Navigation Sensors Research Articles

Metrological characterization of new infrared sensors for robot navigation

UAV role in our society is increasingly important and the number of flying drones is exponentially growing. Safety related problems must be considered and the autonomous navigation is a grand challenge for engineers. This article reports on first experiences using state-of-the-art, time-of-flight, infrared sensors in outdoor environment. Speed and lightness make these sensors suitable for ground and especially for aerial robot navigation. Range, accuracy and precision of different modes and different sensors will be compared.

Enhance the Balance of Quadruped Robot using CMPS12

Quadruped is a robot that can move stably and flexibly on various types of surfaces. However, when quadruped encounters an uneven surface, it needs a good navigation system to get through it. The analysis of increasing the performance of the quadruped robot is based on the addition of the CMPS12 sensor for navigation. The CMPS12 sensor is used to measure the direction of the robot's orientation. Tests were carried out on four types of obstacles, namely broken road obstacles, sloping road obstacles, rocky road obstacles, and muddy road obstacles. The results of testing the robot on broken road obstacles obtained a maximum slope for the pitch axis of 13◦ forward, −21◦ to the rear and for the roll axis at a slope of 24◦ to the left and −19◦ to the right. On inclined road obstacles, the robot can pass through obstacles with an average travel time of 8.07 seconds with a maximum slope of 25◦ on the pitch axis. Then, on the rocky road obstacle, the robot can pass the obstacle with an average travel time of 8.13 seconds, with a maximum slope of 9◦ on the pitch axis and 8◦ on the roll axis. Then, on a muddy road obstacle, the robot can pass the obstacle with an average travel time of 11.67 seconds, with a maximum slope of 15◦ on the pitch axis and −6◦ on the roll axis.

Autonomous Design of a Green Sea-Cleaner Boat

This paper addresses the urgent global issue of marine pollution, with a focus on the inadequacies of traditional cleanup methods and the promising potential of an autonomous green sea-cleaner boat. The study presents a sea-cleaner boat design with reduced wake-wash, integrated with an autonomous system using visual equipment and LiDAR sensors for precise navigation and debris identification. Parametric studies of catamaran hull configurations show that the Flat-Outside Model exhibits the lowest wave elevation, indicating reduced hydrodynamic force and wake-wash, making it an environmentally preferable option. Additionally, the sea-cleaner boat employs LiDAR for obstacle detection and a camera with a Convolutional Neural Network for efficient debris identification and collection, enhancing operational safety and efficiency. Overall, the autonomous green sea-cleaner boat represents a significant advancement in maritime environmental conservation.

ЭКСПЕРИМЕНТАЛЬНАЯ ОЦЕНКА ТОЧНОСТИ ПРОСТРАНСТВЕННОЙ СТАБИЛИЗАЦИИ КВАДРОКОПТЕРА DJI AIR 2S*

The paper proposes a method for assessing the actual accuracy of spatial stabilization of a DJI Air 2S quadcopter using its video camera and processing the resulting video sequences with a developed software package in the mode of hovering over a reference object. The software package was implemented using the OpenCV library in Python. To register deviations of stabilization systems from a given position, it is proposed to use a reference field with known geometric parameters, information about which is entered into the processing algorithm. The main task of the software package is to identify the boundaries of the reference field and measure the geometric sides in pixels which allows you to determine the center of the field and measure its deviation relative to the center of the image as well as determine the height of the unmanned aerial vehicle flight. In the work the height measurement algorithm was tested. This technique is used to evaluate the accuracy of quadcopter stabilization with various combinations of navigation sensors used for this purpose. A statistical analysis of the obtained results was carried out and conclusions were drawn about compliance with the characteristics of positioning accuracy declared by the manufacturer using optical obstacle avoidance systems and satellite navigation systems.

