Group Of Unmanned Aerial Vehicles Research Articles

Exploiting Conventional MANET Routing in UAV’s Based Environment

Nowadays, Unmanned Aerial Vehicles (UAVs), have indeed revolutionized various industries and opened up new possibilities. Additionally, a Flying Ad hoc Network (FANET), a group of UAVs can perform specific functions like delivering goods, assisting people after a disaster, monitoring the weather and environment, among others. FANET is another kind of ad-hoc mobile network. It is capable of connecting with its environment and other nodes to obtain critical information. It is particularly difficult to route across FANETs because of how mobility they are and how fast they may change layouts. Therefore, routing algorithms are crucial for improving the performance of ad-hoc networks. This article will analyse the performance of the AODV, DSDV, and TORA routing protocols in the FANET environment. The NS-2 simulator has been used for testing parameters such as jitter, throughput, End-to-End delay, and packet delivery ratio (PDR). The simulation results indicate that TORA outperforms AODV and DSDV routing protocols across all performance metrics except End-to-End Delay.

THE ROUTES OF UNMANNED AERIAL VEHICLE PLANNING METHODS ONTOLOGY

The article analyzes modern methods of the route of unmanned aerial vehicles (UAVs) planning. An ontology for the routes of the UAVs group planning algorithms classification has been developed, which includes two elements: global planning and local planning. The article notes that the purpose of UAV flight routes planning is the optimal (rational) solution of specific tasks under appropriate weather conditions and taking into account other factors of the external environment. According to the premise of taking into account a significant number of constraints, the objective function of planning is to reach each UAV from the group to the end point (mission point). Planning the UAVs group route is a complex task of its multi-criteria optimization and solution in the presence of constraints. With the increase in the UAVs number, the analytical space of the route planning task will grow exponentially. The existing methods of planning the of the UAVs group movement route have a number of disadvantages, therefore, the development of a comprehensive method that reduces computational requirements, saves time, allows to plan routes in real time, and is also more effective from the group's mission performance point of view. The article defines the requirements for the UAVs group routes planning promising methods: complex modeling of many factors of the external environment influence; planning in real time; integration of planning methods. Combining the characteristics of heuristic algorithms and machine learning methods also allows you to obtain complex methods that have a higher adaptability to the conditions of the external environment and expand the range of applications.

AUTHENTICATION OF WIRELESS SYSTEMS BASED ON A DRONE SWARM AS A COMPONENT OF THE 5G RADIO ACCESS NETWORK ARCHITECTURE

Background. When establishing security measures for telecommunication networks involving unmanned aerial vehicles (UAVs), characteristics such as high scalability, device diversity, and high mobility should be considered. Authentication is a fundamental property that allows a UAV network to establish secure communication between its core components. Authentication also protects the UAV network from attackers impersonating legitimate UAVs. UAV authentication can further secure the communication channel by preventing impersonation and replay attacks. The design of UAV access control schemes, such as authorization and authentication mechanisms, remains a challenging research problem in UAV networks. The network becomes even more complicated when it interacts with a multitude of UAVs, called a swarm. A swarm by its very nature has a dynamic structure, and this raises the issue of unreliable constraints on UAVs in its composition. A standardized solution for the authentication of a single drone using the new 5G radio network (NR) is known, but for a swarm of drones, this is an open field of research. Per-UAV authentication key sharing as described in 5G NR does not scale across groups of UAVs. Objective. The purpose of this work is to conduct an analytical review and consider approaches to creating procedures for the authentication of a swarm of UAVs/drones with wireless equipment on board for the 5G NR network, taking into account the features of swarm formation and the very requirements for 5G authentication. Methods. Analysis of factors affecting the quality of provision of telecommunication services using UAVs in fifth generation networks. Analysis of well-known publications dedicated to the implementation of 5G networks and the use of drones in them. Comparing the implementation of UAV authentication procedures with on-board wireless equipment in the 5G network. Results. The widespread use of small UAVs, as well as the large expansion of wireless 5G networks, requires new security measures to prevent unauthorized access to sensitive data. Identification and Authentication for a mobile operator's network using drones allows for secure communication between its main components. This makes it possible to recognize the very drones that participate in the formation of such a network. Drone authentication often protects the communication channel by preventing replay attacks. The development of drone radio access control mechanisms, such as authorization and authentication mechanisms, remain relevant researches for the construction of promising radio access networks involving UAVs. It has been confirmed that the introduction of special group procedures for the authentication of a swarm of drones in the 5G network can significantly improve the quality of the provision of telecommunication services. Conclusions. When working with a swarm of drones, in addition to the usual problems with encryption and authentication (within the swarm and for communication between the swarm and the ground control station), there are additional problems related to the constant change in the composition of the swarm and its hovering position: drones can join or leave a swarm. Depending on the swarm management structure, a different method of authentication will be needed, which makes it difficult to unify such procedures for a swarm of drones. Authentication procedures for a swarm of drones in 5G can be implemented through the following approaches: individual authentication, when each drone as a member of the swarm undergoes authentication with one NR 5G ground station; you can consider such an approach as authentication of a group of IoT devices, if the traffic of the swarm is very limited; group authentication through a leader drone that communicates with swarm members and the 5G operator's network; group distributed authentication through edge drones. Group authentication via a drone leader is presented, where authentication is performed through a mechanism based on distributed delegation to reduce the service traffic directed to the 5G operator's core network. Here, legitimate drones are authorized as proxy delegated signers to perform authentication on behalf of the underlying network. Group distributed authentication through boundary drones is considered, which offers more solutions than the case of authentication through a leader drone. Here, a solution is possible for several cases at once, for example, authentication of new drones (entering the swarm or leaving the swarm) and merging two separate drone swarms.

