extremely difficult. This paper presents a cross-sectional localisation system using Hough Scan Matching and a simple kinematic Kalman filter. Using the estimated state information, an integral backstepping controller is implemented which enables quadrotors to robustly fly in tunnel-like confined environments. A semi-autonomous system is proposed with self-stabilisation in the vertical and lateral axes while a pilot provides commands in the longitudinal direction. The results of a series of experiments in a simulated tunnel show that the proposed system successfully hovered itself and tracked various trajectories in a cross-sectional area without the aid of any external sensing or computing system.
Implementation of Software-Defined 6LoWPANs in Contiki OS

Wireless Sensor Networks (WSNs) are one of the main building blocks of the future Internet of Things (IoT). Thanks to the introduction of the 6LoWPAN standard, WSNs can also be integrated into IP networks in a seamless manner. In a previous work, we defined SD-6LoWPAN, an architecture specifically designed to enable software defined networking in 6LoWPANs. In this work, we focus on the implementation of the proposed SDN solution on top of real constrained devices powered by the Contiki operating system. We illustrate the main challenges discovered, and how they have been addressed by extending the Contiki network stack in order to provide a working prototype of SD-6LoWPAN. The latter is then validated by running a set of experiments on a real testbed.
Optimal Proactive Path Planning for Collaborative Robots in Industrial Contexts

The coexistence of humans and robots in the future production plants is one of the pillars of Industry 4.0. Humans and robots will collaborate to accomplish common tasks in order to mutually compensate their deficiencies. In recent years, many efforts have been spent to develop safe motion planning strategies, designed to prevent robots from injuring humans. Most of the previous techniques are classifiable as reactive, since the considered motion controllers impose some local corrective actions in order to dodge the space occupied by the human. In this paper, a proactive approach is adopted, optimizing robotic paths according to a prediction of the volume occupied by the human when collaborating with the robot. The validity of the approach is shown in a realistic use-case involving the collaboration of a human operator with a 7 degrees robotic arm, the ABB YuMi.
A Study of a Parallel Architecture for Accelerating Batch-Learning Self-Organizing Map by using Dedicated Hardware

Batch-learning SOM (BL-SOM) is known as one type of SOM, where all input vectors are processed in one batch and updated collectively. The advantage of BL-SOM is that the resulting map is obtained independently with the order of input vectors. However, when applied to a large set of input vectors, the amount of computation becomes enormous. In this paper, we investigate a parallel architecture for accelerating Batch-learning Self-organizing map by using dedicated hardware. In the evaluation, we present the estimation results of the relationship between the execution time and chip area for various degrees of parallelism.
Novel Integrated Three-Port Bidirectional DC/DC Converter for Energy Storage System

The study proposes a novel integrated three-port bidirectional dc/dc converter for energy storage systems. The converter includes two batteries, namely 24- and 48-V batteries, used as input source and for backup energy, respectively. Each battery can supply to dc load in the normal case. When the grid power fails, 24-V battery input is stepped up to the dc bus through a high step-up converter. The 48-V battery serves as a buffer power supply when the load increases instantaneously. At night, when the 48-V battery is under low power consumption, the dc bus can charge the battery. In addition, the converter can monitor both battery voltages simultaneously; when one battery is used excessively, the other battery can charge it, thus keeping the system power stable. The integrated three-port bidirectional dc/dc converter is a combination of a boost–flyback, forward converter, and voltage doubler and has the following advantages: 1) it operates in input continuous current and low voltage stress; 2) it provides input current recovery; 3) it improves high reverse voltage caused by the transformer; 4) it operates in zero current switching (ZCS); and 5) its doubler circuit can flexibly adjust the dc bus voltage. A 500-W three-port bidirectional dc/dc device was implemented to verify the feasibility and practicability of the proposed converter. The highest efficiency achieved for operation in 24-V battery high step-up mode was 95.3%; it was 94.9% and 95.2% in 48-V battery step-up and step-down mode, respectively.
On the Extension of Digital Ecosystems for SCM and Customs with Distributed Ledger Technologies : Requirements Anlysis, Innovation Assessment, and Prototype Design for the Lake Constance Region