Proposal for Generation of a Pseudo GPS Signal Used in Indoor Mode for Navigation Testing of Small Autonomous Flying Robot

In addition to the inertial navigation sensors embedded in an autonomous flying robot, Global Navigation Satellite Systems are most often used, in particular, the GPS (Global Positioning System). This offer information with an accuracy that depends on the arrangement of a constellation of satellites. It is very often that a measurement with errors of several meters is delivered by this system. With the need for agile decisions of the autonomous flying robot during its mission, especially in the case of rescuing people during disasters, it is not allowed for uncertainty to reign over the decision of the flying robot. In order to improve the estimators implemented experimentally, a secure laboratory test environment is more preferable. Here, we propose a way that helps researchers to conduct indoor experiments with a pseudo GPS that offers circumstances similar to outdoor navigation. The results that will be discussed are obtained with the Vicon motion tracker laboratory at Aalborg University, Denmark.

Adversarial waypoint injection attacks on Maritime Autonomous Surface Ships (MASS) collision avoidance systems

ABSTRACT Autonomous navigation is currently subject to particular interest for naval and commercial vessels. In addition to the regulatory effort, several pieces of research tackled the development of new guidance laws, stable and robust control algorithms, methodologies to increase situational awareness, and collision avoidance algorithms. However, most of these systems blindly trust information from navigation sensors, which can malfunction or, even worse, have their data altered by cyber-attacks. This last scenario will be increasingly common shortly and represents a dangerous threat that future highly computerised ships must face. The proposed work shows an approach capable of hijacking a state-of-the-art collision avoidance algorithm route by feeding it with rogue data. The effects of such a cyber-attack are shown via an extensive simulation campaign, testing the methodology on three different COLREG scenarios: head-on, crossing, and overtaking. The study additionally highlights the susceptibility of forthcoming navigation systems to security breaches, which still needs to be addressed in the existing literature. According to the experimental findings, attackers can attract the autonomous ship guidance toward their objective at a mean distance of 133 m . Finally, suggestions on how to harden these systems against this family of attacks are provided in the conclusion.

FOREST FEATURE LIDAR SLAM (F2-LSLAM) AND INTEGRATED SCAN SIMULTANEOUS TRAJECTORY ENHANCEMENT AND MAPPING (IS2-TEAM) FOR ACCURATE FOREST INVENTORY USING BACKPACK SYSTEMS

Abstract. Under-canopy mapping is desired to derive critical forest biometrics, such as diameter at breast height (DBH), merchantable height, and debris volume. The main challenge of such under-canopy mapping is the intermittent access to the global navigation satellite system (GNSS) signal, which is crucial to deriving accurately georeferenced mapping products. In this study, we propose two frameworks – Forest Feature LiDAR Simultaneous Localization and Mapping (F2-LSLAM) and Integrated Scan Simultaneous Trajectory Enhancement and mapping (IS2-TEAM) – for 3D LiDAR unit mounted on backpack systems to achieve accurate forest inventory. In the F2-LSLAM strategy, ground/tree trunk features are extracted from individual LiDAR scans. On the other hand, when trajectory information provided by navigation sensors is available, these semantic features are derived from LiDAR points within several scans (i.e., integrated scan) for the IS2-TEAM strategy. Then, local/global least squares adjustment (LSA) using derived features is performed to register LiDAR scans to a common reference frame for both strategies. To evaluate the performance of the proposed strategies, three in-house developed backpack systems with varying specifications were used to collect data in complicated forest environments. Through the comparison with point clouds acquired by a commercial backpack LiDAR system, the proposed frameworks are capable of generating point clouds with satisfactory intra-dataset alignment quality (in the range of 2–4 cm) for all backpack systems in natural forest areas with relatively flat terrain. However, for more challenging areas with dense undergrowth vegetation and/or large height differences, F2-LSLAM framework cannot extract sufficient features, while IS2-TEAM still exhibits good performance.