Multi-UAV Cooperative Coverage Search for Various Regions Based on Differential Evolution Algorithm.

In recent years, remotely controlling an unmanned aerial vehicle (UAV) to perform coverage search missions has become increasingly popular due to the advantages of the UAV, such as small size, high maneuverability, and low cost. However, due to the distance limitations of the remote control and endurance of a UAV, a single UAV cannot effectively perform a search mission in various and complex regions. Thus, using a group of UAVs to deal with coverage search missions has become a research hotspot in the last decade. In this paper, a differential evolution (DE)-based multi-UAV cooperative coverage algorithm is proposed to deal with the coverage tasks in different regions. In the proposed algorithm, named DECSMU, the entire coverage process is divided into many coverage stages. Before each coverage stage, every UAV automatically plans its flight path based on DE. To obtain a promising flight trajectory for a UAV, a dynamic reward function is designed to evaluate the quality of the planned path in terms of the coverage rate and the energy consumption of the UAV. In each coverage stage, an information interaction between different UAVs is carried out through a communication network, and a distributed model predictive control is used to realize the collaborative coverage of multiple UAVs. The experimental results show that the strategy can achieve high coverage and a low energy consumption index under the constraints of collision avoidance. The favorable performance in DECSMU on different regions also demonstrate that it has outstanding stability and generality.

UAV group control protocol with adaptive consensus

SummaryIn this paper, a modified consensus protocol algorithm is proposed for controlling a group of identical unmanned aerial vehicles (UAVs), which have been subjected to interfering signals during coordinate information exchange, using an unknown parameter in the consensus protocol. The maximum levels of interfering signals in the proposed protocol were adjusted by incorporating a hysteresis function with a dead zone consistent with the initial coordinates and the interfering signal levels. An adaptation algorithm is proposed to address a priori uncertainty regarding the consensus parameters, involving the correction of an unknown parameter by eliminating control signals exhibiting false sign changes. This correction relies on the coordinates in the phase plane, indicating that the delay in maneuver execution occurs at the beginning of the maneuver. Furthermore, by modeling synchronized motion, UAV group consensus is demonstrated for an ideal case devoid of a priori uncertainty regarding control protocol parameters, interfering signals, or consequences. The convergence of the adaptation algorithm was assessed by defining a vector function to track parameter changes during tuning. The monotonically decreasing nature of the resulting curve, along with the finite duration of the tuning process, provides confirmation of the convergence of the adaptation algorithm.

