Multiple Unmanned Aerial Vehicles Research Articles

Research on multi-UAV air pollution source localization algorithm based on emotional quantity

With the advancement of industrialization, the problem of atmospheric environmental pollution is becoming more and more prominent. To solve this problem, an unmanned aerial vehicle (UAV) as an airborne platform was used to design an air pollution source localization method based on an anxiety-auction algorithm and verify the feasibility of the algorithm through simulation analysis and indoor source localization experiments. The algorithm innovatively introduces the concept of anxiety in psychology into the traditional auction algorithm. By enabling each drone to make a “rational” auction time decision based on its emotional state, team resources can be conserved, and overall source localization efficiency can be enhanced. Based on different environmental factors and conditions, the number of drones and other multi-perspective comparison analyses with the traditional auction algorithm, the analysis results show that the anxiety-auction algorithm performs better in terms of success rate and distance ratio. This paper also built a set of atmospheric pollutant source localization platforms, consisting of an ultra-wideband (UWB) indoor positioning device, UAV platform, source localization monitoring and control module, and the indoor source localization experiment of atmospheric pollutants based on multiple UAVs was successfully designed and carried out.

Service Time Maximization for Data Collection in Multi-UAV-Aided Networks

Unmanned aerial vehicles (UAVs) have been enormously gaining attention to offload traffic or collect data in wireless networks due to their key attributes, such as mobility, flexibility, and cost-effective deployment. However, the limited onboard energy inhibits the UAV from serving for a longer duration. Therefore, this paper studies a UAV-aided network where multiple UAVs are launched to collect data from the mobile nodes. In particular, we aim to maximize the service time of the UAVs by jointly optimizing the three-dimensional (3D) trajectory of the UAVs and resources allocated to each node by the UAVs such that each mobile node receives a minimum specified data rate. To facilitate a solution, we construct an equivalent problem that considers the UAV's energy consumption. In particular, we minimize the maximum energy consumed by the UAVs in each time slot. To solve the problem, an iterative approach is presented that decouples the problem into two sub-problems. The optimal location of the UAVs is computed in the first sub-problem, while resource allocation is carried out in the second sub-problem. These two sub-problems are solved in an iterative manner using the alternating optimization approach. We show that the proposed approach improves the service time of the network by 20% on average compared to the existing approaches.

Free-Position Dynamic WPT System Supporting InFlight Concurrent Charging for Multiple Unmanned Aerial Vehicles

Free-Position Dynamic WPT System Supporting InFlight Concurrent Charging for Multiple Unmanned Aerial Vehicles

Adaptive Distributed Fuzzy Control for Prescribed-Time Formation of Multiple Unmanned Aerial Vehicles

Adaptive Distributed Fuzzy Control for Prescribed-Time Formation of Multiple Unmanned Aerial Vehicles

Multiple Unmanned Aerial Vehicles Coalition Formation and Control for Collaborative Defense Mission

Multiple Unmanned Aerial Vehicles Coalition Formation and Control for Collaborative Defense Mission

C3R: A novel Classification model-based Coordination method for online Conflict Resolution of Multiple Unmanned Aerial Vehicles

C3R: A novel Classification model-based Coordination method for online Conflict Resolution of Multiple Unmanned Aerial Vehicles

Cooperative Landing on Mobile Platform for Multiple Unmanned Aerial Vehicles via Reinforcement Learning

Cooperative Landing on Mobile Platform for Multiple Unmanned Aerial Vehicles via Reinforcement Learning

An Efficient Matching Game Approach to Association Formation in UAV-Enabled Hierarchical Distributed Learning.

