Autonomous Unmanned Systems Research Articles

An Explainable Data-Driven Optimization Method for Unmanned Autonomous System Performance Assessment

Unmanned autonomous systems (UASs), including drones and robotics, are widely employed across various fields. Despite significant advances in AI-enhanced intelligent systems, there remains a notable deficiency in the interpretability and comprehensive quantitative evaluation of these systems. The existing literature has primarily focused on constructing evaluation frameworks and methods, but has often overlooked the rationality and reliability of these methods. To address these challenges, this paper proposes an innovative optimization evaluation method for data-driven unmanned autonomous systems. By optimizing the weights of existing indicators based on data distribution characteristics, this method enhances the stability and reliability of assessment outcomes. Furthermore, interpretability techniques such as Local Interpretable Model-agnostic Explanations (LIMEs) and Partial Dependence Plots (PDPs) were employed to verify the effectiveness of the designed evaluation indicators, thereby ensuring the robustness of the evaluation system. The experimental results validated the effectiveness of the proposed approach.

Adaptive sensor fault tolerant control with prescribed performance for unmanned autonomous helicopter based on neural networks

PurposeThis paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.Design/methodology/approachTo ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.FindingsThe developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.Originality/valueThe tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

Research on the application of Artificial Intelligence

Currently, the field of artificial intelligence mainly includes key technologies such as computer vision, natural language processing, cross-media analytical reasoning, intellectually adaptive learning, group intelligence, autonomous unmanned systems, smart chips and brain-computer interfaces. These operations that can be completed with the assistance of artificial intelligence, with the help of artificial intelligence, life efficiency is greatly improved and the cost is greatly reduced. However, at present, Artificial Intelligence is only effective in the field of pointed and high-end research and learning, and there are still some thresholds in the application of daily life. In recent days, there have been a series of new technological inventions such as ChatGPT and a series of civilian Artificial Intelligence. Most of these new programs cannot be reasonably applied. This paper takes the application of Artificial Intelligence in the professional field as the background, and deeply explores the application of Artificial Intelligence technology in the non-professional field as exemplified by ChatGPT. This paper finds that in the medical field, Artificial Intelligence has played a considerable role, and Artificial Intelligence research on medical care is also in progress, so this paper makes predictions about the Artificial Intelligence that will appear in the medical industry based on the current situation, and investigates the understanding and use of Artificial Intelligence among teenagers through questionnaires, and analyzes the collected data.

Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network

Non-terrestrial network (NTN) is a trending topic in the field of communication, as it shows promise for scenarios in which terrestrial infrastructure is unavailable. Unmanned autonomous intelligent systems (UAISs), as a physical form of artificial intelligence (AI), have gained significant attention from academia and industry. These systems have various applications in autonomous driving, logistics, area surveillance, and medical services. With the rapid evolution of information and communication technology (ICT), 5G and beyond-5G communication have enabled numerous intelligent applications through the comprehensive utilization of advanced NTN communication technology and artificial intelligence. To meet the demands of complex tasks in remote or communication-challenged areas, there is an urgent need for reliable, ultra-low latency communication networks to enable unmanned autonomous intelligent systems for applications such as localization, navigation, perception, decision-making, and motion planning. However, in remote areas, reliable communication coverage is not available, which poses a significant challenge for intelligent systems applications. The rapid development of non-terrestrial networks (NTNs) communication has shed new light on intelligent applications that require ubiquitous network connections in space, air, ground, and sea. However, challenges arise when using NTN technology in unmanned autonomous intelligent systems. Our research examines the advancements and obstacles in academic research and industry applications of NTN technology concerning UAIS, which is supported by unmanned aerial vehicles (UAV) and other low-altitude platforms. Nevertheless, edge computing and cloud computing are crucial for unmanned autonomous intelligent systems, which also necessitate distributed computation architectures for computationally intensive tasks and massive data offloading. This paper presents a comprehensive analysis of the opportunities and challenges of unmanned autonomous intelligent systems in UAV NTN, along with NTN-based unmanned autonomous intelligent systems and their applications. A field trial case study is presented to demonstrate the application of NTN in UAIS.

