Swarm Of Vehicles Research Articles

Performance Analysis of Reconnaissance Coverage for HUAV Swarms under Communication Interference Based on Different Architectures

In environments with unknown communication interference, the mission efficiency of heterogeneous unmanned aerial vehicle (HUAV) swarms is often impacted by communication disruptions due to regions of strong interference encountered when executing reconnaissance and coverage missions. Existing research has rarely focused on communication interference or on the differences in HUAV characteristics under various control architectures; thus, in this paper we explore the performance differences between HUAV swarms based on centralized, distributed, and centralized-distributed architectures when executing reconnaissance and coverage missions in environments with unknown communication interference. First, a communication model in an unknown interference environment is constructed to reflect the real-time communication status of the swarm. Second, in response to the limitations of the traditional artificial potential field (APF) algorithm in this environment, a coverage-oriented artificial potential field (COAPF) algorithm is proposed. Finally, based on the COAPF algorithm, a multi-dimensional comparison of the mission completion efficiency of HUAV swarms with three different architectures is conducted. Our simulation results indicate that the distributed architecture is suitable for large-scale environments with strong interference, while the centralized–distributed architecture performs better in small-scale environments with weak interference. Conversely, the centralized architecture exhibits poor performance in all interference scenarios due to its lack of decision-making capabilities.

A Multi Moving Target Localization in Agricultural Farmlands by Employing Optimized Cooperative Unmanned Aerial Vehicle Swarm

The paper proposes an original method for employing optimised cooperative swarms of Unmanned Aerial Vehicles (UAVs) to localise multiple moving objects in agricultural farmlands. Crop Monitoring (CM), targeted fertilizer distribution, and Livestock Management (LM) are some of the Smart Farming (SF) applications of UAVs. However, the ever-changing nature of agricultural settings makes it challenging to set up UAV swarms. Detecting multiple evolving objectives in dynamic environments is complicated, and conventional methods are regularly optimized for single objectives, such as area or reduced Energy Consumption (EC), which is unsuitable. This research recommends a Multi-Objective Evolutionary Algorithm (MOEA) as a model for UAV swarms to balance task service, communication, and EC during the investigation. The approach paves the method for innovation in the agricultural sector by optimizing tasks in real-time, addressing unpredictable targets, boosting productivity, and reducing costs. The study’s findings present optimism for smart farm management and accurate SF by improving UAV systems’ response time and scalability.

A Target Sensing and Visual Tracking Method for Countering Unmanned Aerial Vehicle Swarm

A Target Sensing and Visual Tracking Method for Countering Unmanned Aerial Vehicle Swarm

Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects

Interstellar objects (ISOs) are likely representatives of primitive materials invaluable in understanding exoplanetary star systems. Due to their poorly constrained orbits with generally high inclinations and relative velocities, however, exploring ISOs with conventional human-in-the-loop approaches is significantly challenging. This paper presents Neural-Rendezvous—a deep-learning-based guidance and control framework for encountering fast-moving objects, including ISOs, robustly, accurately, and autonomously in real time. It uses pointwise minimum norm tracking control on top of a guidance policy modeled by a spectrally normalized deep neural network, where its hyperparameters are tuned with a loss function directly penalizing the model predictive control state trajectory tracking error. We show that Neural-Rendezvous provides a high probability exponential bound on the expected spacecraft delivery error, the proof of which leverages stochastic incremental stability analysis. In particular, it is used to construct a non-negative function with a supermartingale property, explicitly accounting for the ISO state uncertainty and the local nature of nonlinear state estimation guarantees. In numerical simulations, Neural-Rendezvous is demonstrated to satisfy the expected error bound for 100 ISO candidates. This performance is also empirically validated using our spacecraft simulator and in high-conflict and distributed unmanned aerial vehicle swarm reconfiguration with up to 20 unmanned aerial vehicles.