Global supply chains represent the backbone of the modern manufacturing industry. Planning of global supply chains still represents a major hurdle, mainly because of the high complexity and unforeseen disruptions that have to be mastered for meeting the different logistics windows in a globally distributed production environment. Trust in supply chains is an additional challenge. A major – albeit sometimes overlooked - part of Supply Chain Management (SCM) is the management and integration of customs processes, clearing of tariffs, (re-)billing of customers, and fulfilling other legal requirements related to crossing borders, ranging from environmental standards over goods inspection to general paper work. With the exception of work offered by the World Customs Organization (WCO) the issue of customs and blockchain is still underrepresented in research and practice. In this paper, we look at innovations that drive the current ICT-enabled SCM research and how these can be combined with smart customs management. After a literature review and introduction to the state-of-the-art, we list potential trust-based innovations for SCM and customs in digital business ecosystems. Based upon the innovations we also describe a requirements analysis of existing distributed ledger technologies (requirements for system layout, system configuration, system governance). A description of the prototype for the Lake Constance region – on which we are currently working – concludes the paper.
Joint Cotask-Aware Offloading and Scheduling in Mobile Edge Computing Systems

Mobile edge computing (MEC) systems provide mobile devices (MDs) with low-latency cloud services by deploying edge servers (ESs) in the vicinity. In fact, various mobile applications may generate cotasks, each of which is completed only if all its constituent tasks are finished. Existing works have been devoted to the design of MEC offloading and scheduling, but none of them exploits the cotask feature to better utilize the networked computing resources. In this paper, we investigate the problem of joint cotask-aware offloading and scheduling in MEC systems (Cool-Edge), and we formulate it as a mixed integer non-linear program (MINLP), the objective of which is to minimize average cotask completion time (ACCT). To cope with the Cool-Edge problem, we propose two low-complexity algorithms to offload cotasks based on an LP rounding technique and schedule them according to an earliest-cotask-arrival-first rule, respectively, and we further prove the approximation factor jointly achieved by the two algorithms. Finally, we conduct testbed experiments and simulations to demonstrate the effectiveness of our proposed solution, and we also show how ACCT varies with the network environment.
Enrely: A reliable MLC PCM Architecture based on Data Encoding

Phase Change Memory (PCM) has promising features of a good candidate to replace existing memory technologies. Multi-level cell (MLC) operation leads to enhanced storage density in PCM It however suffers from poor reliability due to the resistance drift problem. Exploiting the data-dependent nature of the resistance drift problem, we introduce a novel MLC PCM architecture, named ‘Enrely’ based on effective data encoding. With a simple flip operation applied ingeniously to the data prior to a write, Enrely enhances the reliability of MLC PCM. Simulation results indicate up to an order of 104 times reduction in Error Rate using Enrely as compared to the conventional MLC PCM.
IoTTracker: An Enhanced Engine for Discovering Internet-of-Thing Devices

Effectively identifying IoT devices in cyberspace is significant for grasping the security posture of cyberspace. However, there are still some IoT devices without vendor or product keywords in response data that cannot be identified by existing device identification engines. In this paper, we propose a new engine (IoT Tracker)for identifying IoT devices by leveraging the highest similarity of response data between IoT devices of the same vendor or product. Based on the protocol features, IoT Tracker divides application-layer protocols into semistructured data protocols and unstructured data protocols. For each category, IoT Tracker extracts structure structure, style structure or simhash feature. Then, IoTTracker utilizes features extracted from the response data to identify IoT devices. We implement a prototype of our proposed engine and evaluate its effectiveness through real-world experiments. The experimental results show that IoTTracker yield very high accuracy with 95.54 % precision and 93.08 % recall at vendor-level. Compared with existing methods, IoT Tracker adds 40.76% of identifiable devices after de-duplicating the identified dataset.
License Plate Detection and Integral Intensity Projection for Automatic Finding the Vacant of Car Parking Space

This paper demonstrates approaches for automatic counting the number of car in a parking lot image by using the location of license plate area and the result of intensity projection. Firstly, in order to obtain the car height boundaries, we applied the edge processing histogram in a vertical direction. This histogram results from the total summation of difference intensity values between the pixel and it neighboring pixel of the enhanced image. Next, the result of derivative method is applied to segment the image into the region of interest (ROI). To estimate the license plate area, the edge processing histogram is utilized again along the horizontal direction. After that the integral intensity projection is applied to sum and smooth both intensity projection result of the histogram and the ROI image in vertical direction. Finally, the number of parking spots were detected and counted by using peak detection results. The accuracy for counting the number of cars in the indoor parking lot image is 81.16% by applying these techniques.