Realization of Robust Formation for Multi-UAV Systems Using Control Barrier Functions

Network robustness is a necessary prerequisite for the effective execution of resilient control in distributed multiple unmanned aerial vehicles (UAVs). However, it remains a challenging task to construct a communication graph that satisfies specific network robustness properties. This paper investigates robust formation control of multi-UAV systems using control barrier functions (CBFs). We first propose a novel formation law to drive groups of UAVs into formations with communication graphs that satisfy the [Formula: see text]-robustness. With such a law, all the normal UAVs in the formation are capable of executing a given resilient consensus protocol, and achieving convergence in the presence of malicious attackers. Then, we present a control law that facilitates the establishment of UAV formations, in which the communication graphs satisfy p-fraction robustness. Finally, simulation examples are given to verify the effectiveness of the proposed formation control laws.

An evolutionary multi-agent reinforcement learning algorithm for multi-UAV air combat

In recent years, multi-agent reinforcement learning (MARL) has been widely used as a multi-UAV autonomous countermeasure. However, because training is usually performed in a self-play manner, the existing algorithms may suffer from strategic cycles without progress. Moreover, in complex air combat, the state dimensions change dynamically with the varying number of unmanned aerial vehicles (UAVs), making it difficult for existing MARL algorithms with fixed-input-size network architectures to effectively handle the variations. Therefore, we propose an evolutionary MARL method that can avoid cycles through the evolution of a population of UAV groups. In addition, an attention mechanism with death masking was adopted to handle dynamic changes in the number of UAVs. Experimental results conducted in our constructed multi-UAV air combat environment show that our method outperforms state-of-the-art methods and can help UAVs generate effective maneuver strategies to defeat opponents.

Distributed formation control of multi-UAV systems using relative information

An adaptive distributed control strategy is designed for the formation of multiple unmanned aerial vehicle (UAV) groups by using only relative measurement information. A distributed estimator is first designed to get the estimate of the consensus error. Then, an adaptive distributed controller is proposed to realize the formation tracking with the relative measurements. Based on the position and velocity information, a control barrier function (CBF) algorithm is applied to achieve the UAV formation under safe position constraints. In addition, in order to break up and reorganize multiple UAV groups, the proposed controller is designed based on the time-varying network topology. It is proved that the closed-loop system is uniformly asymptotically stability. Simulation and experiment results indicate that the proposed strategy can achieve formation control of multiple UAVs under safety constraints based on the measurement of relative measurement information.

Security Improvement of UAV Group IoT in Polygonal Area Based on Similarity Clustering Algorithm

Abstract Currently, unmanned aerial vehicle (UAV) group Internet of Things (IoT) has poor stability, poor endurance, and low security. In this regard, we report a polygon area UAV group IoT clustering algorithm for large-scale, high-speed mobile IoT environment. First, the UAV group IoT was divided into a polygon area and a few cluster heads were used to cover the polygon area uniformly. Then, the clustering index in the maximum speed similarity clustering algorithm was induced into the weighted clustering algorithm. Simultaneously, the link retention rate, relative node degree, and node residual energy ratio were improved to select the IoT node with the maximum weight as the cluster head. Finally, the IoT security was improved by reputation calculation. The results show that the clustering algorithm in this paper can reduce the number of clusters and lower the handover rate between clusters, improve the stability of clustering, prolong the survival time of the minimum node, improve the overall endurance of the IoT, and have high security.

UAV-Enabled Secure Communications via Collaborative Beamforming With Imperfect Eavesdropper Information