An improved whale optimization algorithm for UAV swarm trajectory planning

For the problem of trajectory planning in the small-scale unmanned aerial vehicle (UAV) swarms, classical intelligent algorithms and newly emerged bio-inspired algorithms often suffer from being trapped in local optima, leading to suboptimal solutions. Moreover, these algorithms fail to ensure optimal solutions and convergence speed in high-speed dynamic UAV networking scenarios. In response to these challenges, this paper proposes an Improved Whale Optimization Algorithm (IWOA) that considers the global perspective. To address the issue of initial solution diversity, an opposition-based learning method is introduced by constructing a reverse population, enhancing the diversity of the initial population. To overcome the problem of the algorithm getting trapped in local optima, random convergence factors, and end-point random neighborhood perturbations are incorporated to accelerate the global convergence speed of the IWOA and prevent it from getting stuck in local optima. Additionally, a grid digital elevation model is employed when constructing the experimental environment model, considering various physical constraints of the UAV swarm. This ensures that the simulation validation of the IWOA algorithm is closer to real-world scenarios. Simulation results indicate that, compared to commonly used algorithms, the IWOA can generate more optimal trajectories for drone swarm planning under constraints resembling real-world scenarios. It exhibits better performance in trajectory evaluation, convergence speed, and stability, thereby meeting the requirements of trajectory planning for small-scale UAV swarms. The proposed IWOA enhances UAV swarm coordination and efficiency, significantly impacting real-world applications in various fields.

Mission Planning and Trajectory Optimization in UAV Swarm for Track Deception against Radar Network

In this article, a mission planning and trajectory optimization scheme in unmanned aerial vehicle (UAV) swarm for track deception against radar networks is proposed. The core of this scheme is to formulate the track deception problem as a model with the objective of simultaneously maximizing the number of phantom tracks while minimizing the total flight distance of the UAV swarm, subject to the constraints of UAV kinematic performance, phantom track rotation angles, and a homology test. It is shown that the formulated track deception problem is a mixed-integer programming, multivariable, and non-linear optimization model. By incorporating mission planning based on platform reuse and a particle swarm optimization (PSO) algorithm, a three-stage solution methodology is proposed to tackle the above problem. Through joint optimization for mission planning and flight trajectories of the UAV swarm, a low-speed UAV swarm is capable of generating a number of high-speed phantom tracks. Numerical results demonstrate that the proposed scheme enables a low-speed UAV swarm to generate as many high-speed phantom tracks as possible, effectively achieving track deception against radar network.

Adaptive dynamic programming–based fault-tolerant control of multi-UAV formation system

With the increasing utilization of unmanned aerial vehicle (UAV) swarms in civilian and military field applications, it is crucial to eliminate the potential impacts of UAV faults on task completion and efficiency. To address this issue, the present paper investigates a fault-tolerant control scheme based on the adaptive dynamic programming (ADP) method and fault observer. The whole closed-loop system is composed of a position loop and an attitude loop. An ADP-based position-tracking controller is established with an improved cost function that can simultaneously represent actuator faults, control inputs, and tracking errors. In addition, a single-layer critic neural network is constructed to estimate the cost function and approximate control inputs. In the process of adjusting the weights of the neural network, a unique adjustment method combining policy gradient and prioritized experience replay is adopted to improve the data usage efficiency and speed up the weight convergence. Furthermore, a sliding mode–based attitude controller is utilized for the inner-loop attitude subsystem. The significant feature of this scheme is that it has the ability to self-learn and self-adapt. A Lyapunov stability analysis is performed to verify whether the signals are uniformly ultimately bounded. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed scheme.

Adaptive decision-making with deep Q-network for heterogeneous unmanned aerial vehicle swarms in dynamic environments

With the continuous advancement of Unmanned Aerial Vehicle (UAV) swarm technology, the field of adaptive decision-making for heterogeneous UAV swarms across a spectrum of tasks is rapidly emerging as a focal point of research. This domain holds significant importance for enhancing the operational efficiency and responsiveness of UAV swarms in complex and dynamic environments. This paper addresses this challenge by introducing an innovative adaptive task decision-making method, which leverages Deep Q-Network (DQN) algorithms, specifically tailored to meet the unique requirements of heterogeneous UAV swarms. We initially construct a mathematical model that transforms the intricate swarm decision-making issues into a standard Markov Decision Process (MDP), and meticulously design the state space, action space, reward function, and state transition function to cater to the decision-making needs of UAV swarms. Utilizing this model, we further integrate a hybrid experience replay mechanism, coupled with DQN algorithms for offline training, enabling the efficient transformation of training outcomes into a network model that is directly applicable to real-time online decision-making. This approach markedly enhances the decision-making adaptability and dynamic adaptability of UAV swarms when confronted with a variety of tasks. During the offline training phase, we also employ a hybrid experience replay mechanism and target network approximation techniques to bolster the network's stability and training efficiency, ensuring high reliability in online decision-making. Comparative simulations with existing DQN algorithms reveal the distinct superiority of our method in addressing adaptive task decision-making challenges. Moreover, through 1000 comprehensive simulation experiments, we further validate the stability and effectiveness of our method across diverse task environments, providing a solid theoretical foundation and technical support for the practical application and proliferation of UAV swarm technology.