A Practical Approach on the Design of an Interdisciplinary Postgraduate Program: The Case of the “Unmanned Autonomous and Remote-Control Systems” Postgraduate Program

There is a long-standing collaboration between the Department of Industrial Design and Production Engineering of the School of Engineering of the University of West Attica and the Department of Naval Studies of the Hellenic Naval Academy in the fields of research and applications. This collaboration has already led to the creation of a common interest direction in an existing Postgraduate Program. Naturally, the common interest in the subject of Unmanned Vehicles led to relevant discussions. These discussions matured quickly and led to a structured form and a specific goal: the design, production and operation of an interdisciplinary/joint Postgraduate Program. This paper presents the development process of the Curriculum for the Program and the basic assumptions about it that emerged from the combination of a review of the relevant literature and the identification of needs in the Greek and European markets.

Event-driven intelligent cooperative control of unmanned autonomous systems in complex environment

This paper addresses the event-driven cooperative tracking control problem of unmanned autonomous systems with unknown dead zones, unknown disturbances, and output constraints, which are induced by complex environment. The nonlinear functions of system model are estimated by using the neural networks. Furthermore, a state transformation technique is presented to solve the problem of the constrained control. Unlike most existing methods that only consider unilateral factor, the intelligent cooperative control method designed in this paper can deal with multiple factors in complex environment at the same time, and make the unmanned autonomous systems save more energy and improve endurance through event-driven mechanism. Detailed analysis is given based on Lyapunov stability theory which guarantees that the consensus tracking errors are uniformly ultimately bounded. Finally, results of a numerical example and a practical example of marine surface vehicles are shown to verify the effectiveness of the proposed intelligent control protocol.

DEVELOPMENT OF CONTROL LAWS OF UNMANNED AERIAL VEHICLES FOR PERFORMING GROUP FLIGHT AT THE STRAIGHT-LINE HORIZONTAL FLIGHT STAGE

The article proposes an improved approach to controlling groups of unmanned aerial vehicles (UAVs) aimed at increasing the overall efficiency and flexibility of the control process. The use of a heterogeneous external field, which varies both in magnitude and direction, allows achieving greater adaptability and accuracy in controlling a group of UAVs. A vector field for unmanned aerial vehicles determines the direction and intensity of the vehicles' movement in space. Such vector fields can be used to develop UAV control laws, including determining optimal flight paths, controlling speed, avoiding obstacles, and ensuring coordination of a group of UAVs. The subject of the study is the methods of controlling groups of autonomous UAVs, where each vehicle may have different speeds and flight directions. To solve this problem, various methods of using a heterogeneous field have been developed and proposed. Instead of using a homogeneous field that provides a constant flight speed, a vector field is used that adapts to different conditions and characteristics of the vehicles in the group. This method allows for effective group management, ensuring the necessary coordination and interaction between the vehicles. An analysis of recent research and publications in the field of autonomous system control indicates the feasibility of using machine learning, vector fields, and a large amount of data to successfully coordinate the movement of autonomous systems. These approaches make it possible to create efficient and reliable control systems. The aim of the study is to develop laws for controlling the movement of a group of autonomous unmanned aerial vehicles at the stage of straight-line horizontal flight based on natural analogues to improve the efficiency and reliability of their coordinated movement in different conditions. The main conclusions of the research are that the proposed method of controlling groups of UAVs based on a heterogeneous field can be implemented. It takes into account a variety of vehicle characteristics and environmental conditions that are typical for real-world use scenarios. This work opens up prospects for further improving the management of UAV groups and their use in various fields of activity. The article emphasises the relevance of technology development for autonomous unmanned systems, especially in the context of autonomous transport systems.

Role-Based Bayesian Decision Framework for Autonomous Unmanned Systems

Role-Based Bayesian Decision Framework for Autonomous Unmanned Systems

A survey of technologies supporting design of a multimodal interactive robot for military communication

