Collaborative Unmanned Aerial Vehicles Research Articles

A collaborative navigation algorithm for UAV Ad Hoc network based on improved sequence quadratic programming and unscented Kalman filtering in GNSS denied area

In global navigation satellite system (GNSS) denied area, collaborative positioning precision is important for unmanned aerial vehicle (UAV) to perform collaborative tasks in Ad Hoc network. Continuous optimization of algorithm complexity and parameter setting has long been a focal point in the field of collaborative positioning research. An algorithm of UAVs collaborative positioning based on improved sequential quadratic programming (SQP) and unscented kalman filter (UKF) is proposed. By optimizing the parameter constraints of SQP, parameters are controlled within certain conditions. The convergence speed of SQP is improved, and more accurate filtering parameters are obtained. The improved SQP and UKF are combined to optimize the location data of UAVs, which improves the precision of collaborative positioning. The simulation results show that the positioning precision can be greatly enhanced by approximately 25% when using the proposed algorithm, as compared to the traditional algorithm. The influence of the collaborative UAV number also is simulated. The positioning performance improve slowly when more than five UAVs are used for collaborative positioning. Therefore, the positioning performance cannot be improved only by increasing the number of UAVs, but also by improving the performance of collaborative positioning algorithm.

Application of intelligent self-organizing algorithms in UAV cooperative inspection of power distribution networks

In the rapidly evolving technological landscape, the advent of collaborative Unmanned Aerial Vehicle (UAV) inspections represents a revolutionary leap forward in the monitoring and maintenance of power distribution networks. This innovative approach harnesses the synergy of UAVs working together, marking a significant milestone in enhancing the reliability and efficiency of infrastructure management. Despite its promise, current research in this domain frequently grapples with challenges related to efficient coordination, data processing, and adaptive decision-making under complex and dynamic conditions. Intelligent self-organizing algorithms emerge as pivotal in addressing these gaps, offering sophisticated methods to enhance the autonomy, efficiency, and reliability of UAV collaborative inspections. In response to these challenges, we propose the MARL-SOM-GNNs network model, an innovative integration of Multi-Agent Reinforcement Learning, Self-Organizing Maps, and Graph Neural Networks, designed to optimize UAV cooperative behavior, data interpretation, and network analysis. Experimental results demonstrate that our model significantly outperforms existing approaches in terms of inspection accuracy, operational efficiency, and adaptability to environmental changes. The significance of our research lies in its potential to revolutionize the way power distribution networks are inspected and maintained, paving the way for more resilient and intelligent infrastructure systems. By leveraging the capabilities of MARL for dynamic decision-making, SOM for efficient data clustering, and GNNs for intricate network topology understanding, our model not only addresses current shortcomings in UAV collaborative inspection strategies but also sets a new benchmark for future developments in autonomous infrastructure monitoring, highlighting the crucial role of intelligent algorithms in advancing UAV technologies.

Incorporating Symbolic Discrete Controller Synthesis into a Virtual Robot Experimental Platform: An Implementation with Collaborative Unmanned Aerial Vehicle Robots

We introduce a modeling framework aimed at incorporating symbolic discrete controller synthesis (DCS) into a virtual robot experimental platform. This framework involves symbolically representing the behaviors of robotic systems along with their control objectives using synchronous programming techniques. We employed DCS algorithms through the reactive synchronous environment ReaX to generate controllers that fulfill specified objectives. These resulting controllers were subsequently deployed on the virtual robot experimental platform Simscape. To demonstrate and validate our approach, we provide an implementation example involving collaborative UAV robots.