Predefined-time convergence strategies for multi-cluster games in hybrid heterogeneous systems

This paper explores the problem of (generalized) Nash equilibrium search in multi-cluster games with heterogeneous dynamics and multiple constraints. Within this research framework, each agent acquires information solely through local interactions with its neighbors and forms clusters based on similarity of interests. These clusters manifest dual relationships of cooperation and competition: agents within the same cluster enhance decision-making capabilities through cooperation, while different clusters compete to maximize their respective benefits. To delve into these complex interactions among clusters and the learning and evolution processes among agents, four distributed control algorithms suitable for various scenario requirements are designed and implemented. These algorithms ensure that each agent converges to a Nash equilibrium (NE) or generalized Nash equilibrium (GNE) of the multi-cluster system within predefined time points. Finally, we apply these algorithms to the connectivity control problem of unmanned aerial vehicle swarms with diverse dynamics, validating the theoretical results through comprehensive simulations.

Load-adaptive MAC protocol for frontier detection in Underwater Mobile Sensor Network

This work proposes a load-adaptive Medium Access Control (MAC) protocol for the frontier/boundary detection application of underwater phenomena using Underwater Mobile Sensor Network (UWMSN). A leader-follower architecture of a swarm of underwater vehicles is proposed here. Autonomous Underwater Vehicles (AUVs) traverse a random mobility pattern beneath one Autonomous Surface Vehicle (ASV) (leader) in the proposed network. ASV has to guide multiple-follower AUVs in the event of interest. The vehicular swarm aims to explore the frontiers in the event to build the map. Load-adaptive MAC protocol is therefore proposed and implemented in this hybrid multi-vehicular network to ensure seamless vehicular communications. The ASV has navigational capabilities to aid the AUVs in navigation and data collection. The proposed MAC protocol can adjust the dynamic mobility and load in the network. The protocol aims to provide dynamic Time Division Multiple Access (TDMA) slots for the AUVs wirelessly linked in the vicinity of the ASV. These slots are used for ranging/navigation and data transmission. Additional urgent data from any AUVs can be transmitted in open Carrier Sense Multiple Access (CSMA) protocol following the TDMA duration. Results have been generated by comparing protocols like CSMA, ALOHA, and TDMA with the proposed Load-Adaptive MAC protocol. The protocols have been compared to the throughput vs number of nodes and throughput vs simulation time. It has been observed that the proposed MAC can perform better than ALOHA and CSMA protocols. Nevertheless, it can produce comparable results for TDMA protocol while supporting the dynamic mobility and load in the network meantime supporting urgent data transmission for nodes in demand.

Theory and experiment on distributed output formation tracking of unmanned aerial and ground vehicle swarm systems over jointly connected digraphs

This paper studies the distributed output formation tracking control problem of the unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) swarm systems, where the UAV swarm cooperatively tracks the output trajectory of the UGV in a formation under directed jointly connected communication networks. A three-layer formation tracking protocol is proposed. Firstly, a novel distributed observer using neighbor interactions is designed for each UAV to estimate the states of the UGV with parameterized inputs over periodic jointly connected digraphs. Next, based on the observation result and output regulation theory, a virtual reference system that tracks the trajectory of the UGV in the desired formation is constructed to generate reference states for each UAV. Then based on the differential flatness of UAVs, a geometric controller is utilized for UAVs to track the reference states and form the formation. An algorithm to determine the gain matrices of the protocol is also presented while the convergence of the system is analyzed. Finally, an experiment platform with three quadrotor UAVs and one UGV is built. The effectiveness of the proposed protocol is validated both by the simulation and experiment.

Distributed safety flight control for LAV swarm in a hostile environment under multi-constraints and sudden appearing risks

PurposeLoitering aerial vehicle (LAV) swarm safety flight control is an unmanned system control problem under multiple constraints, which are derived to prevent the LAVs from suffering risks inside and outside the swarms. The computational complexity of the safety flight control problem grows as the number of LAVs and of the constraints increases. Besides some important constraints, the swarms will encounter with sudden appearing risks in a hostile environment. The purpose of this study is to design a safety flight control algorithm for LAV swarm, which can timely respond to sudden appearing risks and reduce the computational burden.Design/methodology/approachTo address the problem, this paper proposes a distributed safety flight control algorithm that includes a trajectory planning stage using kinodynamic rapidly exploring random trees (KRRT*) and a tracking stage based on distributed model predictive control (DMPC).FindingsThe proposed algorithm reduces the computational burden of the safety flight control problem and can fast find optimal flight trajectories for the LAVs in a swarm even there are multi-constraints and sudden appearing risks.Originality/valueThe proposed algorithm did not handle the constraints synchronously, but first uses the KRRT* to handle some constraints, and then uses the DMPC to deal with the rest constraints. In addition, the proposed algorithm can effectively respond to sudden appearing risks by online re-plan the trajectories of LAVs within the swarm.