Adaptive sensor management for UGV monitoring based on risk maps

Because of the recent advances in sensor technology, unmanned ground vehicles (UGVs) are equipped with various high-performance sensors to improve their mission performance. The energy consumption of the sensors is a critical issue because most UGVs operate on rechargeable batteries while conducting their missions. In particular, efficient sensing has become a crucial challenge due to the growing demand for advanced intelligent monitoring missions. However, while previous studies have focused on power consumption models for vehicle locomotion, they have not fully addressed the high energy consumption of sensors.This study proposes an adaptive sensor-management algorithm to address the critical issue of energy consumption by considering the monitoring and navigation sensors that are widely used in UGV monitoring. After characterizing the proposed adaptive sensor-management strategy based on a risk map composed of monitoring and saving zones, we present a power consumption model to quantify the energy savings of the sensors. Furthermore, the optimal interval was derived to minimize power consumption during UGV monitoring. The proposed algorithm can minimize the energy consumption by adjusting the optimal interval according to the mission environment. We demonstrate the results of our analysis through various evaluations.

STRATEGY ON HIGH-DEFINITION POINT CLOUD MAP CREATION FOR AUTONOMOUS DRIVING IN HIGHWAY ENVIRONMENTS

Abstract. In recent years, a lot of researchers have been trying the development of efficient ways to create HD maps with centimeter-level precision. Mobile mapping system (MMS) produce 3D HD point cloud map of the surrounding by integrating navigation (i.e., direct georeferencing or DG process) and high-resolution imaging sensor data. Unfortunately, in partially environments, the provided accuracy of the GNSS system degrades dramatically. In order to constraint the drift and correct the georeferenced point cloud map, ground control points (GCPs) are placed along the road. Moreover, there are approaches which use laser-based point cloud registration techniques to construct the point cloud map. However, all promising mapping techniques which currently use the laser as the core sensor for mapping the high-definition point cloud map may not be promised to construct the point cloud map in partially or unfriendly environments. As a literature review and result, a suitable approach for creating the promising point cloud map is to combine the INS/GNSS navigation solution, LiDAR matching techniques, and GCPs. Thus, this study introduces the HD point cloud map generation method that can potentially help researchers create personalized and globalized HD point cloud maps and develop new HD point cloud map generation methodologies.

RESEARCH AND IMPROVEMENT OF THE IMAGE PROCESSING PROCESS OF THE NAVIGATION SYSTEM OF AGRICULTURAL MACHINERY TAKING INTO ACCOUNT CONDITIONS OF LIMITED VISIBILITY

Modern image processing technologies are already widely used in agriculture. Tractors can be equipped with GPS receivers, cameras and sensors for navigation, measuring soil moisture, controlling the application of resources (such as fertilizers and pesticides), and monitoring plant growth. Identification and analysis of the main shortcomings of existing methods of pre-processing and selection of image contours and is of particular interest in the field of processing and improving the quality of digital images in navigation systems, such as: automatic driving systems (Auto Guidance Systems) The relevance of improving the process of image processing in navigation systems of agricultural machines in conditions of limited visibility is important for several reasons: - effective image processing allows agricultural machines to automatically determine their location and perform navigation tasks even in difficult weather conditions when visibility is limited. - accurate image processing can help avoid errors related to navigation, which can be critical in growing crops. - with accurate image processing, agricultural machines can precisely determine the places where resources such as fertilizers or plant protection products need to be applied, allowing to optimize their use and reduce costs. - conditions of limited visibility, such as fog, rain or high humidity, may affect visibility for operators. Modern image processing technologies are already widely used in agriculture. Tractors can be equipped with GPS receivers, cameras and sensors for navigation, measuring soil moisture, controlling the application of resources (such as fertilizers and pesticides), and monitoring plant growth. Taking into account these factors, the development and improvement of image processing technologies in the navigation systems of agricultural machines remains an important topic of research and development in the field of agriculture.

