Miracle-dz/UniFAR-FTUs huggingface dataset:
An Adaptive Velocity Obstacle Avoidance Algorithm for Autonomous Surface Vehicles

This paper presents a new algorithm for a real-time obstacle avoidance for autonomous surface vehicles (ASV) that is capable of undertaking preemptive actions in complex and challenging scenarios. The algorithm is called adaptive velocity obstacle avoidance (AVOA) and takes into consideration the kinematic and dynamic constraints of autonomous vessels along with a protective zone concept to determine the safe crossing distance to obstacles. A configuration space that includes both the position and velocity of static or dynamic elements within the field-of-view of the ASV is supporting a particle swarm optimization procedure that minimizes the risk of harm and the deviation towards a predefined course while generating a navigation path with capabilities to prevent potential collisions.Extensive experiments demonstrate the ability of AVOA to select a velocity estimative for ASVs that originates a smoother, safer and, at least, two times more effective collision-free path when compared to existing techniques.
Dempster Shafer Grid-based Hybrid Fusion of Virtual Lanes for Autonomous Driving

Lane estimation plays a central role in the context of self-driving vehicles, requiring clear road markings and a high quality infrastructure. To meet the safety requirements and manage difficult use cases (i.e. poor quality or absence of road- marking, traffic jams, severe weather conditions, etc.) without depending on cartography and lane markings, we propose a lidar-based Virtual Lanes (VL) generation system to provide a comfortable and safe ride. For this purpose, hybrid fusion based on Dempster-Shafer Theory (DST) coupled with a particle filter is introduced. Two VL strategies are merged: the first constructs virtual lanes based on independent road-borders detection, while the second uses moving objects trajectories. A novel lane similarity computation is also adopted in order to estimate an effective lane comparison. The performance is reported through extensive experiments with Valeo demo-car on highway and beltway roads. Experimental results demonstrate the accurate and robust performance of the proposed system.
Piecewise Rigid Scene Flow with Implicit Motion Segmentation

In this paper, we introduce a novel variational approach to estimate the scene flow from RGB-D images. We regularize the ill-conditioned problem of scene flow estimation in a unified framework by enforcing piecewise rigid motion through decomposition into rotational and translational motion parts. Our model crucially regularizes these components by an L0 “norm”, thereby facilitating implicit motion segmentation in a joint energy minimization problem. Yet, we also show that this energy can be efficiently minimized by a proximal primal-dual algorithm. By implementing this approximate L0 rigid motion regularization, our scene flow estimation approach implicitly segments the observed scene of into regions of nearly constant rigid motion. We evaluate our joint scene flow and segmentation estimation approach on a variety of test scenarios, with and without ground truth data, and demonstrate that we outperform current scene flow techniques.
QoE-based Load Balancing of OTT Video Content in SDN Networks

The proliferation of multimedia devices and video content availability is producing an increase in video content consumption. Video is now the most significant slice of all Internet traffic produced, and it is still growing. However, Over-The-Top (OTT) video is the one significantly increasing its strength, causing infrastructural and business challenges to Telecommunication Operators (Telcos), which provide traditional video services. Telcos must improve their network infrastructure to sustain future demand for OTT video content while improving user perceived Quality-of-Experience (QoE). A meaningful way to accomplish that is to distribute hardware appliances compatible with the Software-Defined Networking (SDN) paradigm throughout the network, improving flexibility and capacity while introducing new ways of content delivery.Towards this objective, this paper proposes an SDN load balancing approach to distribute OTT video requests through multiple servers. The load balancing decision is performed at the edge of the network influenced by the client's QoE. Comparing to a traditional load balancing scheme, the proposed method outperforms the non-SDN approaches in scenarios in which the server-side infrastructure starts to become stressed.
PCon: A Novel Opportunistic Routing Protocol for Duty-Cycled Internet of Underwater Things

Internet of Underwater Things (IoUTs) has emerged as an evolution of traditional underwater wireless sensor networks, with programmable nodes interconnected to the Internet. Despite the advancements, IoUTs will still face critical challenges imposed by the use of the lossy and energy-hungry underwater acoustic channel. Two major critical challenges faced by IoUT applications are the low reliable data delivery due to the poor quality of underwater acoustic links, and the high energy cost for underwater wireless communication. In this paper, we tackle both challenges by proposing the PCon protocol. The PCon is a power-controlled opportunistic routing protocol for data routing in duty-cycled IoUTs. At each hop, the PCon protocol selects the most suitable transmission power, from a set of discrete transmission power levels, to maintain a reasonable data delivery ratio while reducing the energy consumption of duty-cycled IoUTs. To do so, the PCon takes into consideration the energy cost for delivering the data packet, calculated as a function if the probability of having the next-hop node awake during the transmission. Simulation results showed that the PCon protocol, even in a harsh scenario of duty-cycling of 50%, ensures a packet delivery rate of 40% while decreases the energy cost in 78%.
MQTT-based Middleware for Container Support in Fog Computing Environments