Compatible multi-model output fusion for complex systems modeling via set operation-based focus data identification

Is an inferior model completely useless in complex systems modeling? This study proposes a novel multi-model output fusion approach that makes the best of the outputs from multi-models, i.e., using the limited superior results from inferior models to compensate for certain inferior results from even superior models. For fusing the outputs from multi-models, the weight assigned to each output is calculated based on two factors, namely the accuracy of each model and the similarity between the testing input and the focus data used for constructing the respective model. Specifically for similarity calculation, the focus data list is identified based on set operations. There are three theoretical contributions of this study, namely accuracy-and-similarity-based weight calculation, the set-operation-based similarity calculation which is an addition to traditional distance-based calculation, and the high compatibility of the proposed approach which is independent from any baseline approach. A practical case of overall reconnaissance capability evaluation of the Unmanned Aerial Vehicle (UAV) swarm is studied for validation. Primary results indicate that the proposed approach can outperform two single models: the backpropagation neural network (BPNN) and the Radial Basis Function (RBF) neural network. Further validations demonstrate that the proposed approach also outperforms multi-model output fusion with equal weights, without model accuracy, with varied focus data percentages ranging from 0.1 to 0.9. More importantly, the proposed approach remains effective in four different conditions of multi-model outputs fusion with improvements from 9.58 % to 38.03 %.

A swarm anomaly detection model for IoT UAVs based on a multi-modal denoising autoencoder and federated learning

The widespread application of unmanned aerial vehicle (UAV) swarms has posed unique challenges for anomaly detection. Multi-modal noise from multi-source heterogeneous sensors during UAV swarm communication affects data quality, and limited data sharing between different UAV organisations restricts training a unified anomaly detection model. To address these problems, this study proposes a UAV swarm anomaly detection model based on a multi-modal denoising autoencoder and federated learning (L-MDAE). First, L-MDAE simulates noise by adding perturbations to the original data during UAV swarm communication. Second, according to the characteristics of UAV data noise, this study designs a new MSE loss function (normalised mean square error, NMSE) based on the normalised correlation coefficient. Furthermore, heterogeneous neural networks with NMSE are constructed to enhance the multi-modal noise–removal capability of the model. Finally, this study considers the UAV control node as the client and the ground control station as the server. Using a federated learning mechanism, L-MDAE is trained on a client dataset, and its parameters are integrated and distributed on the server. In this way, each UAV can effectively detect abnormal data using L-MDAE. Experimental results on five datasets, including ALFA, TLM and ITS, demonstrate that L-MDAE outperforms baseline and related models. When using ALFA, L-MDAE achieved an accuracy of 0.9919 and a swarm anomaly detection accuracy of 0.9901, approximately 2% higher than that of the baseline model.

Enhancing Mission Planning of Large-Scale UAV Swarms with Ensemble Predictive Model

Target assignment and trajectory planning are two crucial components of mission planning for unmanned aerial vehicle (UAV) swarms. In large-scale missions, the significance of planning efficiency becomes more pronounced. However, existing planning algorithms based on evolutionary computation and swarm intelligence face formidable challenges in terms of both efficiency and effectiveness. Additionally, the extensive trajectory planning involved is a significant factor affecting efficiency. Therefore, this paper proposes a dedicated method for large-scale mission planning. Firstly, to avoid extensive trajectory planning operations, this paper suggests utilizing a machine learning algorithm to establish a predictive model of trajectory length. To ensure predictive accuracy, an ensemble algorithm based on Gaussian process regression (GPR) is proposed. Secondly, to ensure the efficiency and effectiveness of target assignments in large-scale missions, this paper draws inspiration from a greedy search and proposes a simple yet effective target assignment algorithm. This algorithm can effectively handle a large number of decision variables and constraints involved in large-scale missions. Finally, we validated the effectiveness of the proposed method through 15 simulated missions of different scales. Among the 10 medium- to large-scale missions, our method achieved the best results in 9 of them, demonstrating the competitive advantage of our method in large-scale missions. Comparative results demonstrate the advantage of the proposed methods from both prediction and mission planning perspectives.

Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution.