A novel recurrent self-evolving fuzzy neural network for consensus decision-making of unmanned aerial vehicles

Currently, for years, unmanned aerial vehicles have been widely applied in a comprehensive realm. By enhancing computer photography and artificial intelligence, it can automatically discriminate against environmental objectives and detect events that occur in the real scene. The application of collaborative unmanned aerial vehicles will offer diverse interpretations which support a multiperspective view of the scene. Due to diverse interpretations of unmanned aerial vehicles usually deviates, thus, unmanned aerial vehicles require a consensus interpretation for the scenario. To previous purposes, this study presents an original consensus-based method to pilot multi-unmanned aerial vehicle systems for achieving consensus on their observation as well as constructing a group situation-based depiction of the scenario. Further, a fuzzy neural network generalized prediction control system known as a recurrent self-evolving fuzzy neural network is mainly used to ensure stability through the use of a descending gradient online learning rule. At the same time, users can think along the lines of evolutionary biological design. Unmanned aerial vehicles can be modeled as system experts for solving group problems that require the definition of conditions that best describe the scene. First, this method allows each unmanned aerial vehicle to set high-level conditions for detection events by aggregating events based on fuzzy information. These aggregated events are modeled by a fuzzy system ontology, which allows each unmanned aerial vehicle to report its preferences in conditions. Therefore, the interpretation of each drone is compressed to achieve a collective interpretation of the state. The final polls, consent and affinity polls confirmed the final decision group’s reliability ratings. The rated consensus indicates how well the collective interpretation of the scene matches each drone’s point of view.

Optimizing Task Offloading for Collaborative Unmanned Aerial Vehicles (UAVs) in Fog—Cloud Computing Environments

Optimizing Task Offloading for Collaborative Unmanned Aerial Vehicles (UAVs) in Fog—Cloud Computing Environments

A Study on Secured Unmanned Aerial Vehicle-Based Fog Computing Networks

<div>With the recent advancement in technologies, researchers worldwide have a growing interest in unmanned aerial vehicles (UAVs). The last few years have been significant in terms of its global awareness, adoption, and applications across industries. In UAV-aided wireless networks, there are some limitations in terms of power consumption, data computation, data processing, endurance, and security. So, the idea of UAVs and Edge or Fog computing together deals with the limitations and provides intelligence at the network’s edge, which makes it more valuable to use in emergency applications. Fog computing distributes data in a decentralized way and blockchain also works on the principle of decentralization. Blockchain, as a decentralized database, uses cryptographic methods including hash functions and public key encryption to secure the user information. It is a prominent solution to secure the user’s information in blocks and maintain privacy. The ongoing development in collaborative UAVs and fog computing networks had put further a major challenge of security that need to be investigated. This article presents a study on security attacks and requirements needed to ensure security in UAV-based fog networks. We provide blockchain security in UAV-based fog computing to secure a large volume of data. The communicated data is stored in the form of blocks with hash functions, which guarantee security in the network. Thus, the aim of blockchain security is to guarantee that any hostile third party cannot corrupt or change the live missions or tasks allocated to actual UAV–Fog nodes.</div>

MW-MADDPG: a meta-learning based decision-making method for collaborative UAV swarm.

Unmanned Aerial Vehicles (UAVs) have gained popularity due to their low lifecycle cost and minimal human risk, resulting in their widespread use in recent years. In the UAV swarm cooperative decision domain, multi-agent deep reinforcement learning has significant potential. However, current approaches are challenged by the multivariate mission environment and mission time constraints. In light of this, the present study proposes a meta-learning based multi-agent deep reinforcement learning approach that provides a viable solution to this problem. This paper presents an improved MAML-based multi-agent deep deterministic policy gradient (MADDPG) algorithm that achieves an unbiased initialization network by automatically assigning weights to meta-learning trajectories. In addition, a Reward-TD prioritized experience replay technique is introduced, which takes into account immediate reward and TD-error to improve the resilience and sample utilization of the algorithm. Experiment results show that the proposed approach effectively accomplishes the task in the new scenario, with significantly improved task success rate, average reward, and robustness compared to existing methods.

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs.

Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAV Networks: A Metaheuristic Approach

Nowadays, unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) systems have been exploited as a promising solution for providing computation services to mobile users outside of terrestrial networks. However, it remains challenging for standalone UAVs to meet the computation requirement of numerous users due to their limited computation capacity and battery lives. Therefore, we propose a collaborative scheme among UAVs to share the workload between them. Furthermore, this work is the first to consider the task topology of offloading in MEC-enabled UAVs networks while restricting their power consumption. We study the task topology, in which a task consists of a set of sub-tasks, and each sub-task has dependencies upon other sub-tasks. In the real world, sub-tasks with dependencies must wait for their preceding sub-tasks to complete before being executed, and this affects the offloading strategy. Next, we formulate an optimization problem to minimize the average latency of users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem is NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two sub-problems: offloading decision problem and communication resource allocation problem. Then, a meta-heuristic method is proposed to find the sub-optimal solution to the former problem, while the latter problem is solved by using convex optimization. Finally, we conduct simulation experiments to prove that our proposed offloading technique outperforms several benchmark schemes in minimizing the average latency of users for dependency tasks and achieving higher uplink transmission rates.

Q-learning-based routing inspired by adaptive flocking control for collaborative unmanned aerial vehicle swarms

A collaborative unmanned aerial vehicle (UAV) swarm can support various applications, including aerial surveillance and emergency communication. Owing to the high mobility, limited energy of UAVs, and frequent link breakages, data routing from remote UAVs to a base station may produce retransmissions, loops, energy holes, and long delay. In a UAV swarm network, therefore, relative mobility, path stability, and delay should be jointly taken into consideration to improve routing performance because they are highly coupled with each other. However, they are not properly exploited in the existing literature. To address these issues, in this paper, a Q-learning (QL)-based routing protocol inspired by adaptive flocking control (QRIFC) is proposed. The proposed adaptive flocking control algorithm generates optimal mobility with fairness in travel distance for each UAV to control the optimal node density. It also addresses the trade-off between aerial coverage and quality of service in connectivity by imposing constraints on the minimum separation distance and maximum allowable inter-UAV spacing using two-hop neighbor information. Additionally, it provides a stable link duration (LD) between neighboring UAVs and optimizes the control overhead. Furthermore, QL performs multi-objective optimization by utilizing a new state exploration and exploitation strategy to select an optimal routing path in terms of delay, stable path selection defined by predictive LD, and energy consumption. According to an extensive performance study, the proposed QRIFC outperforms existing routing protocols by 21-40 percent of average end-to-end delay and 9-23 percent of average packet delivery ratio, with less retransmissions.

An Improved Identity-Based Generalized Signcryption Scheme for Secure Multi-Access Edge Computing Empowered Flying Ad Hoc Networks

Emerging Unmanned Aerial Vehicles (UAVs) have applications for traffic monitoring, public safety, surveillance, agriculture, health services. Collaborative UAVs can form a flying ad hoc network having security vulnerabilities due to open access media and limited power. Very recently, Khan et al. presented an Identity-Based Generalized Signcryption for Multiaccess Edge Computing Empowered by Flying Ad Hoc Networks Secure Deployment. The paper presented the cryptanalysis of Khan et al. scheme shows that their scheme does not provide message confidentiality, Authenticity, and integrity. Also, presented an improved scheme, Identity-Based Generalized Signcryption. The proposed scheme is provably secure against the mentioned security attacks. The improved scheme is efficient and attractive for Multiaccess Edge Computing Empowered by Flying Ad Hoc Networks.

A Provenance-Aware Distributed Trust Model for Resilient Unmanned Aerial Vehicle Networks

An unmanned aerial vehicle (UAV) network is an emerging industrial IoT network for collaborative UAV communication and management. The open architecture and dynamic topology, which provide functional benefits, unfortunately make UAVNs more vulnerable to a variety of attacks. In UAVNs, malicious nodes not only eavesdrop the communications between UAV nodes but also attempt to attack the entire network by injecting or modifying messages. This work proposes a provenance-aware distributed trust model, named UAV-pro, for UAVNs that aim to achieve accurate peer-to-peer trust assessment and maximize the delivery of correct messages received by destination nodes while minimizing the message delay and communication cost under resource-constrained network environments. Provenance refers to the history of ownership of messages transmitted on the network. The behavior of message creators and operators can be effectively evaluated based on message integrity, then generate the observational evidence. We collect the observational evidence for distributed trust evaluation, then identify malicious nodes in the network and isolate them from the network. UAVN-pro takes a data-driven approach to reduce resource consumption in the presence of selfish or malicious nodes while ensuring the safe transmission of data by digital signature technology. The experimental results show that UAVN-pro works are compatible with the existing UAV network routing protocols, and can effectively identify attacks, such as the black hole, gray hole, message modification, fake recommendation, and fake identity in UAV networks. UAVN-pro is superior to the existing security model in terms of detection rate, delivery rate, and system energy consumption in most cases.

