Operation Of Unmanned Vehicles Research Articles

Enhanced High-Definition Video Transmission for Unmanned Driving in Mining Environments

In the development of intelligent mines, unmanned driving transportation has emerged as a key technology to reduce human involvement and enable unmanned operations. The operation of unmanned vehicles in mining environments relies on remote operation, which necessitates the low-latency transmission of high-definition video data across multiple channels for comprehensive monitoring and precise remote control. To address the challenges associated with unmanned driving in mines, we propose a comprehensive scheme that leverages the capabilities of 5G super uplink, edge collaborative computing, and advanced video transmission strategies. This approach utilizes dual-frequency bands, specifically 3.5 GHz and 2.1 GHz, within the 5G super uplink framework to establish an infrastructure designed for high-bandwidth and low-latency information transmission, crucial for real-time autonomous operations. To overcome limitations due to computational resources at terminal devices, our scheme incorporates task offloading and edge computing methodologies to effectively reduce latency and enhance decision-making speed for real-time autonomous activities. Additionally, to consolidate the benefits of low latency, we implement several video transmission strategies, such as optimized network usage, service-specific wireless channel identification, and dynamic frame allocation. An experimental evaluation demonstrates that our approach achieves an uplink peak rate of 418.5 Mbps with an average latency of 18.3 ms during the parallel transmission of seven channels of 4K video, meeting the stringent requirements for remote control of unmanned mining vehicles.

Study on Fault Diagnosis Technology for Efficient Swarm Control Operation of Unmanned Surface Vehicles

The purpose of this study is to design a Swarm Control algorithm for the effective mission performance of multiple unmanned surface vehicles (USVs) used for marine research purposes at sea. For this purpose, external force information was utilized for the control of multiple USV swarms using a lead–follow-formation technique. At this time, to efficiently control multiple USVs, the LSTM algorithm was used to learn ocean currents. Then, the predicted ocean currents were used to control USVs, and a study was conducted on behavioral-based control to manage USV formation. In this study, a control system model for several USVs, each equipped with two rear thrusters and a front lateral thruster, was designed. The LSTM algorithm was trained using historical ocean current data to predict the velocity of subsequent ocean currents. These predictions were subsequently utilized as system disturbances to adjust the controller’s thrust. To measure ocean currents at sea as each USV moves, velocity, azimuth, and position data (latitude, longitude) from the GPS units mounted on the USVs were utilized to determine the speed and direction of the hull’s movement. Furthermore, the flow rate was measured using a flow rate sensor on a small USV. The movement and position of the USV were regulated using an Artificial Neural Network-PID (ANN-PID) controller. Subsequently, this study involved a comparative analysis between the results obtained from the designed USV model and those simulated, encompassing the behavioral control rules of the USV swarm and the path traced by the actual USV swarm at sea. The effectiveness of the USV mathematical model and behavior control rules were verified. Through a comparison of the movement paths of the swarm USV with and without the disturbance learning algorithm and the ANN-PID control algorithm applied to the designed simulator, we analyzed the position error and maintenance performance of the swarm formation. Subsequently, we compared the application results.

Terrain-Vechicle Traction Control Parameters for Tracked-Robot

Abstract: Here we have the systems of :- 1) Advancements in Vehicle Control :- This summary highlights two recent advancements in vehicle control technology which are the enhanced Handling for Passenger Cars: Researchers have developed a novel control system inspired by expert driver techniques. This system optimizes a car's handling during maneuvers requiring simultaneous acceleration and sharp turns. Simulation testing demonstrates significant performance improvements. Improved Unmanned Ground Vehicle (UGV) Operation on Unstructured Terrain: A new approach addresses the challenges of controlling UGVs on rough terrain. The solution combines sensor fusion for terrain characterization, a neural network for terrain classification, and an adaptive control system for real-time stability adjustments. This multi-pronged approach has been validated through successful experimentation on a UGV platform. 2) Enhancing UGV Control with Haptic Feedback :- A separate study explores the use of haptic feedback to improve UGV control in difficult environments. Traditional operator interfaces often lack sufficient sensory information, leading to reduced situational awareness and operational difficulties. This research proposes a haptic tablet interface that provides the operator with a tactile sense of the UGV's traction state. User studies confirm that this approach significantly reduces the time a UGV spends immobilized due to traction loss. These advancements collectively represent significant progress in vehicle control technology, with potential benefits for both manned and unmanned vehicles operating in challenging scenarios