Rapid Initialization Method of Unmanned Aerial Vehicle Swarm Based on VIO-UWB in Satellite Denial Environment

In environments where satellite signals are blocked, initializing UAV swarms quickly is a technical challenge, especially indoors or in areas with weak satellite signals, making it difficult to establish the relative position of the swarm. Two common methods for initialization are using the camera for joint SLAM initialization, which increases communication burden due to image feature point analysis, and obtaining a rough positional relationship using prior information through a device such as a magnetic compass, which lacks accuracy. In recent years, visual–inertial odometry (VIO) technology has significantly progressed, providing new solutions. With improved computing power and enhanced VIO accuracy, it is now possible to establish the relative position relationship through the movement of drones. This paper proposes a two-stage robust initialization method for swarms of more than four UAVs, suitable for larger-scale satellite denial scenarios. Firstly, the paper analyzes the Cramér–Rao lower bound (CRLB) problem and the moving configuration problem of the cluster to determine the optimal anchor node for the algorithm. Subsequently, a strategy is used to screen anchor nodes that are close to the lower bound of CRLB, and an optimization problem is constructed to solve the position relationship between anchor nodes through the relative motion and ranging relationship between UAVs. This optimization problem includes quadratic constraints as well as linear constraints and is a quadratically constrained quadratic programming problem (QCQP) with high robustness and high precision. After addressing the anchor node problem, this paper simplifies and improves a fast swarm cooperative positioning algorithm, which is faster than the traditional multidimensional scaling (MDS) algorithm. The results of theoretical simulations and actual UAV tests demonstrate that the proposed algorithm is advanced, superior, and effectively solves the UAV swarm initialization problem under the condition of a satellite signal rejection.

Intelligent Decision-Making Algorithm for UAV Swarm Confrontation Jamming: An M2AC-Based Approach

Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an intelligent decision-making algorithm for UAV swarm confrontation jamming based on the multi-agent actor–critic (M2AC) model. First, based on Markov games, an intelligent mathematical decision-making model is constructed to transform the confrontation jamming scenario into a symbolized mathematical problem. Second, the indicator function under this learning paradigm is designed by combining the actor–critic algorithm with Markov games. Finally, by employing a reinforcement learning algorithm with multithreaded parallel training–contrastive execution for solving the model, a Markov perfect equilibrium solution is obtained. The experimental results indicate that the algorithm based on M2AC can achieve faster training and decision-making speeds, while effectively obtaining a Markov perfect equilibrium solution. The training time is reduced to less than 50% compared to the baseline algorithm, with decision times maintained below 0.05 s across all simulation conditions. This helps alleviate the low-timeliness problem of UAV swarm confrontation jamming intelligent decision-making algorithms under highly dynamic real-time conditions, leading to more effective and efficient UAV swarm operations in various jamming and electronic warfare scenarios.

Cooperative Target Fencing Control for Unmanned Aerial Vehicle Swarm with Collision, Obstacle Avoidance, and Connectivity Maintenance

Cooperative Target Fencing Control for Unmanned Aerial Vehicle Swarm with Collision, Obstacle Avoidance, and Connectivity Maintenance

Reinforced robotic bean optimization algorithm for cooperative target search of unmanned aerial vehicle swarm

Increasing attention has been given to the utilization of swarm intelligent optimization algorithms to facilitate cooperative target search of unmanned aerial vehicle swarm (UAVs). However, there exist common issues associated with swarm intelligent optimization algorithms, which are low search efficiency and easy to trap in local optima. Simultaneously, the concentrated initial positioning of UAVs increase the probability of collisions between UAVs. To address these issues, this paper proposes a reinforced robotic bean optimization algorithm (RRBOA) aimed at enhancing the efficiency of UAVs for cooperative target search in unknown environments. Firstly, the algorithm employs a region segmentation exploration strategy to enhance the initialization of UAVs, ensuring a uniform distribution of UAVs to avoid collisions and the coverage capability of UAVs search. Subsequently, a neutral evolution strategy is incorporated based on the spatial distribution pattern of population, which aims to enhance cooperative search by enabling UAVs to freely explore the search space, thus improving the global exploration capability of UAVs. Finally, an adaptive Levy flight strategy is introduced to expand the search range of UAVs, enhancing the diversity of UAVs search and then preventing the UAVs search from converging to local optima. Experimental results demonstrate that RRBOA has significant advantages over other methods on nine benchmark simulations. Furthermore, the extension testing, which focuses on simulating pollution source search, confirms the effectiveness and applicability of RRBOA