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Unmanned aerial vehicles (UAVs) are becoming largely ubiquitous with an increasing demand for aerial data. Accurate navigation and localization, required for precise data collection in many industrial applications, often relies on RTK GNSS. These systems, able of centimeter-level accuracy, require a setup and calibration process and are relatively expensive. This paper addresses the problem of accurate positioning and navigation of UAVs through cooperative localization. Inexpensive ultra-wideband (UWB) transceivers installed on both the UAV and a support ground robot enable centimeter-level relative positioning. With fast deployment and wide setup flexibility, the proposed system is able to accommodate different environments and can also be utilized in GNSS-denied environments. Through extensive simulations and test fields, we evaluate the accuracy of the system and compare it to GNSS in urban environments where multipath transmission degrades accuracy. For completeness, we include visual-inertial odometry in the experiments and compare the performance with the UWB-based cooperative localization.

Ultra-wideband (UWB) technology is a mature technology that contested other wireless technologies in the advent of the IoT but did not achieve the same levels of widespread adoption. In recent years, however, with its potential as a wireless ranging and localization solution, it has regained momentum. Within the robotics field, UWB positioning systems are being increasingly adopted for localizing autonomous ground or aerial robots. In the Industrial IoT (IIoT) domain, its potential for ad-hoc networking and simultaneous positioning is also being explored. This survey overviews the state-of-the-art in UWB networking and localization for robotic and autonomous systems. We also cover novel techniques focusing on more scalable systems, collaborative approaches to localization, ad-hoc networking, and solutions involving machine learning to improve accuracy. This is, to the best of our knowledge, the first survey to put together the robotics and IIoT perspectives and to emphasize novel ranging and positioning modalities. We complete the survey with a discussion on current trends and open research problems., Comment: 2021 10th Mediterranean Conference on Embedded Computing, Track: CPS&IoT

Current research directions in deep reinforcement learning include bridging the simulation-reality gap, improving sample efficiency of experiences in distributed multi-agent reinforcement learning, together with the development of robust methods against adversarial agents in distributed learning, among many others. In this work, we are particularly interested in analyzing how multi-agent reinforcement learning can bridge the gap to reality in distributed multi-robot systems where the operation of the different robots is not necessarily homogeneous. These variations can happen due to sensing mismatches, inherent errors in terms of calibration of the mechanical joints, or simple differences in accuracy. While our results are simulation-based, we introduce the effect of sensing, calibration, and accuracy mismatches in distributed reinforcement learning with proximal policy optimization (PPO). We discuss on how both the different types of perturbances and how the number of agents experiencing those perturbances affect the collaborative learning effort. The simulations are carried out using a Kuka arm model in the Bullet physics engine. This is, to the best of our knowledge, the first work exploring the limitations of PPO in multi-robot systems when considering that different robots might be exposed to different environments where their sensors or actuators have induced errors. With the conclusions of this work, we set the initial point for future work on designing and developing methods to achieve robust reinforcement learning on the presence of real-world perturbances that might differ within a multi-robot system., Accepted to the 5th International Conference on Robotics and Automation Engineering, IEEE, 2020

Unmanned Aerial Vehicles (UAVs) have been playing an increasingly active role in supporting search and rescue (SAR) operations in recent years. The benefits are multiple such as enhanced situational awareness, status assessment, or mapping of the operational area through aerial imagery. Most of these application scenarios require the UAVs to cover a certain area. If the objective is to detect people or other objects, or analyze in detail the area, then there is a trade-off between speed (higher altitude coverage) and perception accuracy (lower altitude). An optimal point in between requires active perception on-board the UAV to dynamically adjust the flight altitude and path planning. As an initial step towards active vision in UAV search in maritime SAR scenarios, in this paper we focus on analyzing how the flight altitude affects the accuracy of object detection algorithms. In particular, we quantify what are the probabilities for false negatives and false positives in human detection at different altitudes. Our results define the correlation between the altitude and the ability of UAVs to effectively detect people in the water. acceptedVersion

Mobile communication base stations are the basic facilities of telecommunication operation networks. When the communication base station is struck by lightning, a very high overvoltage will appear on the top of pole, which leads the power supply cable of RRU(Remote Radio Unit) directly break through, and a large lightning current will flow through the cable, damaging the communication equipment and the power supply. Therefore, the research on the lightning current distribution characteristics of the mobile communication base station has important theoretical significance and engineering application value for the lightning protection of the mobile communication base station. In this paper, a simulation model of the current distribution characteristics of the communication pole system with overvoltage protective varistor is established, and the influence of the impulse current, the length of the pole and the DC reference voltage on the current distribution characteristics of the pole system is studied. The results show that: (1) When $8 /20\ \mu \mathrm{s}$ impulse current is injected into the 10m long pole system, the dynamic resistance $r$ of varistor decreases with the increase of impulse current peak value, the current distribution ratio of pole and core of power cable gradually approaches to 9:1. (2) As the length of the pole increases, the influence of the varistor on the current waveform parameters of the core branch of the power gradually decreases, and the front time distortion rate through the power line core branch decreases. (3) When the peak value of the impulse current and the length of the pole are the same, the lower the DC reference voltage of the varistor, the smaller the current waveform distortion of the core branch of the power, and the greater the current distribution.