Distributed architectures where the Internet of Things (IoT) and the cloud are efficiently integrated play an increasingly important role for IoT solutions. Among these architectures, there is a growing interest in the ones that support the opportunity of functionality offloading towards either intermediate fog nodes or IoT end devices. Relevant existing research work has mainly focused so far on virtual machine and container migration to intermediate fog nodes and on migration of very simple functions to IoT endpoints (to preserve their limited resources available). In this paper, we originally concentrate on the gap associated with benefitting from fog functionality at resource-powerful IoT endpoints, to create a continuum deployment that glues IoT devices and the cloud. In particular, this paper originally presents a middleware that manages application deployment and life-cycle by simplifying and optimizing management operations such as device configuration and application constraint satisfaction. The proposed solution particularly fits highly articulated scenarios with large numbers of IoT devices and intermediate fog nodes, by supporting the opportunity to offload functionality in a split way between IoT endpoints and edge nodes. The reported experimental results confirm the feasibility of our approach in term of overhead, scalability, and application life-cycle management.
Map Based Human Motion Prediction for People Tracking*

Mobile service robots deployed in populated environments like train stations, airports or offices are not only required to move safely, but also in socially acceptable ways. In order to achieve this, robots need to be able to track people within their vicinity. This work presents an approach to tracking people from a mobile robot platform, incorporating a novel approach to human motion prediction. Unfavorable viewing angles, motion blur and ever-changing light conditions are constant issues for sensors on mobile vehicles. Therefore, a system is needed which increases the tracking quality of humans in order to cope with a low detection rate. The scientific contribution of this paper lies in a precise human model for tracking which utilizes historical spatial data of pedestrians from previous detection and from simulation. The model is embedded into a particle-filter based tracking approach designed for the use on a moving platform and is able to incorporate a variety of person detectors. Experiments conducted prove that the proposed method increases tracking quality, especially at a low detection rate.
Prediction of Human Arm Target for Robot Reaching Movements

The raise of collaborative robotics has allowed to create new spaces where robots and humans work in proximity. Consequently, to predict human movements and his/her final intention becomes crucial to anticipate robot next move, preserving safety and increasing efficiency. In this paper we propose a human-arm prediction algorithm that allows to infer if the human operator is moving towards the robot to intentionally interact with it. The human hand position is tracked by an RGB-D camera online. By combining the Minimum Jerk model with Semi-Adaptable Neural Networks we obtain a reliable prediction of the human hand trajectory and final target in a short amount of time. The proposed algorithm was tested in a multi-movements scenario with FANUC LR Mate 200iD/7L industrial robot.
Proposal for Disassembly of Electric Vehicle Batteries used in the Volkswagen Jetta Hybrid System

Li-ion batteries (LIBs) are currently the most available and suitable energy storage device for powering electric vehicles (EVs) due to their wantable properties including high energy efficiency, lack of memory effect, high energy density, high power density and long life cycle. These advantages allow them to be smaller and lighter than other conventional rechargeable batteries such as lead-acid batteries, nickel-cadmium (Ni-Cd) and nickel-metal (Ni-MH) hybride batteries. The rising number of electric vehicles comes along with an increasing demand for LIBs. As resources such as lithium are valuable, it is economically desired to recycle EV batteries. One of the first steps of every battery recycling process is the disassembly, which can be a quite time and cost consuming process and hence has to be planned accurately and properly. Using the battery of the hybrid car Volkswagen Jetta Hybrid as a case study, a planning approach for the disassembly will be discussed in this paper. Therefore, disassembly sequences will be derived from a priority matrix and a disassembly graph will be drawn up. In fine, the design of the disassembly system and work stations will be recommended.
A Benchmark for Visual-Inertial Odometry Systems Employing Onboard Illumination

We present a dataset for evaluating the performance of visual-inertial odometry (VIO) systems employing an onboard light source. The dataset consists of 39 sequences, recorded in mines, tunnels, and other dark environments, totaling more than 160 minutes of stereo camera video and IMU data. In each sequence, the scene is illuminated by an onboard light of approximately 1300, 4500, or 9000 lumens. We accommodate both direct and indirect visual odometry methods by providing the geometric and photometric camera calibrations (i.e. response, attenuation, and exposure times). In contrast with existing datasets, we also calibrate the light source itself and publish data for inferring more complex light models. Ground-truth position data are available for a subset of sequences, as captured by a Leica total station. All remaining sequences start and end at the same position, permitting the use of total accumulated drift as a metric for evaluation. Using our proposed benchmark, we analyze the performance of several start-of-the-art VO and VIO frame-works. The full dataset, including sensor data, calibration sequences, and evaluation scripts, is publicly available online at http://arpg.colorado. edu/research/oivio.
A Cloud based Soil health Digitalization and Monitoring Technique for Optimum Resource Utilization in Smart Farming