Distributed machine learning has emerged as a promising data processing technology for next-generation communication systems. It leverages the computational capabilities of local nodes to efficiently handle large datasets, creating highly accurate data-driven models for analysis and prediction purposes. However, the performance of distributed machine learning can be significantly hampered by communication bottlenecks and node dropouts. In this article, a novel unmanned aerial vehicle (UAV)-enabled hierarchical distributed learning architecture is proposed to support machine learning applications, e.g., regional monitoring. Multiple UAV receivers (URs) are introduced as wireless relays to improve the communication between the UAV transmitters (UTs) and the cloud server. Our objective is to identify the optimal UT-UR association to maximize the social welfare of the network, which is distinctly different from the existing works that focus on the unilateral profit-maximizing problem. We formulate a two-side many-to-one matching game to model the UT-UR association problem, and a two-phase many-to-one matching algorithm is designed to identify the stable matching. The validity of our proposed scheme is verified through in-depth numerical simulations.

Research on weighted energy consumption and delay optimization algorithm based on dual‐queue model

AbstractThis article investigates a mobile edge computing (MEC) network assisted by multiple unmanned aerial vehicles (UAVs) to address the computational and offloading requirements for mobile intelligent terminals (MITs) within crowded venues. The objective is to tackle intricate task processing and diminish MITs' waiting times. Considering the randomness of task arrival at the MITs and the imbalance between the amount of data and computation for complex tasks, a dual‐queue model with data cache queue and computation queue is proposed, with minimizing the weighted system total energy consumption and average delay as the optimization objectives. Lyapunov optimization theory is employed to convert the stochastic optimization problem into a deterministic one, and the initial deployment quantity and hovering position of the UAVs are determined by the density‐based spatial clustering of applications with noise (DBSCAN) method with noise. Then PPO algorithm for MIT task, resource allocation, and UAV trajectory optimization. Numerical results display the proposed scheme can efficaciously diminish energy consumption and delay by 10% and 33% respectively, compared with the baseline scheme. This paper proposes a practical and feasible solution for stochastic computing offloading in UAV‐assisted MEC, which fills the gap in existing research on regarding the consideration of complex task imbalances.

Decentralized control and state estimation of a flying parallel robot interacting with the environment

Unmanned Aerial Vehicles (UAVs) have great potential to achieve a variety of tasks remotely such as aerial grasping, transporting and manipulating objects. Architectures with multiple UAVs have further enhanced the payload capacity and manipulability of these robots, for instance a Flying Parallel Robot (FPR) where a moving platform is cooperatively supported by multiple quadrotors with passive rigid links. In this paper, we address the vision-based state estimation and decentralized control applied to the multi-UAV parallel robot, taking the FPR as an example. An ArUco marker system is applied to estimate the relative pose of each UAV with respect to the common platform frame, along with the Extended Kalman Filter to reconstruct the robot state without the dependence on any external localization system. The interaction controller is then deployed in a decentralized manner, which is potentially more robust to communication delays or interruptions. The proposed methodology has been validated by real-time experiments demonstrating the teleoperation of the FPR interacting with the environment.

Equilibrium optimizer with generalized opposition-based learning for multiple unmanned aerial vehicle path planning

Equilibrium optimizer with generalized opposition-based learning for multiple unmanned aerial vehicle path planning

SEAMLINE OPTIMIZATION FOR UAV IMAGE MOSAICKING USING GEOMETRY OF TRIANGULATED IRREGULAR NETWORK

Abstract. For efficient UAV (Unmanned Aerial Vehicle) image monitoring, it is essential to mosaic multiple UAV images into one seamless image. In a mosaicked image, relief displacement of terrain is a major source of error. It is difficult to form seamlines that avoid all areas of relief displacements. A seamline determination method alone is limited in reducing the mismatch error on mosaicked image. In this study, we constructed a TIN (Triangulated Irregular Network) using tiepoints generated by rigorous bundle adjustments. We detected the regions where relief displacement occurred using the slope of TIN facets. It was found that the error of mosaicked image were mostly on TIN facets with high slopes. Our method generated a mosaicked image after elimination of error-prone region and showed that the distortions were effectively removed. This study showed that the proposed method could produce mosaicked images with stable quality using geometric clues of TIN. We expected that our method can be used for UAV image mosaicking robust to mismatching factors.