PurposeThis paper presents a survey of research into interactive robotic systems for the purpose of identifying the state of the art capabilities as well as the extant gaps in this emerging field. Communication is multimodal. Multimodality is a representation of many modes chosen from rhetorical aspects for its communication potentials. The author seeks to define the available automation capabilities in communication using multimodalities that will support a proposed Interactive Robot System (IRS) as an AI mounted robotic platform to advance the speed and quality of military operational and tactical decision making.Design/methodology/approachThis review will begin by presenting key developments in the robotic interaction field with the objective of identifying essential technological developments that set conditions for robotic platforms to function autonomously. After surveying the key aspects in Human Robot Interaction (HRI), Unmanned Autonomous System (UAS), visualization, Virtual Environment (VE) and prediction, the paper then proceeds to describe the gaps in the application areas that will require extension and integration to enable the prototyping of the IRS. A brief examination of other work in HRI-related fields concludes with a recapitulation of the IRS challenge that will set conditions for future success.FindingsUsing insights from a balanced cross section of sources from the government, academic, and commercial entities that contribute to HRI a multimodal IRS in military communication is introduced. Multimodal IRS (MIRS) in military communication has yet to be deployed.Research limitations/implicationsMultimodal robotic interface for the MIRS is an interdisciplinary endeavour. This is not realistic that one can comprehend all expert and related knowledge and skills to design and develop such multimodal interactive robotic interface. In this brief preliminary survey, the author has discussed extant AI, robotics, NLP, CV, VDM, and VE applications that is directly related to multimodal interaction. Each mode of this multimodal communication is an active research area. Multimodal human/military robot communication is the ultimate goal of this research.Practical implicationsA multimodal autonomous robot in military communication using speech, images, gestures, VST and VE has yet to be deployed. Autonomous multimodal communication is expected to open wider possibilities for all armed forces. Given the density of the land domain, the army is in a position to exploit the opportunities for human–machine teaming (HMT) exposure. Naval and air forces will adopt platform specific suites for specially selected operators to integrate with and leverage this emerging technology. The possession of a flexible communications means that readily adapts to virtual training will enhance planning and mission rehearsals tremendously.Social implicationsInteraction, perception, cognition and visualization based multimodal communication system is yet missing. Options to communicate, express and convey information in HMT setting with multiple options, suggestions and recommendations will certainly enhance military communication, strength, engagement, security, cognition, perception as well as the ability to act confidently for a successful mission.Originality/valueThe objective is to develop a multimodal autonomous interactive robot for military communications. This survey reports the state of the art, what exists and what is missing, what can be done and possibilities of extension that support the military in maintaining effective communication using multimodalities. There are some separate ongoing progresses, such as in machine-enabled speech, image recognition, tracking, visualizations for situational awareness, and virtual environments. At this time, there is no integrated approach for multimodal human robot interaction that proposes a flexible and agile communication. The report briefly introduces the research proposal about multimodal interactive robot in military communication.

A High Performance Tubular Solid Oxide Fuel Cell for Unmanned Autonomous Systems

Small unmanned aerial systems (SUASs) have a great potential for numerous applications. The SUASs are light-weight, man-portable, and capable of delivering commercial products. Currently, small UASs are powered by lithium-ion batteries, however, flight endurance is limited and requires frequent charging battery due to low capacity and energy density. Numerous research and development efforts have focused on alternative power sources. These alternative power sources include technologies such as solar cells and hydrogen based polymer electrolyte membrane fuel cells. Additionally, polymer electrolyte membrane fuel cells still have the same technical constraints, the source of hydrogen, as it did many years ago. We have developed and examined the operational capabilities of propane fueled solid oxide fuel cell (SOFC) technology can power an sUAS. A self-sustaining miniature tubular SOFC stack provides a unique high power and high energy density for UAS. The SOFC has great potential for commercial and civilian applications, ranging from small scale systems (e.g. small unmanned air vehicle, wireless sensor networks) to large scale system (e.g. electric vehicle, wireless sensor networks, electric vehicles).

A Framework of Modeling and Simulation Based on Swarm Ontology for Autonomous Unmanned Systems

For the emerging autonomous swarm technology, from the perspective of systems science and Systems Engineering (SE), there must be novel methodologies and elements to aggregate multiple systems into a group, which distinguish the general components with specific functions. Here, we expect to provide a presentation of their existence in swarm development processes. The inspiration for our approach originates from the integration of swarm ontology, multiparadigm modeling, multiagent systems, cyber-physical systems, etc. Therefore, we chose the model-driven architecture as a framework to provide a method of model representation across the multiple levels of abstraction and composition. The autonomous strategic mechanism was defined and formed in parallel with Concept of Operations (ConOps) analysis and systems design, so as to effectively solve the cognitive problem of emergence caused by nonlinear causation among individual and whole behaviors. Our approach highlights the use of model-based processes and their artifacts in the swarm mechanism to integrate operational and functional models, which means connecting the macro- and micro-aspects in formalism to synthesize a whole with its expected goals, and then to verify and validate within an L-V-C simulation environment.