Advanced UAV-WSN System for Intelligent Monitoring in Precision Agriculture.

The growing need for food worldwide requires the development of a high-performance, high-productivity, and sustainable agriculture, which implies the introduction of new technologies into monitoring activities related to control and decision-making. In this regard, this paper presents a hierarchical structure based on the collaboration between unmanned aerial vehicles (UAVs) and federated wireless sensor networks (WSNs) for crop monitoring in precision agriculture. The integration of UAVs with intelligent, ground WSNs, and IoT proved to be a robust and efficient solution for data collection, control, analysis, and decisions in such specialized applications. Key advantages lay in online data collection and relaying to a central monitoring point, while effectively managing network load and latency through optimized UAV trajectories and in situ data processing. Two important aspects of the collaboration were considered: designing the UAV trajectories for efficient data collection and implementing effective data processing algorithms (consensus and symbolic aggregate approximation) at the network level for the transmission of the relevant data. The experiments were carried out at a Romanian research institute where different crops and methods are developed. The results demonstrate that the collaborative UAV–WSN–IoT approach increases the performances in both precision agriculture and ecological agriculture.

Minimizing Data Collection Time With Collaborative UAVs in Wireless Sensor Networks

Unmanned aerial vehicle (UAV) data collection is a promising research direction that can be applied to many practical scenarios. Due to the limited energy of sensors in wireless sensor networks (WSN), UAV, which is considered as a mobile fusion center, can effectively prolong the lifetime of sensor via supporting communication with the sensor directly. However, since the UAV's energy constrained, it is necessary that multiple UAVs provide data collection to sensors in large areas. In this paper, we consider a scenario where multiple UAVs collect data from a set of two-dimensional distributed sensors. The UAV can communicate with sensors while flying or hovering. The goal is to minimize the total time that all UAVs from data center and return to data center after completing all collection tasks, while giving each sensor a certain amount of data and energy. The problem of minimizing the task completion time of multiple UAVs is still a big challenge. We solve the multi-UAV problem by jointly optimizing UAV-sensor association mechanism and data collection method of UAVs. The numerical results show that the proposed multi-UAV data collection scheme shortens the task completion time.

A Survey of Collaborative UAV-WSN Systems for Efficient Monitoring.

Integrated systems based on wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) with electric propulsion are emerging as state-of-the-art solutions for large scale monitoring. Main advances stemming both from complex system architectures as well as powerful embedded computing and communication platforms, advanced sensing and networking protocols have been leveraged to prove the viability of this concept. The design of suitable algorithms for data processing, communication and control across previously disparate domains has thus currently become an intensive area of interdisciplinary research. The paper was focused on the collaborative aspects of UAV–WSN systems and the reference papers were analyzed from this point of view, on each functional module. The paper offers a timely review of recent advances in this area of critical interest with focus on a comparative perspective across multiple recent theoretical and applied contributions. A systematic approach is carried out in order to structure a unitary from conceptual design towards key implementation aspects. Focus areas are identified and discussed such as distributed data processing algorithms, hierarchical multi-protocol networking aspects and high level WSN–constrained UAV-control. Application references are highlighted in various domains such as environmental, agriculture, emergency situations and homeland security. Finally, a research agenda is outlined to advance the field towards tangible economic and social impact.