The journey of an Agricultural crop from seed germination to the harvesting stage encounters several stages of physical and biochemical changes. The Soil parameters like moisture, macronutrients, and micronutrients play a decisive role to get a healthy agriculture crop with minimal investment and maximal productivity. The information about real-time values of Soil parameters to the end users (farmers) along with scientific advice is essential to avoid over-use or under-use of resources like water and fertilizers, leading to optimal resource utilization. The paper presents Soil sensing technique using spectroscopy and other conventional methods, and its IoT interfacing to measure and digitally disseminate the real-time on-the-soil / information of soil health to the farmers. Further, a cloud based prognostic approach is adopted to assist the farmers for their pilot crops via Maize, Sorghum, and Gram. The proposed technique can be a novel effort in digitalization of manual Soil Health card (Mrida card), and help farmers to increase crop productivity through minimal investigation water and fertilizers.
Transfer Learning Based Method for Two-Step Skin Cancer Images Classification

Classification of skin cancer images is an important research challenge because the number of people is expected to increase significantly in the next years and consequently the number of people that might have skin cancer in one phase of life will increase accordingly. Some types of skin cancer can be treated successfully if they are detected in the early stages and thus the study of the skin cancer images using the latest technological innovations might lead to better results than in the case when traditional methods are applied. In this article is presented a method for the classification of skin cancer images that consists of two steps and which is based on transfer learning and deep learning. The classification models are developed in Python using the PyTorch machine learning library and the dataset used as experimental support for testing and validating the transfer learning based method is Human Against Machine with 10000 training images (HAM10000) dataset. In the first step the accuracy of the prediction model for testing data is 85% and in the second step the accuracy of the prediction model for testing data is 75%.
Robotic Ultrasound for Catheter Navigation in Endovascular Procedures

Endovascular procedures require real time visual feedback on the location of inserted catheters. This is currently achieved using X-ray fluoroscopy, which causes exposure to radiation. This study describes an alternative method using a robotic ultrasound system for catheter tracking and navigation in endovascular interventions, focusing on endovascular aneurysm repair. This approach relies on the registration of pre-operative images to provide both a tracking trajectory and visual feedback of the real-time catheter position. The procedure was validated on healthy volunteers and on a phantom that included a realistic vessel structure, showing an average tracking error of the moving catheter tip of 1.78±1.02 mm.
PPR-Net:Point-wise Pose Regression Network for Instance Segmentation and 6D Pose Estimation in Bin-picking Scenarios

Accurate object 6D pose estimation is a core task for robot bin-picking applications, especially when objects are randomly stacked with heavy occlusion. To address this problem, this paper proposes a simple but novel Point-wise Pose Regression Network (PPR-Net). For each point in the point cloud, the network regresses a 6D pose of the object instance that the point belongs to. We argue that the regressed poses of points from the same object instance should be located closely in pose space. Thus, these points can be clustered into different instances and their corresponding objects’ 6D poses can be estimated simultaneously. In our experiments, PPR-Net outperforms the state-of-the-art approach by 15% - 41% in average precision when evaluated on the benchmark Siléane dataset. In addition, it also works well in real world robot bin-picking tasks.
Towards Human Mobility Detection Scheme for Location-Based Social Network

Location-based Social networks (LBSN) are a promising source for human mobility mining that attracted a lot of attention in the research community. The aim of this research is to discover the patterns and mobility of LBSN such as Twitter by analyzing the spatiotemporal features of user’s tweets. Twitter Streaming Application Program Interface (API) was used to capture Geo-tagged tweets. The analysis shows that geotagged tweets can obtain valuable information on human mobility, such as the relation between movement flow and the time of the day. It provides evidence that Twitter data can be useful for tracking human movement and understand human behavior pattern.
Collaborative Human Augmented SLAM

In this paper, we are proposing a collaborative SLAM system between a team of three heterogeneous agents: a robot, a human operator, and an augmented reality head mounted display (ARHMD). The system allows for online editing of a map produced by a robot running SLAM. Through hand gestures, the user can edit, in real time, the robot map that is augmented on top of the physical environment. Moreover, the proposed system leverages the built-in SLAM capabilities of the AR-HMD to correct the robot’s map and map areas that are not yet discovered by the robot. Our method aims to combine the unique and complementary capabilities of each of the three different agents to produce the maximum possible mapping accuracy in the minimum amount of time. The proposed system is implemented on ROS and Unity. Experiments performed demonstrate the considerably superior SLAM outputs in terms of reducing mapping time, eliminating maps post-processing, and increasing mapping accuracy.
Mapping for Planetary Rovers from Terramechanics Perspective*