A Method for Generating Error-caused Scenarios

In recent years, with the development of artificial intelligence, Unmanned Autonomous Systems (UAS) have also developed rapidly. It is necessary to conduct sufficient testing and evaluation of UAS before using it. This paper proposes a method for generating error-caused scenarios that lead to incorrect behavior in UAS, and how to evaluate the level of incorrect behavior in UAS caused by the scenario. Based on the method proposed in this paper, error-caused scenarios can be generated in the simulation platform, and the dataset constructed from these scenarios can be input into the target detection model to verify the effectiveness of error-caused scenarios. Our experiments have shown that the scenarios generated by the method can easily lead to errors in UAS, and the method can play an important role in the field of model-based UAS testing and validation.

Introduction of Autonomous Combat Vehicles into the Czech Armed Forces

The article addresses the issue of the emergence of new technologies and their impact on the capabilities of the Czech Armed Forces (CAF). A summary of the recommendations is presented. In particular, there is a need for a conceptual approach to the integration of new systems in order to avoid spontaneous purchasing, which could be very logistically demanding. The very rapid development in the field of unmanned autonomous systems using elements of artificial intelligence offers opportunities to use them in support of the tasks of CAF. Due to the breadth of this issue, the scope is limited to the area of the Special Forces, which introduce these new sophisticated means into use throughout the Ministry of Defence of the Czech Republic.

Military Communications and Networks

Communications and networks are critical for collaboration and joint capabilities in military operations. Although technology and operational concepts evolve, the need for voice communication and command and control remains. At the same time, increasing use of unmanned autonomous systems and various types of sensors, potentially in high numbers, pose new and different requirements to networks and communications. Thus, communications and networks need to adapt to these new and changing requirements.

동적 전술 네트워크 환경에서 자율 무인체계를 위한 상호인증 기법

미래 전장환경의 주요 구성요소인 자율 무인체계들은 군집화되어 팀 단위로 활동하고 작전 목적 및 작전 상황에 따라 팀 구성은 매우 유동적일 것이다. 즉, 독립된 작전에 투입되어 운영되던 무인체계 팀들이 하나의 팀으로 통합되거나 하나의 무인체계 팀이 두 개 이상의 팀으로 분할될 수 있다. 새로운 팀으로 재편될 때마다 안전한 인증절차가 필요하다. 무인체계는 인간 전투원과 달리 종합적인 상황판단을 통한 인증이 제한되기 때문에 적 무인체계에 의한 기만적 활동에 취약할 수 있다. 본 논문은 미래 전장에서 안전한 무인체계 활용을 위한 경량화된 인증 기법을 제안한다. 제안하는 기법에서 무인체계는 개별 고유식별번호와 CRT(Chinese Remainder Theorem)를 통해 같은 팀에 소속된 무인체계들은 동일한 정수를 추출하고, 추출한 정수만큼 해시 연산을 반복하여 암호키를 개별적으로 생성한다. 그리고 수신된 암호화 메시지를 복호화함으로써 수신 메시지가 아군으로부터 수신된 것임을 인증한다. 제안하는 기법의 성능분석을 통해 제안하는 기법이 메시지 재전송 공격, 위장 공격, 메시지 변형 공격 등에 안전하고 기존 유사 기법에 비해 적은 연산량이 요구됨을 확인하였다.

Multi-USV Dynamic Navigation and Target Capture: A Guided Multi-Agent Reinforcement Learning Approach

Autonomous unmanned systems have become an attractive vehicle for a myriad of military and civilian applications. This can be partly attributed to their ability to bring payloads for utility, sensing, and other uses for various applications autonomously. However, a key challenge in realizing autonomous unmanned systems is the ability to perform complex group missions, which require coordination and collaboration among multiple platforms. This paper presents a cooperative navigating task approach that enables multiple unmanned surface vehicles (multi-USV) to autonomously capture a maneuvering target while avoiding both static and dynamic obstacles. The approach adopts a hybrid multi-agent deep reinforcement learning framework that leverages heuristic mechanisms to guide the group mission learning of the vehicles. Specifically, the proposed framework consists of two stages. In the first stage, navigation subgoal sets are generated based on expert knowledge, and a goal selection heuristic model based on the immune network model is used to select navigation targets during training. Next, the selected goals’ executions are learned using actor-critic proximal policy optimization. The simulation results with multi-USV target capture show that the proposed approach is capable of abstracting and guiding the unmanned vehicle group coordination learning and achieving a generally optimized mission execution.