Collision avoidance control for limited perception unmanned surface vehicle swarm based on proximal policy optimization

In order to ensure the safe and coordinated operation of unmanned surface vehicle (USV) swarm in complex marine environments, the primary problem is collision avoidance control (CAC). However, the limited perception, environmental uncertainty and multi-source complexity bring significant challenges to the efficient collaboration and CAC of the USV swarm. To overcome the above challenges, this paper aims to propose a distributed CAC method for USVs based on the proximal policy optimization (PPO). This method does not necessitate precise system models and is capable of autonomous learning, effectively adapting to unknown environments. In terms of CAC, unlike designing reward functions based solely on the distance from obstacles, we additionally consider the velocity of obstacles, and combine optimal reciprocal collision avoidance (ORCA) to design a reward function. We further consider the limited perception range of USVs and construct a bidirectional gated recurrent unit (BiGRU) network to extract features of variable length observation data, effectively overcoming the problem of dimensionality in observable data. Moreover, we construct a high-quality USV swarm simulation environment using the Gazebo 3D physics engine, which is used for testing the generalization capability of collision avoidance policy. Finally, to verify the effectiveness of the policy learning and optimization, a series of experiments are conducted in various scenarios, network architectures, and control methods. The experimental results indicate that our approach has remarkable superiority in terms of travel time, average velocity, average reward, and success rate.

Внедрение и эксплуатация беспилотных автомобилей

The advantages of unmanned vehicles are noted. Eight technical, economic and political problems in the implementation and use of unmanned vehicles are identified. An important problem is the lack of a systematic approach to creating a system for the functioning of unmanned vehicles, which should consist of an unmanned vehicle subsystem, an unmanned vehicle infrastructure subsystem and an external environment subsystem. It is necessary to create a system for ensuring, managing and improving the quality of operation of unmanned vehicles based on international standards

Synthetic data formation for machine learning recognition of underwater objects

The main problem of machine learning for control systems of unmanned underwater vehicles is objectively very small samples of real data. The study aim was to develop an approach to the creation of synthetic data describing underwater objects for as the samples for training and validation in machine learning of control systems for auton-omous unmanned underwater vehicles. The subject of the study was a variety of underwater objects because real information about the shape, size and their external images is very limited. The data augmentation method was used, which makes it possible to obtain additional data on underdetermined objects of observation from the initial data while maintaining the classification features. Eight models have been developed that imitate the influence of various factors of the aquatic environment and allow using various augmentation methods (changing the position, adding noise, glare to the image, defocusing to create fuzziness; fragmentation, etc.) to obtain an almost unlimited number of images of any objects of man-made activity immersed in underwater environment, to varying degrees similar to the reference. Examples of the use of augmentation models that take into account changes in illumina-tion, transparency and the presence of an underwater landscape are given. Such synthetic (model) images may be the basis of a training set for machine learning to recognize and identify underwater objects. The trained model can be used as the basis of a decision support system for operators of remote-controlled unmanned underwater vehicles and as the basis for building control systems for autonomous uninhabited underwater vehicles for moni-toring underwater spaces.

Risk Management for the Safe Use of Highly Automated Vehicles in a Metropolis: Systems and Legal Analysis

The article presents the results of a scientific study of current issues of risk management associated with ensuring safe operation of unmanned vehicles, obtained by the authors as a result of implementation of the second stage of a scientific project supported by the Russian Science Foundation. The conclusions presented in the article are based on the results of a study of legal and organisational problems of ensuring the safe use of unmanned vehicles in a metropolis, carried out during the first stage of the project.The study confirmed the authors’ assumption that solving the problem of ensuring the safe use of unmanned vehicles is possible based on scientific research into the risks arising during their operation. It is substantiated that identification, differentiation, monitoring of risks determined by appearance of unmanned vehicles on highways is a necessary condition for development of a scientifically based system of legal measures of general and private prevention, including measures of legal liability for relevant offenses, based on categorisation of risks and threats of violation of mandatory requirements adopted to ensure transport safety and traffic management using this type of vehicle.The fundamental provisions are analysed and comprehended underlying the application of the risk­based approach in homogeneous and single-type social relations.