Autonomous small robots are getting a lot of attention for their use in industrial and domestic purposes. These are equipped with few to numerous amounts of sensors to help understand the operating environment, take appropriate next-step decisions, prioritize actions and operate with little to no human intervention. Light detection and ranging (LIDAR) sensors are widely used for detecting objects around an autonomous robot and to drive by effectively avoiding any possible obstacle. However, cheap LIDARs available in the market mostly can measure the distance of surrounding objects with a rotating LASER beam and an accompanying sensor, thus obtaining a two-dimensional (2D) map. This is insufficient for extracting enough contextual data for autonomous robot movement. In this paper, we propose a novel approach for enhancing the detection capabilities of a typical 2D LIDAR to yield perception in three dimensions (3D). This can facilitate more efficient object detection while keeping the costs of small-robots to an affordable level. The proposed approach requires little customization to achieve reasonably enhanced mapping. From our experiments, it is evident that our proposed method provides better mapping and detection capabilities using commonly available low-cost 2D LIDAR. Furthermore, integration of Edge computing makes data processing efficient and noticeably increases battery life of a robot resulting in a longer run-time.

This conceptual paper discusses how different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge Computing (MEC) layer. We overview the state-of-the-art in MEC slicing, distributed robotic systems and blockchain technology to define a framework for services the MEC layer that will enhance the autonomous operations of connected robots and vehicles., Accepted to the Fifth International Conference on Fog and Mobile Edge Computing (FMEC 2020)

A recent trend in the IoT is to shift from traditional cloud-centric applications towards more distributed approaches embracing the fog and edge computing paradigms. In autonomous robots and vehicles, much research has been put into the potential of offloading computationally intensive tasks to cloud computing. Visual odometry is a common example, as realtime analysis of one or multiple video feeds requires significant on-board computation. If this operations are offloaded, then the on-board hardware can be simplified, and the battery life extended. In the case of self-driving cars, efficient offloading can significantly decrease the price of the hardware. Nonetheless, offloading to cloud computing compromises the system's latency and poses serious reliability issues. Visual odometry offloading requires streaming of video-feeds in real-time. In a multivehicle scenario, enabling efficient data compression without compromising performance can help save bandwidth and increase reliability.

The need of data compression at smart Edge/Fog-based gateways is undeniable as data compression can significantly reduce the amount of data that has to be transmitted over a network. This, in turn, has a direct impact on reducing transmission latency and increasing network bandwidth. In time-critical and data sensitive IoT applications such as healthcare, lossless data compression is preferable as compressed data can be recovered without losing any information. However, it is not an easy task to choose a proper lossless data compression algorithm for IoT applications as each lossless data compression method has its own advantages and disadvantages. This paper focuses on the analysis of lossless data compression algorithms run at smart Edge/Fog gateways. Widely used lossless data compression are run at different hardware which is often used as smart Fog/Edge gateways. The latency of data compression and compression rate in different cases of input data sizes are analyzed. The paper provides guidelines for choosing a proper lossless data compression algorithm for time-critical IoT applications.

Three-dimensional representations and maps are the key behind self-driving vehicles and many types of advanced autonomous robots. Localization and mapping algorithms can achieve much higher levels of accuracy with dense 3D point clouds. However, the cost of a multiple-channel three-dimensional lidar with a 360°field of view is at least ten times the cost of an equivalent single-channel two-dimensional lidar. Therefore, while 3D lidars have become an essential component of self-driving vehicles, their cost has limited their integration and penetration within smaller robots. We present an FPGA-based 3D lidar built with multiple inexpensive RPLidar A1 2D lidars, which are rotated via a servo motor and their signals combined with an FPGA board. A C++ package for the Robot Operating System (ROS) has been written, which publishes a 3D point cloud. The mapping of points from the two-dimensional lidar output to the three-dimensional point cloud is done at the FPGA level, as well as continuous calibration of the motor speed and lidar orientation based on a built-in landmark recognition. This inexpensive design opens a wider range of possibilities for lower-end and smaller autonomous robots, which can be able to produce three-dimensional world representations. We demonstrate the possibilities of our design by mapping different environments.

The development of autonomous vehicles has seen considerable advances over the past decade. However, specific challenges remain in the area of autonomous waterborne navigation. Two key aspects in autonomous surface vehicles are sensor calibration and segmentation of water surface. Cameras and other sensors in a car or drone can be installed accurately in a specific position and orientation. In a large vessel, this is not always possible, as sensors might be installed around the vessel or on masts. Taking advantage of the medium in which these vehicles operate, the water plane can be used as a reference for different sensors to calibrate their orientation. This allows more accurate localization of obstacles of objects. State-of-the-art deep learning techniques have been successfully applied for water surface segmentation in open sea. However, in other environments such as small rivers or lakes with still waters, a different approach might enable more accurate water surface estimation. We propose a method to estimate the water plane based on the detection of local symmetry planes that naturally occur when objects are reflected in the water. By using a point cloud generated with a stereo camera, we are able to accurately estimate the water level and, at the same time, calibrate the camera position and improve the localization of obstacles. We assume that an approximate position and orientation of the camera is known with respect to the sea level. We demonstrate the efficiency of our method with data obtained in the Aura river, Finland, with our prototype vessel facing both the riverside and the center of the river.

In order to reduce the effect of the loosely coupled transformer on the wireless power transfer system and achieve maximum power transmission of the system, the LCC compensated inductively coupled power transfer system based on the class-E inverter is proposed. According to the fundamental characteristics of the class-E amplifier and the LCC circuit, combined with the soft-switching operating conditions, the formula for calculating parameters of LCC compensation circuit is derived. By applying the constraint to the resistive component of the primary coil input impedance and the coupling coefficient, the optimum parameter value can be selected to ensure the system efficiency. Finally, the parameters simulation are supplied to verify the system transfer efficiency and load-carrying capacity. It shows that the zero-voltage turn off of the high-frequency inverter is realized and the overall ICPT system efficiency is improved.