Adaptive event-triggered pseudolinear consensus filter for multi-UAVs bearings-only target tracking

An improved version of the pseudolinear filter is proposed, namely an adaptive event-triggered instrumental variable-based pseudolinear information consensus filter, with a special focus on three-dimensional(3D) unbiased target tracking with multiple unmanned aerial vehicles (UAVs) bearings-only measurements under communication faults. To deal with the unknown communication faults, a credibility-based weighted average consensus rule is constructed, which considers the network topology and the information quality of UAVs. Furthermore, an adaptive event-triggered mechanism is designed to reduce the computation burden and communication bandwidth. The selective-angle-measurement strategy (SAMS)-based instrumental variable is integrated into the pseudolinear information consensus filter to achieve unbiased estimates. It is proved that the estimates of all UAVs will eventually achieve consistency, and its estimation errors are bounded. Finally, numerical simulations are given to verify the advantages of the proposed filtering algorithm in terms of enhanced accuracy and faster convergence rates.

Reinforcement learning-based formation-surrounding control for multiple quadrotor UAVs pursuit-evasion games

This paper proposes a reinforcement learning-based formation-surrounding control method for multiple quadrotor unmanned aerial vehicles (UAVs) pursuit-evasion (MPE) games system subject to external disturbances. In the framework of the MPE games, the pursuers aim to equally surround the evaders which try to avoid being surrounded when forming the desired formation. By constructing position and attitude tracking error subsystems of quadrotor UAV, this paper proposes two control strategies which combines the feedforward control technique and reinforcement learning (RL) method. First, two novel cost functions are presented for the quadrotor UAV with external disturbances. Then, two control schemes based on RL have been developed to guarantee the stability of the tracking error subsystem. Subsequently, two critic-only neural networks (NN) weight update laws that only satisfy finite excitation conditions are proposed to estimate the optimal cost function. Furthermore, Nash equilibrium for multiple quadrotor UAVs is achieved by means of RL strategy to solve the Hamilton-Jacobi-Isaacs (HJI) equations. And the property of equally surrounding is proved for the first time by utilizing Euler's formula in this paper. Finally, the numerical simulation results are given to show the effectiveness and superior performance of the proposed control method.

On the Interplay Between Sensing and Communications for UAV Trajectory Design

The unmanned aerial vehicles (UAVs) are envisioned as promising aerial facilities for providing advanced communication services as well as sensing functionalities in the next-generation wireless system. This paper considers a UAV-enabled integrated sensing and communications (ISAC) system, where a moving ground user (GU) is simultaneously tracked by multiple UAVs and receives the downlink communication information transmitted from the UAV. In particular, to jointly enhance the sensing and communication (S&C) performance, optimizing the UAV moving trajectory is demanded. To achieve this goal, we first harness the extended Kalman filtering (EKF) method for predicting and tracking the motion parameters of GU at each time slot, which relies on the range measurements extracted from the sensing echoes at the base station (BS). Afterward, we formulate a weighted optimization problem that addresses the design of UAV trajectories and GU-UAV association simultaneously, incorporating the consideration of real-time downlink communication rates and the Cramér-Rao bound (CRB) for GU tracking. The problem further is constrained by the maximum consumed power, maximum traveling distance, and minimum collision avoidance distance. As a step forward, to address the resultant non-convex problem, we develop an efficient iterative algorithm to obtain a near-optimal solution by utilizing the successive convex approximate (SCA) technique. Specifically, we alternately solve the GU-UAV association and the real-time trajectory design problem at each time slot. Finally, our numerical simulations illustrate that our proposed algorithm can track the GU accurately while meeting the sensing-centric and/or communication-centric requirements.