Multi-Traveler Salesman Problem for Unmanned Vehicles: Optimization through Improved Hopfield Neural Network

In response to the COVID-19 pandemic, communities utilize unmanned vehicles to minimize person-to-person contact and lower the risk of infection. This paper addresses the critical considerations of these unmanned vehicles’ maximum load capacity and service time, formulating them as constraints within a multi-traveling salesman problem (MTSP). We propose a comprehensive optimization approach that combines a genetic simulated annealing algorithm with clustering techniques and an improved Hopfield neural network (IHNN). First, the MTSP is decomposed into multiple independent TSPs using the fuzzy C-means clustering algorithm based on a genetic simulated annealing algorithm (SA-GA-FCM). Subsequently, the HNN is employed to introduce the data transformation technique and dynamic step factor to prepare more suitable inputs for the HNN training process to avoid the energy function from falling into local solutions, and the simulated annealing algorithm is introduced to solve multiple TSP separately. Finally, the effectiveness of the proposed algorithm is verified by small-scale and large-scale instances, and the results clearly demonstrate that each unmanned vehicle can meet the specified constraints and successfully complete all delivery tasks. Furthermore, to gauge the performance of our algorithm, we conducted ten simulation comparisons with other combinatorial optimization and heuristic algorithms. These comparisons indicate that IHNN outperforms the algorithms mentioned above regarding solution quality and efficiency and exhibits robustness against falling into local solutions. As presented in this paper, the solution to the unmanned vehicle traveling salesman problem facilitates contactless material distribution, reducing time and resource wastage while enhancing the efficiency of unmanned vehicle operations, which has profound implications for promoting low-carbon sustainable development, optimizing logistics efficiency, and mitigating the risk of pandemic spread.

An Observer-Based Adaptive Neural Network Finite-Time Tracking Control for Autonomous Underwater Vehicles via Command Filters

Due to the hostile marine environment, there will inevitably be unpredictable factors during the operation of unmanned underwater vehicles, including changes in ocean currents, hull dimensions, and velocity measurement uncertainties. An improved finite-time adaptive tracking control issue is considered for autonomous underwater vehicles (AUVs) with uncertain dynamics, unknown external disturbances, and unavailable speed information. A state observer is designed to estimate the position and velocity of the vehicle via a neural network (NN) approach. The NN is used to estimate uncertainties and external disturbances. A finite-time controller is designed via backstepping and command filter techniques. A multi-input multi-output (MIMO) filter for AUVs is established, and the corresponding MIMO filter compensation signal is constructed to eliminate the effect of filtering error. All the signals of the closed-loop system are proved to be finite-time bounded. An example with comparison is given to show the effectiveness of our method.

The Project of an Autonomous Microboat with a Laser Device for Estimation of Water Area Pollution by Microplastic

Water area pollution by microplastic particles smaller than 5 mm is a serious environmental problem. Usually, studies of water pollution are carried out by taking water samples with their further analysis in the laboratory. Therefore, study of the water areas for the presence of plastic with the identification of extensive pollution sites in real time is actual.The project of an autonomous unmanned research vehicle operating in water area and transferring the research data to the information processing center is discussed in the article. The device produces laser probing of the aquatic environment to detect polyethylene microparticles and to build maps of pollution sites in the studied water area.To implement the proposed project, the following tasks are solved:detection of plastic microparticles in the studied volume of the water environment where particle sizes are several orders of magnitude smaller than the studied water volume;identification of microparticles in presence of particles of other types of substances in the investigated water environment volume;estimation of the number particles’ in the studied water environmentA new approach which consists in creating an autonomous microboat that explores the water area along a route set from a control center located on the shore is proposed. The study involves the detection of microparticles in a certain volume of liquid flowing through a research tube installed in the bottom of the boat. There is a Schauberger tube in the central part of which particles are concentrated inside the tube. It makes possible to search for particles by laser probing with a wavelength about 3.5 μm, on which the peak absorption of light by polyethylene many times exceeds the water absorption.To ensure the energy efficiency of the microboat it is proposed to use a scheme for laser pulses combining using a fiber-optic delay line. This makes it possible to reduce the power consumption of the laser by almost an order of magnitude. The proposed project of the boat will make it possible to conduct studies of large water areas with the detection of plastic microparticles’ pollution sites. Examples of the used optical fiber, photodetector and laser source for the problem being solved are derived in the article.