Unmanned aerial vehicles (UAVs) are playing a pivotal role in wireless networks due to their high mobility and on-demand deployment advantages. However, the UAV-enabled communications are susceptible to be wiretapped by eavesdroppers due to the strong line-of-sight (LoS) dominated air-ground channel. In this paper, we consider a UAV-enabled secure communication scenario, in which a group of UAVs form a UAV-enabled virtual antenna array (UVAA) to transmit information towards the remote base stations (BSs) via collaborative beamforming (CB), while multiple known and unknown eavesdroppers aiming to wiretap the information. Specifically, a secure communication multi-objective optimization problem (SCMOP) is formulated to achieve the maximization of the worst-case secrecy rate, the minimization of the maximum sidelobe level (SLL) as well as the minimization of the flight energy consumption of UAVs by obtaining optimal locations and excitation current weights concerning the UAVs as well as determining an optimal receiver BS that can achieve superior communication performance. To solve the formulated SCMOP which is demonstrated to be non-convex and NP-hard, an improved multi-objective salp swarm algorithm (IMSSA) with several specific operating factors is proposed. Simulations results demonstrate that the proposed IMSSA can deal with the formulated SCMOP effectively and outperforms other benchmark strategies. Moreover, the multi-hop relay is introduced to verify the reasonability of the UVAA system, and two benchmark schemes of the formulated SCMOP are introduced to demonstrate the necessity of the formulated SCMOP. In addition, the performance of the UVAA system under certain unexpected circumstances is estimated. Finally, experimental implementation is conducted by using a Raspberry Pi and the results demonstrate the practicality of the proposed CB-based secure communication approach in real-world scenarios.

Dynamic Screening Strategy Based on Feature Graphs for UAV Object and Group Re-Identification

In contemporary times, owing to the swift advancement of Unmanned Aerial Vehicles (UAVs), there is enormous potential for the use of UAVs to ensure public safety. Most research on capturing images by UAVs mainly focuses on object detection and tracking tasks, but few studies have focused on the UAV object re-identification task. In addition, in the real-world scenarios, objects frequently get together in groups. Therefore, re-identifying UAV objects and groups poses a significant challenge. In this paper, a novel dynamic screening strategy based on feature graphs framework is proposed for UAV object and group re-identification. Specifically, the graph-based feature matching module presented aims to enhance the transmission of group contextual information by using adjacent feature nodes. Additionally, a dynamic screening strategy designed attempts to prune the feature nodes that are not identified as the same group to reduce the impact of noise (other group members but not belonging to this group). Extensive experiments have been conducted on the Road Group, DukeMTMC Group and CUHK-SYSU-Group datasets to validate our framework, revealing superior performance compared to most methods. The Rank-1 on CUHK-SYSU-Group, Road Group and DukeMTMC Group datasets reaches 71.8%, 86.4% and 57.8%, respectively. Meanwhile, our method performance is explored on the UAV datasets of PRAI-1581 and Aerial Image, the infrared datasets of SYSU-MM01 and CM-Group and the NIR dataset of RBG-NIR Scene dataset; the unexpected findings demonstrate the robustness and wide applicability of our method.

ПОТЕНЦИАЛ ГЕНЕТИЧЕСКИХ АЛГОРИТМОВ В ЗАДАЧАХ ПОКРЫТИЯ ТЕРРИТОРИИ ГРУППОЙ БЛА

Unmanned aerial vehicles (UAVs) have become key tools in automated area coverage tasks due to the fact that they are not limited by obstacles on the ground and are able to quickly complete tasks. However, when planning routes for UAVs covering an area, difficulties arise in finding optimal solutions due to the computational complexity of the problem. Therefore, when solving problems with a group of UAVs over large areas, heuristic algorithms are used to find coverage paths that are close to optimal. one popular algorithm for that is the genetic algorithm. The article studies the potential of using genetic algorithms in the context of solving the problem of covering an area using a group of UAVs. The article provides a review of methods for developing and optimizing modified genetic algorithms that take into account the unique features of the coverage problem. The characteristics of the genetic algorithm and the representation of chromosomes when solving the coverage problem are analyzed, depending on the different types of representation of territories. The article considers features of applying genetic operations to chromosomes that reflect the trajectory of the UAV, as well as the features of the task when working with a group of UAVs. In addition, it also discusses collision avoidance techniques in UAV swarm missions, their advantages and limitations.

Network analysis of decentralized fault-tolerant UAV swarm coordination in critical missions

Unmanned aerial vehicles (UAVs) have gained prominence across various sectors for their versatile applications. While their advantages are evident, addressing concerns associated with their deployment is essential to ensure reliability. This study presents an innovative approach for coordinating a group of UAVs in aerial survey missions. The decentralized strategy presented in this article allow UAVs to self-organize into linear formation, optimize their coverage paths, and adapt to agent failures, thereby ensuring efficient and adaptive mission execution. The strategy has been tested and validated on two different platforms: the inter-UAV communication performance is evaluated on NS-3 simulator to measure metrices such as packet delivery ratio, throughput, delay, and routing overhead within the UAV swarms, while mission efficiency and fault tolerance is analyzed on robot operating system framework, and visualized on Gazebo simulator with real-time parameters. Through experimental results, we show that, after proper tuning of control parameters, the approach succeeds in flock formation with high level of fault tolerance, offering higher efficiency in terms of mission time, transmission delay, packet delivery rate, and control overhead, when compared to the benchmark approaches.