Performance evaluation of solid NaBH4-based hydrogen generator for fuel-cell-powered unmanned autonomous systems

A solid NaBH4-based hydrogen generator was developed as a hydrogen source for fuel-cell-powered unmanned autonomous systems (UAS). Although many studies have been conducted to extract hydrogen from solid NaBH4, performance evaluation required for commercialization are still insufficient so that a new structure of the hydrogen generator is required to meet the performance requirement. For this purpose, in this study, a new reactor was made of a magnesium alloy with perfluoroalkoxy coating to improve its thermal conductivity and chemical resistance. In addition, a porous partition was installed inside the reactor to separate the hydrogen buffer volume from the reaction zone where NaBH4 granules was filled. Next, The operational characteristics of the developed hydrogen generator were evaluated in high- and low-temperature environments, inclined pitch and roll angles, unpredictable power cut-off situations, and sudden hydrogen demand, including the idle, rated, and maximum power modes. A start-up of the hydrogen generator was possible without additional heating and insulation at −15 °C and the hydrogen generation was stable after start-up for all pitch (±30°) and roll (±45°) angles, whereas the CoG shifted forward by 26% of the total length as the agent solution was consumed. In addition, the hydrogen supply immediately recovered within 30 s after an unpredictable power cut-off. Hydrogen purity was 99.64% on average and impurities originated from the water vapor. Finally, the dynamic response to the sudden hydrogen demand and precise controllability of the hydrogen supply rate were tested according to the simulated hydrogen consumption profile for practical use in fuel cells. In conclusion, the developed hydrogen generator satisfied the requirements for commercialization.

Integral Backstepping Sliding Mode Control for Unmanned Autonomous Helicopters Based on Neural Networks

In this paper, we propose an adaptive control approach to deal with the problems of input saturation, external disturbances, and uncertainty in the unmanned autonomous helicopter system. The dynamics of the system take into account the presence of input saturation, uncertainty, and external disturbances. Auxiliary systems are built to handle the input saturation. The neural networks are applied to approximate the uncertain terms. The control scheme combining integral backstepping and sliding mode control is developed in position and attitude subsystems, respectively. In the closed-loop system, the boundedness of the signals is proved by means of the Lyapunov theory. The simulation demonstrates that the approach has good robustness and tracking performance.

Network-based formation control of unmanned autonomous systems with directed topologies

The formation control of unmanned autonomous systems with nonlinear dynamics and directed topology is studied in this paper. By virtue of ultra-wide bandwidth (UWB) localisation system, the relative position between each follower and the neighbouring agents can be obtained. A formation control condition for unmanned autonomous systems is derived by appropriately constructing a Lyapunov function. Further, a formation controller is designed to form the formation of all autonomous agents and reach the consensus of velocities. Some parameters are designed to ensure the feasibility of formation controller. The effectiveness of the formation control protocol is verified by numerical simulations.

Integrated Task Sensing and Whole Body Control for Mobile Manipulation With Series Elastic Actuators

In this paper, an integrated framework consisting of the sensing, navigation and control is proposed for an autonomous mobile manipulator driven by series elastic actuators (SEAs) to preform mobile manipulation tasks in unknown environments. First, ORB-SLAM2 technique is combined into the environment sensing by extracting the ORB features, automatic initialization, repositioning and loop detection for real-time posture estimation. Then, the navigation function is designed for generating collision-free trajectory in an environment with obstacles. To realize kinematic and dynamic control of the mobile manipulator with the developed SEA joints, the whole body dynamics is considered and described. And to handle dynamic uncertainties and the SEA inherent saturation limits, a novel adaptive neural network control considering the whole body dynamics is proposed. Without knowing the exact parameters of the whole body model, the designed controller merely requires the position and velocity of the actuators and links, which can make the tracking errors converge to zero and keep all signals uniformly bounded in the closed-loop system. The performance and efficiency of the proposed method are verified by extensive experiments. Note to Practitioners—This paper is motivated by issues of manipulation control of autonomous unmanned system. Traditional manipulation frameworks focus either on sensing or control by assuming that the environment is known, which would result in lacking of autonomy for a specified task. Since mobile manipulation tasks often consist of nonholonomic and holonomic constraints for wheeled mobile manipulators with differential steering. In addition, most current works for whole body control are based on the condition that robot dynamic parameters are known beforehand. Therefore, it is necessary to establish an enhanced framework to simultaneously deal with these problems. In this paper, an integrated navigation and control framework is proposed. To make the mobile manipulator work in the unknown environment, task sensing and whole body control for mobile manipulators are also developed. The framework is partitioned into the task sensing, navigation and control, where the mobile manipulator can fulfill the mobile manipulation tasks in the unstructured environments.