Field demonstration of advanced autonomous navigation technique for a fully unmanned surface vehicle in complex coastal traffic areas

AbstractThis study introduces an advanced autonomous navigation algorithm for unmanned surface vehicle (USV) operations in complex coastal traffic areas. To facilitate the undertaking of tests for innovative technologies, the Korean government has designated regulation‐free special zones, thereby enabling field tests for USV demonstrations without an onboard safety person on real manned‐ship navigation routes. To enable real‐world USV operation without onboard safety person, an existing autonomous navigation algorithm is extended to achieve sufficiently reliable and robust USV operation. Accordingly, berthing and unberthing algorithms are newly developed for unmanned operation, and enhanced situational awareness algorithms are applied to detect all types of obstacles, including small‐sized floating objects spread over a wide range, such as a fish farm. In particular, the Korean intelligent maritime transportation service, called e‐Navigation, is incorporated into the autonomous navigation algorithms to overcome the limitations of vehicle autonomy based on onboard sensors. To demonstrate the capability of fully unmanned operation in real marine traffic scenarios, long‐distance navigation (54 km on a single voyage) was conducted by running several missions, including navigation in complex fish farm areas, maritime surveys, collision avoidance, navigation on real traffic routes, and emergency response. The extended navigation algorithms and results of the field tests are presented and discussed in this paper.

Parallel crosschecking neural network based fault-tolerant flight parameter estimation and faulty sensor identification

Reliable flight parameter estimation is crucial for the safe operation of unmanned air vehicles. Recent studies on flight parameter estimation based on distributed airflow sensors have shown promising results; however, how to extract reliable flight parameters and locate faults in case of sensor failures are not studied yet. This paper proposes a novel fault-tolerant flight parameter estimation method with capabilities to identify and isolate faulty sensors for a flight parameter estimation system based on distributed airflow sensors. First, the distributed sensors are grouped into subsets using combinations. Second, feedforward backpropagation neural networks of the same structure are designed and used by all subsets for flight parameter estimation. The crosschecking between these parallel networks improves the robustness of the estimation. In case of sensor failures, the sensor groups that contain any faulty sensors will result in wrong flight parameter estimations. Especially, a directional random sample consensus algorithm is developed and used to identify the subsets that contain faulty sensors. Then, the ratio of the faulty subsets is used to identify the number of faulty sensors. Finally, the faulty sensors will be identified according to the frequencies of their appearance in the subsets that contain faulty sensors. The effectiveness of the proposed method is validated by using wind tunnel tests. The results show that the proposed method can effectively provide reliable flight parameters and identify faulty sensors.

Demonstration of Spiral Wavefront Navigation on an Unmanned Underwater Vehicle

Experiments carried out at NUWC's Seneca Lake facility in the summer of 2020 demonstrate the efficacy of underwater navigation via a single spiral wavefront (SWF) acoustic beacon. An SWF beacon is a transducer array that has the capability of generating a spiral acoustic signal whose phase varies linearly with angular aspect along with a reference signal with constant phase. An unmanned underwater vehicle (UUV) operating in Seneca Lake under various channel conditions receives a regular signal from a fixed position single SWF beacon. The UUV's aspect relative to the beacon and its radial velocity can be determined by comparing the phase of the spiral signal to that of the reference signal. The SWF navigation solutions are compared to the UUVs inertial/GPS navigation. Advances in signal processing for the spiral wavefront navigation in multipath environments are detailed. An example of multipath performance from experimental data is shown.