Navigation Sensor Data Reliability Model-Based on Self-Evaluation and Mutual Evaluation

Navigation Sensor Data Reliability Model-Based on Self-Evaluation and Mutual Evaluation

Distributed Range-Only Localization That Preserves Sensor and Navigator Privacies

Distributed state estimation and localisation methods have become increasingly popular with the rise of ubiquitous computing, and have led naturally to an increased concern regarding data and estimation privacy. Traditional distributed sensor navigation methods typically involve the leakage of sensor or navigator information by communicating measurements or estimates and thus do not preserve participants' privacy. The existing approaches that do provide such guarantees fail to address sensor and navigator privacy in the common application of model-based range-only localisation, consequently forfeiting broad applicability. In this work, we define a notion of privacy-preserving linear combination aggregation and use it to derive a modified Extended Kalman Filter using range measurements such that navigator location, sensors' locations, and sensors' measurements are kept private during navigation. Additionally, a formal cryptographic backing is presented to guarantee our method's privacy as well as an implementation to evaluate its performance. The novel, provably secure, range-based localisation method has applications in a variety of environments where sensors may not be trusted or estimates are considered sensitive, such as autonomous vehicle localisation or air traffic navigation.

Consistent mapping of marine structures with an autonomous surface vehicle using motion compensation and submap-based filtering

Inspection and maintenance of marine energy farms are becoming increasingly important as more farms are developed and constructed. In this study, structural monitoring system using an autonomous surface vehicle (ASV) has been proposed to enable the inspection of an offshore power plant under harsh ocean disturbances. In particular, the ASV uses guidance and control laws to minimize the deviation from the desired path in waypoint tracking. The ASV was also equipped with multi-modal sensors – sonar and light detection and ranging (LiDAR) – for mapping underwater and surface structures. By utilizing the navigation sensors, the motion of the ASV in ocean disturbances was accurately estimated and incorporated into the mapping process to produce consistent multi-modal maps. Furthermore, dedicated outlier removal methods were proposed to improve the performance over conventional approaches in real offshore data. We considered the sparsity of the sonar and LiDAR data in their respective modality during acquisition and designed novel submap-based filtering to remove the outliers. To validate the proposed system, a field test was conducted on an offshore wave farm structure. It was verified that the ASV can cope with ocean disturbances in its waypoint tracking, and the resulting multi-modal maps are consistent and outlier-free.

Field experiment of autonomous ship navigation in canal and surrounding nearshore environments

AbstractIn this paper, we present the development of autonomous navigation capabilities for small cruise boats, and their verification by field experiments in a canal and its surrounding waters. A cruise boat was converted to an autonomous surface vehicle (ASV) by installing various sensors and actuators to enable autonomous navigation. Navigation and perception sensors, such as global positioning system, attitude and heading reference system, radar, light detection and ranging (LiDAR), and cameras, were mounted on the ASV to estimate its motion and perceive the surrounding environment. Motors and potentiometers were installed for active control of the ASV. Software system components including navigation filters, object‐detection, path‐planning, and control algorithms were designed and implemented. In the narrow canal region, LiDARs were used to detect the side walls and boundaries of the canal. In open areas outside the canal, obstacles and object features were detected using various combinations of onboard sensors. A model‐based path‐planning algorithm was designed to avoid the detected obstacles, and the line‐of‐sight guidance was employed to control the vehicle. The performance of the developed system was verified through a field experiment in a real‐world maritime environment.

High Temperature Inertial Navigation Sensors, Electronics and Packaging for reliable 300°C Operation

Conventional oil and gas drilling measurement while drilling (MWD) systems are typically designed to operate at temperatures <200°C. To increase the efficiency of drilling unconventional geothermal wells, downhole MWD tools are needed that can operate in the unique thermal challenges encountered in enhanced geothermal systems (EGS). GE demonstrated a 300°C downhole navigation system. The system utilizes GE’s internally developed state-of-the-art MEMS multiple ring Gyroscope (MRG), and a high temperature gyroscope control and readout application specific integrated circuit (ASIC). The MRG, ASIC and associated high temperature, high reliability packaging were integrated to demonstrate the performance and functionality of the gyroscope across the temperature range from room temperature to 300°C. Specialized long-term capable high temperature test platforms were developed to enable the characterization and testing of the gyroscope system and utilized to validate application-relevant lifetime capability.