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.

Event-triggered consensus control method with communication faults for multi-UAV

This paper investigates the event-triggered consensus for a group of unmanned aerial vehicles (UAVs) with communication faults under the assumption that the position sensors of some individuals are damaged. The objective is to make the UAV group reach consensus in urgent tasks such as obstacle avoidance or evasion. Using the Lyapunov stability theory, sufficient conditions to achieve system consensus are given based on different velocity and position interaction topologies. Considering the limited capabilities of sensors and processors, an event-triggered consensus protocol is adopted to reduce the sampling frequency. Finally, simulation results illustrate the effectiveness of our approach.

Research on Scheme Design and Decision of Multiple Unmanned Aerial Vehicle Cooperation Anti-Submarine Based on Knowledge-Driven Soft Actor-Critic

To enhance the anti-submarine and search capabilities of multiple Unmanned Aerial Vehicle (UAV) groups in complex marine environments, this paper proposes a flexible action-evaluation algorithm known as Knowledge-Driven Soft Actor-Critic (KD-SAC), which can effectively interact with real-time environmental information. KD-SAC is a reinforcement learning algorithm that consists of two main components: UAV Group Search Knowledge Base (UGSKB) and path planning strategy. Firstly, based on the UGSKB, we establish a cooperation search framework that comprises three layers of information models: the data layer provides prior information and fundamental search rules to the system, the knowledge layer enriches search rules and database in continuous searching processes, and the decision layer utilizes above two layers of information models to enable autonomous decision-making by UAVs. Secondly, we propose a rule-based deductive inference return visit (RDIRV) strategy to enhance the knowledge base of search. The core concept of this strategy is to enable UAVs to learn from both successful and unsuccessful experiences, thereby enriching the search rules based on optimal decisions as exemplary cases. This approach can significantly enhance the learning performance of KD-SAC. The subsequent step involves designing an event-based UGSKB calling mechanism at the decision-making level, which calls a template based on the target and current motion. Finally, it uses a punishment function, and is then employed to achieve optimal decision-making for UAV actions and states. The feasibility and superiority of our proposed algorithm are demonstrated through experimental comparisons with alternative methods. The final results demonstrate that the proposed method achieves a success rate of 73.63% in multi-UAV flight path planning within complex environments, surpassing the other three algorithms by 17.27%, 29.88%, and 33.51%, respectively. In addition, the KD-SAC algorithm outperforms the other three algorithms in terms of synergy and average search reward.

Research on Efficient Multiagent Reinforcement Learning for Multiple UAVs’ Distributed Jamming Strategy

To support Unmanned Aerial Vehicle (UAV) joint electromagnetic countermeasure decisions in real time, coordinating multiple UAVs for efficiently jamming distributed hostile radar stations requires complex and highly flexible strategies. However, with the nature of the high complexity dimension and partial observation of the electromagnetic battleground, no such strategy can be generated by pre-coded software or decided by a human commander. In this paper, an initial effort is made to integrate multiagent reinforcement learning, which has been proven to be effective in game strategy generation, into the distributed airborne electromagnetic countermeasures domain. The key idea is to design a training simulator which close to a real electromagnetic countermeasure strategy game, so that we can easily collect huge valuable training data other than in the real battle ground which is sparse and far less than sufficient. In addition, this simulator is able to simulate all the necessary decision factors for multiple UAV coordination, so that multiagents can freely search for their optimal joint strategies with our improved Independent Proximal Policy Optimization (IPPO) learning algorithm which suits the game well. In the last part, a typical domain scenario is built to test, and the use case and experiment results manifest that the design is efficient in coordinating a group of UAVs equipped with lightweight jamming devices. Their coordination strategies are not only capable of handling given jamming tasks for the dynamic jamming of hostile radar stations but also beat expectations. The reinforcement learning algorithm can do some heuristic searches to help the group find the tactical vulnerabilities of the enemies and improve the multiple UAVs’ jamming performance.