Adaptive fault identification and reconfigurable fault‐tolerant control for unmanned surface vehicle with actuator magnitude and rate faults

AbstractThis study proposes a reconfigurable fault‐tolerant control method using an adaptive fault identification technique with application to an unmanned surface vehicle with consideration of environmental disturbances and system constraints, including both second‐order input operations (actuator magnitude and rate faults) and multi‐classification fault modeling features (lock‐in‐place or hard‐over, and loss of effectiveness). To begin with, unknown parameter compensator‐based observers with adaptive projection laws are designed for the respective double‐parameter and single‐parameter adaptive fault identification cases, and it is proved that the actual input estimation and fault parameter estimation errors are bounded. Then, double‐parameter and single‐parameter adaptive reconfigurable fault‐tolerant controllers are synthesized by combining the finite‐time baseline tracking control and terminal sliding‐mode mechanism to guarantee the second‐order dynamical tracking errors converge to the origin for the proper unmanned surface vehicle operation regardless of actuator magnitude and rate faults. Finally, simulation results and comparisons demonstrate the effectiveness and superiority of the proposed reconfigurable fault‐tolerant control algorithm.

Transformer-Based Dual-Channel Self-Attention for UUV Autonomous Collision Avoidance

Autonomous collision avoidance is a critical technology in intelligent control, which is of great significance for autonomous navigation and operation of Unmanned Underwater Vehicles (UUVs). To enhance the autonomy of UUV collision avoidance and improve its adaptability to unstable forward-looking sonar observation and uncertain environments, we propose a novel Transformer-based Dual-Channel Self-attention (TDCS) architecture for UUV collision avoidance. TDCS is a network architecture composed of two encoders and a decoder, which integrates dynamic/static obstacle recognition, obstacle motion prediction, collision risk assessment, and collision avoidance decision. The two encoders extract the observation features of different sensors in parallel, and this dual-channel structure avoids the mutual interference of input information. Moreover, the two encoders are beneficial for adaptive learning to focus on sensor information with varying forms of representation. The fusion layer integrates the bimodal features extracted from different sensors by the dual-channel encoder. Finally, the fusion features and historical decisions are recombined through the decoder to form a new decision.

An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle

Most of the existing studies developed and improved local path planning algorithms independently of global planning, i.e., ignoring the global optimal constrains. To meet the requirements of practical applications, this paper presented an energy-efficient hierarchical collision avoidance algorithm for unmanned surface vehicle operating in clustered dynamic environments. For the global level, genetic algorithm was modified by strategies of greedy-inspired population initialization, penalty-based multi-objective fitness function, and joint crossover. For the local level, velocity obstacle was combined with dynamic window approach to provide the kinematic constraints of the vehicle to its admissible velocities and simplified collision avoidance rules to guide the evasive maneuvers. Simulations showed that the proposed global algorithm was superior to three other algorithms in terms of path length, path smoothness, and convergence speed regardless of the environment size. The performance of the local algorithm was also verified for various encounter scenarios and speed ratios. In addition, the combination of the global and local planning can effectively solve the path optimization and dynamic obstacle avoidance in a designed offshore environment of fish cage culture.

The concept of sustainable transportation and unmanned railway technologies: the EAEU experience

The article attempts to critically assess the current regulatory framework in the field of unmanned technologies through the prism of the concept of sustainable transportation in the EAEU. The main trends in the development of unmanned technologies in transportation are identified. Using the example of the Russian railway transportation, the key technical and technological aspects of the operation of unmanned systems, which need a primary mechanism of regulation, are shown. The study shows that the effectiveness of high-level automation technologies directly depends on adequate normative and technical regulation and legal basis for ensuring safety issues and regulating responsibility at the interstate level. The authors consider it advisable to apply a test procedure for the use of unmanned technologies at the initial stage of operation, as well as to create “regulatory sandboxes” before the mass operation of unmanned vehicles. The development of international legal regulation of unmanned transport will become a driver for the realization of Russia’s transit potential, especially in the context of de-globalization trends in the world economy and anti-Russian sanctions.