Online Distributed Relative Positioning Utilizing Multiple Cooperative Autonomous Agents

Currently, an unmanned aerial vehicle (UAV) utilizes global navigation satellite systems (GNSS) in conjunction with other modalities for localization purposes. Nevertheless, this approach faces robustness issues when GNSS signals become unavailable or sensors malfunction. Clearly, the robustness of the system increases considerably when multiple UAV agents are employed to perform collaborative positioning. In this work, an online distributed solution is proposed for relative localization, which incorporates multiple UAVs together with Signals of Opportunity (SOPs) as well as inertial, visual, and optical flow measurements. The proposed localization system includes relative self-localization of each UAV agent, as well as a reliable distributed relative positioning system (DRPS) for each UAV based on the relative positions from other UAV agents in its vicinity. The latter positioning strategy is required in case the relative self-localization fails, mainly due to such problems as inertial measurement unit (IMU) accumulated error drift, camera sensor errors, or SOP shortfalls due to multipath or antenna obstruction. Extensive field experiments validate the proposed technique and demonstrate increased localization accuracy and robustness when compared to the benchmark approach that does not include cooperation between UAVs.

Distributed Adaptive Fault-Tolerant Formation-Containment Control With Prescribed Performance for Heterogeneous Multiagent Systems.

This article proposes a distributed adaptive fault-tolerant formation-containment control with prescribed performance for heterogeneous multiagent systems (MASs) consisting of multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults. First, utilizing the neighborhood formation error information, the distributed fault-tolerant formation control strategy is developed for the trajectory dynamics of each UAV to achieve the formation tracking, that is, all UAVs track the virtual leader and perform the prespecified formation configuration. Then, the adaptive fault-tolerant containment algorithm, independent of the positions of the leaders, is proposed to guarantee the UGVs converge to the convex hull formed by the leader UAVs. The adaptive estimation scheme is constructed to compensate for the unknown system parameters and actuator loss-of-effectiveness and bias faults. The formation-containment tracking performance is analyzed based on Lyapunov theory with the synchronization errors satisfying the prescribed performance. A simulation example based on UAVs-UGVs systems is adopted to verify the effectiveness of the proposed control strategy.

Hierarchical Decision-Making Framework for Multi-UAV Task Assignment via Enhanced Pigeon-Inspired Optimization

Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems. However, in the development of feasible plans, challenges stemming from extensive and prolonged task requirements are encountered. This paper establishes a decision-making framework for multiple unmanned aerial vehicles (multi-UAV) based on the well-known pigeon-inspired optimization (PIO) algorithm. By addressing the problem from a hierarchical structural perspective, the initial stage involves minimizing the global objective of the flight distance cost after obtaining the entire task distribution and task requirements, utilizing the global optimization capability of the classical PIO algorithm to allocate feasible task spaces for each UAV. In the second stage, building upon the decisions made in the preceding stage, each UAV is abstracted as an agent maximizing its own task execution benefits. An improved version of the PIO algorithm modified with a sine-cosine search mechanism is proposed, enabling the acquisition of the optimal task execution sequence. Simulation experiments involving two different scales of UAVs validate the effectiveness of the proposed methodology. Moreover, dynamic events such as UAV damage and task changes are considered in the simulation to validate the efficacy of the two-stage framework.

Cooperative Visual–Range–Inertial Navigation for Multiple Unmanned Aerial Vehicles

Cooperative Visual–Range–Inertial Navigation for Multiple Unmanned Aerial Vehicles

Toward Smart Traffic Management With 3D Placement Optimization in UAV-Assisted NOMA IIoT Networks

Next generation networks will involve huge number of industrial internet of things (IIoT) sensors which require reliable connectivity with low latency to manage the data transmission and processing. The design of these networks entails a lot of challenges. This article describes the 3D placement of multiple unmanned aerial vehicles (UAVs) in an IIoT network that supports non-orthogonal multiple access (NOMA). UAVs act as decode and forward (DF) relays. The 3D UAV placement problem is formulated which is highly non-convex in the coordinates. Therefore, we employ an improved adaptive whale optimization algorithm (IAWOA) to handle the problem. Even with its improved performance, IAWOA is not suitable for real-time application. Hence, we propose path aggregation network (PANet) to handle the 3D UAV placement. The simulation results show that PANet is more suitable for the online-learning.