MULTI-AGENT MODEL OF GROUP CONTROL POINT OF UNMANNED AIRCRAFT AND UNMANNED GROUND VEHICLE WHEN PERFORMING SPECIAL MISSIONS

Unmanned aerial vehicles (UAVs) are becoming very important platforms for conducting combat operations in the russian-ukrainian war, as they significantly reduce the risk of human life during special and combat missions. The UAV group can be used to perform a wide range of military tasks: destruction of enemy equipment, reconnaissance (monitoring) of the area, construction of aerial networks, etc.
 In the conditions of a change in the tactical situation, countermeasures by the enemy, effective management of the UAV group is impossible without an appropriate management system. The UAV group can include both unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UAVs). The following main tasks are assigned to the management system: determination and coordination of the order of joint actions of autonomous UAV groups; formation of flight (ground) tasks and flight (movement) trajectories of the UAV group; dynamic adjustment of the flight task and the role of each UAV during its use; organization of the communication network between the control center and between UAV groups, etc. The tasks and target functions of management of each of their subsystems are determined. The features of the UAV group control cycle are described.
 The main stages of the synthesis of the control system are considered: the choice of the control method (centralized, hierarchical, decentralized and mixed); selection of algorithms (protocols) and organization of the process of data exchange between UAVs. A functional model of the UAV group management system is proposed. Tasks and main management functions of each of the subsystems are defined. Features of the UAV group management cycle are described. The main stages of the synthesis of the control system are considered: the selection of algorithms (protocols) and the organized process of information exchange between UAVs; variants of formation and swarm models of UAV movement in groups; multi-agent decision-making models for the organization of task performance by the UAV group.

Robust Consensus-based Formation Control of a Group of UAV

Consensus-based formation control (CFC) is one of the most phenomenal formation control methods designed to achieve consensus between any vehicles that using and sharing their position and/or linear velocity with each other in a swarm mission. In this paper, a robust CFC (R-CFC) has been designed and proposed to realize pre-defined formation shapes with a team of unmanned aerial vehicles (UAVs). First, the double integrator dynamics of an UAV is presented. Second, the graph theory is explained briefly to understand any adjacency between UAVs. Then, the proposed R-CFC algorithm has been derived and the stability analysis has been proven via algebraic Riccati stability theory based on Lyapunov stability theorem. After that, the effectiveness of the proposed controller has been tested in real time outdoor tests. The experimental results show that the UAVs have been able to create the desired formation shapes and are less affected by external disturbances such as wind thanks to the proposed control algorithm.

Nexus of Deep Reinforcement Learning and Leader–Follower Approach for AIoT Enabled Aerial Networks

The Industrial Internet of Things (IIoT) is a new industrial 4.0 paradigm that combines IoT, robotics, cyber-physical systems, and other future industrial advancements. Unmanned Aerial Vehicles (UAVs), part of the IIoT infrastructure, have a significant potential for civil and military purposes. Through the Artificial Intelligence of Things (AIoT), a well-organized group of UAVs outperforms a single large UAV in terms of device scalability, maintenance, and expense. Therefore, the UAV swarm with industry 4.0 intelligence can be used for a wide range of 24/7 security and remote monitoring applications. Though multi-UAV systems are beneficial, their application has many challenges. There is a high risk of collision in the multi-UAV system without coordination. This paper proposes an AIoT-based Navigation and Formation Control (AIoT-NFC) mechanism to scale down the collision risk by combining Deep Reinforcement Learning (DRL) with the leader-follower approach. In AIoT-NFC, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed to navigate UAVs in remote surveillance without colliding with obstacles and other UAVs. Furthermore, the AIoT-NFC system incorporates a fault tolerance mechanism that can handle the scenario of a leader's failure due to actuator malfunction. Experimental results show that the AIoT-NFC achieves faster convergence with a lower collision rate. AIoT-NFC reduced the collision rate by 14.99% compared to existing navigation methods in successful formation without colliding with the other UAVs.