An Improved Underwater Recognition Algorithm for Subsea X-Tree Key Components Based on Deep Transfer Learning

It is promising to detect or maintain subsea X-trees using a remote operated vehicle (ROV). In this article, an efficient recognition model for the subsea X-tree component is proposed to assist in the autonomous operation of unmanned underwater maintenance vehicles: an efficient network module, SX(subsea X-tree)-DCANet, is designed to replace the CSPBlock of YOLOv4-tiny with ResBlock-D and combine with the ECANet attention module. In addition, two-stage transform learning is used for the insufficiency of underwater target recognition samples as well as the overfitting caused by the subsea target recognition model, thereby providing an effective learning strategy for traditional subsea target recognition. A mosaic data augment algorithm and cosine annealing algorithm are also utilized for better accuracy of network training. The results of ablation studies show that the mean Average Precision (mAP) and speed of the improved algorithm are increased by 1.58% and 10.62%, respectively. Multiple field experiments on the laboratory, experimental pool, and the hydro-electric station prove that the recognition algorithm and training strategy present in this article can be well applied in subsea X-tree component recognition, and can effectively promote the development of intelligent subsea oil extraction projects.

THE PROBLEMS OF LEGAL REGULATION OF LEGAL LIABILITY FOR DAMAGE CAUSED BY THE OPERATION OF UNMANNED (AUTONOMOUS, HIGHLY AUTOMATED) VEHICLES

The world community is currently at the stage of rapid development of all types of technologies – information, industrial, social, each of which, ideally, should be designed to improve various spheres of public life. However, any of the technologies carries certain risks that society and the legislator should take into account. One of the technologies gaining increasing popularity in many developed countries of the world is autonomous driving technology, which poses new challenges to legislators of all countries, forces them to solve new problems arising in connection with their introduction into public life. Such problems may include the problem of liability for damage caused by activities related to the operation of unmanned (autonomous, highly automated) vehicles. The pace of development of new technologies is constantly increasing, and the legislator does not always keep up with the development of social relations. In this situation, science should help to detect the problem and identify possible solutions. The purpose of the study is to analyze the problems of legal regulation of legal liability for damage caused by the operation of unmanned (autonomous, highly automated) vehicles. Methods: to achieve this goal, special legal methods are used: formal legal, based on knowledge of special legal terminology, legal constructions, classifications of legal phenomena; the method of interpretation of law used in the analysis of normative legal acts and their projects. Results: the conclusion is made about the need for a more balanced approach when legislating legal liability for damage caused by the operation of unmanned (autonomous, highly automated) vehicles. The author provides a justification for the need to classify highly automated vehicles (hereinafter referred to as HAV) and activities related to their operation to sources of increased danger, by analyzing the specifics of HAV; reveals the features of the composition of such a civil law tort as causing harm by the operation of HAV; indicates the grounds for civil liability for damage caused by the operation of HAV; designates the conditions and features of legal liability in the area under consideration. The article proposes as a priority for the Russian Federation to choose a concept according to which the subjects of legal liability for damage caused by the operation of the unmanned vehicle are both its owner and maker (manufacturer), who must bear joint responsibility regardless of the presence or absence of guilt.

Hydrodynamic Performance of a Catamaran in Shallow Waters

The effects of limited water depth on the hydrodynamic performance of a catamaran with the full-scale dimensions and geometry of a WAM-V 16 unmanned surface vehicle operating in shallow waters are investigated using an incompressible URANS-VOF solver in OpenFOAM®. Simulations of the flow associated with the passage of the catamaran in shallow waters have been conducted for a range of vehicle speed and several shallow to intermediate water depths under free trim and sinkage conditions. The effects of water depth on the resistance and the dynamic motion of the catamaran are characterized. The total resistance coefficient of the catamaran is shown to increase by as much as over 40% at transcritical Froude numbers, close to the critical depth-dependent Froude number (Frh=1.0). The wave system associated with the flow is examined and its relationship to observed impacts on resistance, trim and sinkage are discussed. The effect of limited water depth on Kelvin’s wake angle is characterized in terms of both length and depth Froude numbers and is shown to be in good agreement with theory.