In an autonomous scientific exploration system, the terrain map generated from mapping process integrates sensing information from multiple aspects and lays the base for decision making processes. With the increasing challenges in planetary exploration, equipping planetary rovers with the principles of terramechanics is becoming more and more common, especially on rough or intricate terrain. However, it is difficult for conventional maps with elevation information only to reflect terrain mechanical properties, which play important roles in terramechanics-based simulation or motion control. This study extracts the dominant parameters in terrain bearing and shearing models, and presents a multi-layered grid map with fundamental geometric and mechanical elements. A corresponding mapping scheme based on dense visual input is designed to reconstruct elevation in the map and predict terrain mechanical parameters of the entire visual field. Experiments are conducted to verify the practicability of the approach proposed in a Mars emulation yard with a rover prototype.
3D Printed Single Incision Laparoscopic Manipulator System Adapted to the Required Forces in Laparoscopic Surgery

3D printing in medical technology is mainly used to produce customized devices for surgical planning or surgical guides and templates. However, for laparoscopic systems, 3D printing is hardly used. A challenge is the transmission of the necessary forces required in laparoscopic surgery.We designed and manufactured a 3D printed customizable manipulator system for Single Incision Laparoscopic Surgery (SILS), fulfilling the force-related requirements in laparoscopic surgery.The Single Incision Laparoscopic Manipulator System can apply forces at its tip up to 5.6 N with acceptable operating forces not exceeding 30 N. We showed the functionality of the manipulator system in a manipulation experiment in comparison to standard SILS instruments.
Virtual-mass-ellipsoid Inverted Pendulum Model and Its Applications to 3D Bipedal Locomotion on Uneven Terrains

It is still an open problem to develop a reduced order model of bipedal walking that closely represents the complex dynamics of humanoid robots. In this paper, we propose control methodologies, removing both the constant CoM height constraint and the constant centroidal angular momentum constraint. We define a capturability criterion. and propose an enhanced intrinsically stable model predict control to fulfill this new capturability criterion. Then the angular momentum can be controlled. The results of simulations using humanoid robot HRP-4 show the proposed methods can improve the stability of bipedal locomotion on uneven terrains.
Design, Modeling and Testing of a Flagellum-inspired Soft Underwater Propeller Exploiting Passive Elasticity

Flagellated micro-organism are regarded as excellent swimmers within their size scales. This, along with the simplicity of their actuation and the richness of their dynamics makes them a valuable source of inspiration to design continuum, self-propelled underwater robots. Here we introduce a soft, flagellum-inspired system which exploits the compliance of its own body to passively attain a range of geometrical configurations from the interaction with the surrounding fluid. The spontaneous formation of stable helical waves along the length of the flagellum is responsible for the generation of positive net thrust. We investigate the relationship between actuation frequency and material elasticity in determining the steady-state configuration of the system and its thrust output. This is ultimately used to perform a parameter identification procedure of an elastodynamic model aimed at investigating the scaling laws in the propulsion of flagellated robots.
A Ring Network Protocol for Articulated Robots

Articulated robots such as the humanoid robot often have multiple joints implemented in its limbs. It is common for sensor and controller cables to be routed inside of the limb, impacting mobility. A network protocol with high efficiency, low latency, fault tolerance is needed to facilitate communication of sensor and controller data over a more compact communications channel. In this paper, we propose a ring network protocol with fault tolerance in low latency, high efficiency communication over a compact channel. The performance of the proposed network protocol was verified in simulation and experiment, showing that low latency, high efficiency, and fault tolerance can be achieved over a single communications channel.
Efficient Design of Tightly Coupled Dipole Array Using an Equivalent Circuit-Based Approach

This paper proposes an efficient and systematic design approach for tightly coupled dipole array (TCDA) designs. In this approach, the radiator structure and feeding structure are designed and optimized based on their equivalent circuit models (ECM) respectively. A novel ECM is proposed to accurately describe the impedance characteristics of the radiator structure with dual-diamond dipoles. The proposed ECM of the radiator structure keeps its accuracy for variable design parameters. The ECM of the proposed feeding structure is also developed to analyze its impedance characteristics. As compared with conventional brute-force optimizations based on full-wave simulations, the two ECMs make it easy and efficient to design wideband TCDAs. A dual-polarized TCDA is designed with the proposed approach to illustrate the efficiency of the approach. The resultant TCDA operates in a wide frequency band of 0.87-10.14 GHz with VSWR ≤2 for broadside radiation. Meanwhile, the VSWR value under all conditions (broadside, ±60° scanning in E- and H-planes) remains ≤3 over the frequency band of 0.8-9 GHz. A $10\,\,{\times }\,\,10$ prototype is fabricated to demonstrate the design.