A Customisable Data Acquisition System for Open-Source Hyperspectral Imaging.

Hyperspectral imagers, or imaging spectrometers, are used in many remote sensing environmental studies in fields such as agriculture, forestry, geology, and hydrology. In recent years, compact hyperspectral imagers were developed using commercial-off-the-shelf components, but there are not yet any off-the-shelf data acquisition systems on the market to deploy them. The lack of a self-contained data acquisition system with navigation sensors is a challenge that needs to be overcome to successfully deploy these sensors on remote platforms such as drones and aircraft. Our work is the first successful attempt to deploy an entirely open-source system that is able to collect hyperspectral and navigation data concurrently for direct georeferencing. In this paper, we describe a low-cost, lightweight, and deployable data acquisition device for the open-source hyperspectral imager (OpenHSI). We utilised commercial-off-the-shelf hardware and open-source software to create a compact data acquisition device that can be easily transported and deployed. The device includes a microcontroller and a custom-designed PCB board to interface with ancillary sensors and a Raspberry Pi 4B/NVIDIA Jetson. We demonstrated our data acquisition system on a Matrice M600 drone at a beach in Sydney, Australia, collecting timestamped hyperspectral, navigation, and orientation data in parallel. Using the navigation and orientation data, the hyperspectral data were georeferenced. While the entire system including the pushbroom hyperspectral imager and housing weighed 735 g, it was designed to be easy to assemble and modify. This low-cost, customisable, deployable data acquisition system provides a cost-effective solution for the remote sensing of hyperspectral data for everyone.

Underwater Wireless Sensor Network-Based Delaunay Triangulation (UWSN-DT) Algorithm for Sonar Map Fusion

Abstract Robust and fast image recognition and matching is an important task in the underwater domain. The primary focus of this work is on extracting subsea features with sonar sensor for further Autonomous Underwater Vehicle navigation, such as the robotic localization and landmark mapping applications. With the assistance of high-resolution underwater features in the Side Scan Sonar (SSS) images, an efficient feature detector and descriptor, Speeded Up Robust Feature, is employed to seabed sonar image fusion task. In order to solve the nonlinear intensity difference problem in SSS images, the main novelty of this work is the proposed Underwater Wireless Sensor Network-based Delaunay Triangulation (UWSN-DT) algorithm for improving the performances of sonar map fusion accuracy with low computational complexity, in which the wireless nodes are considered as underwater feature points, since nodes could provide sufficiently useful information for the underwater map fusion, such as the location. In the simulated experiments, it shows that the presented UWSN-DT approach works efficiently and robustly, especially for the subsea environments where there are few distinguishable feature points.

Integrated robust navigation and guidance for the kinetic impact of near-earth asteroids based on deep reinforcement learning

The defense against near-Earth asteroids (NEAs) using kinetic impact is faced with various challenges, including limited maneuverability of the impactor, inaccurate dynamic models, poor observability of relative navigation, and control execution errors. To address these challenges, an integrated robust navigation and guidance method for the kinetic impact of NEAs based on deep reinforcement learning (DRL) is proposed in this paper, which can directly map angle measurements by a monocular camera to guidance maneuvers. Firstly, a discrete partial observable Markov decision process (POMDP) is modeled for the integrated navigation and guidance problem of NEA interception. To address the situation where impactors are usually only equipped with aiming cameras and only the line of sight (LOS) angle is measured, past and current LOS measurements are concatenated into a one-dimensional state observation vector to directly introduce the memory of historical state information. A shaping reward function design based on potential energy has also been proposed to solve the problem of sparse reward as the main goal. Subsequently, proximal policy optimization (PPO) is used to solve the established POMDP model, obtaining an integrated navigation and guidance policy that directly maps from the original output of the navigation sensor to the guidance command. The potentially hazard asteroid (PHA) Bennu is used as the target and a typical kinetic impact defense scenario is designed to simulate and verify the model and method proposed in this paper, considering the influence of multiple factors. Numerical simulation results show that the proposed method achieves an interception accuracy of 203.58 m (mean value). The proposed method abandons the traditional separate design of navigation and guidance algorithms, and its robustness has been tested and verified in a wide range of uncertain environments.