Autonomous Exploration and Mapping System Using Heterogeneous UAVs and UGVs in GPS-Denied Environments

In this paper, we present a novel integrated vehicular system using collaborative unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) for autonomous exploration, mapping, and navigation in GPS-denied 3-dimensional (3-D) unknown environments. The system implements a novel two-layered exploration strategy to decompose the perception task into a coarse exploration layer and a fine mapping layer. The coarse exploration makes use of a UGV to carry out fast autonomous exploration and active 2.5D simultaneous localization and mapping (SLAM) to generate a coarse environment model, which serves as a navigation reference for subsequent complementary 3-D fine mapping conducted by a UAV. The two layers share a novel optimized exploration path planning and navigation framework, which provides optimal exploration paths and integrates the collaborative exploration and mapping efforts through an OctoMap-based volumetric motion planning interface. The proposed system provides an efficient pipeline of fast environment perception taking advantages of the agility of the UAVs as well as the powerful computation resource aboard UGVs, also allowing assistive local perception with augmented object information when necessary. The effectiveness of our system is verified by both simulations and experiments, which demonstrate its capability of implementing heterogeneous UAV and UGV collaborative exploration and structural reconstruction of the environments through active SLAM, providing optimized perception for navigation tasks.

Learning communication delay patterns for remotely controlled UAV networks

This paper deals with collaborative unmanned aerial vehicles (UAVs) that are remotely controlled from a cloud server. The main contribution is to apply machine learning technique to find a pattern of network-induced effects on maneuvers of UAVs, in order to compensate for time delays and packet losses in remote communication. As machine learning technique, a Gaussian process (GP) based approach is employed due to its computational simplicity and flexibility in modelling complex expressions using a small number of parameters. We combine a deterministic compensation for an enhanced GP model to overcome a problem of the lack of training data at the beginning of training phase. This is done by defining training data input as a set of delayed observation and the deterministic compensation term, and by training the GP on residual between the true state and the input set. The proposed algorithm is evaluated to collaborative trajectory tracking of two UAVs. Simulations are performed for various delays and tracking scenarios. It is shown that the better tracking results are achieved compared to a conventional linear compensation algorithm.

Model of Collaborative UAV Swarm Toward Coordination and Control Mechanisms Study

In recent years, thanks to the low cost of deploying, maintaining an Unmanned Aerial Vehicle (UAV) system and the possibility to operating them in areas inaccessible or dangerous for human pilots, UAVs have attracted much research attention both in the military field and civilian application. In order to deal with more sophisticated tasks, such as searching survival points, multiple target monitoring and tracking, the application of UAV swarms is forseen. This requires more complex control, communication and coordination mechanisms. However, these mechanisms are difficult to test and analyze under flight dynamic conditions. These multi- UAV scenarios are by their nature well suited to be modeled and simulated as multi-agent systems. The first step of modeling an multi-agent system is to construct the model of agent, namely accurate model to represent its behavior, constraints and uncertainties of UAVs. In this paper we introduce our approach to model an UAV as an agent in terms of multi-agent system principle. Construction of the model to satisfy the need for a simulation environment that researchers can use to evaluate and analyze swarm control mechanisms. Simulations results of a case study is provided to demonstrate one potential use of this approach.

An architecture for secure positioning in a UAV swarm using RSSI-based distance estimation

The widespread of small unmanned aerial vehicles (UAVs) offers more opportunities for collaborative UAV swarm to optimise missions. The common application for UAV swarms are surveillance, path planning, airborne, and relay networks. Cooperative applications make use of the UAVs' locations to make decisions. However, the question of vulnerability to security must be considered when the system infers that there are benefits and rights based on the UAV's location. There is a risk that an attacker can cheat the system by claiming a false or inaccurate location to gain access to restricted resources or be engaged in malicious activities without detection. This makes the system be very sensitive and dependent on the trust of the node's geo-temporal information in a swarm. In this paper, we propose an architecture that uses a UAV wireless card to measure the distance between a UAV, called a prover node , and the set of verifier nodes closest to it. The architecture core is based on a multilateration algorithm, which is employed to estimate the prover's position. In a previous study, we applied image processing from a UAV payload stereo camera to measure the distances between the nodes. In this paper, we have extended the study to securing positioning in a UAV swarm by applying the fundamental principles of beaconing-based communication to calculate the distance between the nodes on the basis of the received signal strength indicator (RSSI). The simulation results showed that the correct position determined by RSSI was better than the image processing from stereo cameras. The degree of accuracy achieved from RSSI was greater than 99.6% in distances of up to 165 m , while a similar degree of accuracy was limited up to 100 m of the distance, when a high-definition stereo camera was used.