Athermalization of Optical System with Large Field of View for Image Navigation

Image navigation sensor is one of the most important sensing means for human to explore the extraterrestrial space environment, and optical system is a key component of image navigation sensor, directly determines the amount of information perceived. The optical system requirements of image navigation sensors are gradually developing towards large field of view and miniaturization. The fisheye lens is the first choice for large field of view imaging by selecting the appropriate optical structure, without adding other additional components, and has the advantages of small size and strong perception energy. However, as the image surface illuminance of fisheye lens will decrease sharply with the increase of the field of view, the change of temperature will also degrade the imaging quality of the system, and strong stray light sources such as the sun will easily enter the field of view, which will greatly affect the perception ability of the system. Because of this, effective design of these systems is quite difficult. In this paper, a reverse-telephoto structure was used for large-field imaging, and the degradation of imaging quality caused by temperature changes was eliminated by selecting appropriate optical and mechanical materials. Designed with a focal length of 6.7mm, an F number of 3.6, a viewing angle of 130°, a working band of 0.45μm to 0.65μm, an after work distance of 7mm, a total system length≤60mm, we tested our system in a -40°C to +60°C temperature range and measured the image quality. Through simulation analysis, the system in the whole field of view within our spot diagrams was≤5.5μm, optical transfer function was greater than 0.2 at 90lp/mm, and no vignetting occurred. Furthermore, the imaging illumination of the edge field of view was 0.72 times that of the center field of view, the f – tan (θ) relative distortion value of the center field of view was≤2.3%, and the f – θ relative distortion value of the edge field of view was≤2.7%. Compared to normal temperatures, the optical transfer function changes at -20°C and +40°C were 0.02 and 0.01, respectively. Thus, the system displayed the characteristics of excellent image quality, small size, high energy utilization rate and good non-thermal performance.

AI-ACCIDENT DETECTION SYSTEM FOR FAST RESCUE ON FREEWAYS USING ON-BOARD CAR MOTION SENSORS FUSION

Continuous tracking and detecting position systems are significant services that can using in many applications such as detecting accident cars on Freeways. This paper introduces Artificial intelligent (AI) method to quick discover the accident and determine the position of car on Freeways that will reduce the rate of fatalities. Most tracking and detecting position systems are based on Global Navigation Satellite System (GNSS). But the GNSS signal in certain areas such as buildings and tunnel is unavailable which make a big problem for these systems. To solve this problem, most traditional methods are based on integrated GNSS with other navigation sensors such as accelerometers, gyroscopes, odometers and so on. But these integrated methods still high cost and need to long time processing. Today, most modern Vehicles are equipping with several On-board Car Motion Sensors (OVMS) such as Acceleration Sensors (AS), Wheel Velocity Sensor (WVS), Gravity Sensor (GS), Yaw Rate Sensor (YRS) and additional to GNSS that can improve the safety and also use to determine and detect the position of accident vehicle. This paper develops an approach AI-accident detection system based on integrating GNSS with On-board Vehicle Motion Sensors (OVMS) using Extended Kalman Filter (EKF) algorithm. During GPS outages, the three Acceleration Sensors (3-ASs) are processing to determine velocities and then position of vehicle on three x, y and z axes. While the YRS and GS sensors are using to determine velocities angles on three x, y and z axes, respectively. In same time the three WVS sensors are using to correct velocities errors of three acceleration sensors (3-ASs) until GNSS signal is return again. The proposed AI-accident detection system is tested in different scenarios during GPS outage signal. The results of proposed method can actually detect accident and determine the position of vehicle with high efficiency.