Multiple UAVs Research Articles

Joint Deployment and Power Optimization for UAV Relay in Multiuser Networks

Within the UAV network, the UAV first receives signals from multiple remote mobile devices (MDs) and then amplifies and forwards the transmitted signals to the base station (BS) with different amplification coefficients to form a UAV relay multiuser network. In this paper, we propose a new method to solve the problem of maximizing the throughput of the relay network. The proposed problem is decoupled into two subproblems, UAV deployment design and amplification coefficient optimization, to be solved iteratively, respectively. We solve the UAV deployment problem by adjusting its trajectory with a gradient descent-based method and solve the amplification coefficient subproblem with a convex optimization-based method iteratively. Simulation shows that the proposed UAV deployment and amplification coefficient of the UAV optimization design algorithm significantly improves the sum-rate compared with existing fixed relay and equal power allocation schemes. Finally, we discuss future potential performance enhancing methods including multiple UAV cooperation, massive multi-input multioutput (MIMO) communications, and nonorthogonal multiple access (NOMA) communications.

Multi-Target Association for UAVs Based on Triangular Topological Sequence

Multi-UAV cooperative systems are highly regarded in the field of cooperative multi-target localization and tracking due to their advantages of wide coverage and multi-dimensional perception. However, due to the similarity of target visual characteristics and the limitation of UAV sensor resolution, it is difficult for UAVs to correctly distinguish targets that are visually similar to their associations. Incorrect correlation matching between targets will result in incorrect localization and tracking of multiple targets by multiple UAVs. In order to solve the association problem of targets with similar visual characteristics and reduce the localization and tracking errors caused by target association errors, based on the relative positions of the targets, the paper proposes a globally consistent target association algorithm for multiple UAV vision sensors based on triangular topological sequences. In contrast to Siamese neural networks and trajectory correlation, the relative position relationship between targets is used to distinguish and correlate targets with similar visual features and trajectories. The sequence of neighboring triangles of targets is constructed using the relative position relationship, and the feature is a specific triangular network. Moreover, a method for calculating topological sequence similarity with similar transformation invariance is proposed, as well as a two-step optimal association method that considers global objective association consistency. The results of flight experiments indicate that the algorithm achieves an association accuracy of 84.63%, and that two-step association is 12.83% more accurate than single-step association. Through this work, the multi-target association problem with similar or even identical visual characteristics can be solved in the task of cooperative surveillance and tracking of suspicious vehicles on the ground by multiple UAVs.

Denial of Service Attack of QoS-Based Control of Multi-Agent Systems

This paper presents a secure formation control design of multi-agent systems under denial of service (DoS) attacks. Multiple unmanned aerial vehicle systems (UAVs) are considered in this paper. The proposed technique takes into account communication time delay, as well as formation and cyberattack, and provides a robust guidance method as well as a reliable middleware for information transfer and sharing. To ensure optimal guidance and coordination, a combined approach of L1 adaptive control and graph theory is used. The packet transmission between all UAVs is handled by the data distribution services (DDS) middleware, which overcomes the interoperability problem when dealing with multiple UAVs of different platforms and can be considered as an extra security level based on its quality of service (QoS). The graph theory is utilized to coordinate multiple UAVs in a hexagon formation, while the L1 controller is utilized as a local controller to stabilize the UAV’s dynamic model. A robust control security level is built to handle the effect of cyberattacks based on linear matrix inequalities (LMIs) control. Simulations are used to verify and show the performances of the proposed technique under the conditions indicated earlier.

Adaptive Leader-Following Consensus Tracking Control of Multiple UAVs Subject to Deception Attacks

This article is concerned with the problem of leader-following consensus tracking control for multi-unmanned aerial vehicle (UAV) systems under deception attacks and actuator saturation. While guaranteeing the expected control performance, a novel adaptive event-triggered scheme (AETS) is developed to reduce the frequency of data transmission among UAVs and reduce the release of redundant data, where the adaptive threshold can be adjusted online according to the dynamic error instead of giving a default value. With the help of a new Lyapunov function, sufficient conditions and security criteria are developed to ensure that all following UAVs could reach secure consensus asymptotically with a leader UAV. Lastly, an illustrative example is presented to validate the effectiveness of the designed method.

Cloud DEVS-based computation of UAVs trajectories for search and rescue missions

ABSTRACT This paper presents a new Cloud-deployable DEVS-based framework for optimising UAV trajectories and sensor strategies in target-search missions. DEVS provides it with a well-established, flexible, and verifiable modelling strategy to include different models for the UAV, sensor, and target dynamics; the target and sensor uncertainty; and the optimising process. Its Cloud deployability speeds up the evaluations/simulations required to optimise this NP-hard problem, which involves computationally heavy models when solving real-world missions. The framework, designed to handle different types of target-search missions, currently optimises, using a multi-objective Genetic Algorithm, free-shape trajectories of multiple UAVs,eqquiped with several static/movable sensors to detect a target within a search area. It is implemented in xDEVS and deployable over a set of containers in the Google Cloud Platform. The results show that our deployment policy speeds up the computation up to 3.35 times, letting the operator simultaneously optimise several search strategies for agiven scenario.

On the performance evaluation of object classification models in low altitude aerial data.

This paper compares the classification performance of machine learning classifiers vs. deep learning-based handcrafted models and various pretrained deep networks. The proposed study performs a comprehensive analysis of object classification techniques implemented on low-altitude UAV datasets using various machine and deep learning models. Multiple UAV object classification is performed through widely deployed machine learning-based classifiers such as K nearest neighbor, decision trees, naïve Bayes, random forest, a deep handcrafted model based on convolutional layers, and pretrained deep models. The best result obtained using random forest classifiers on the UAV dataset is 90%. The handcrafted deep model's accuracy score suggests the efficacy of deep models over machine learning-based classifiers in low-altitude aerial images. This model attains 92.48% accuracy, which is a significant improvement over machine learning-based classifiers. Thereafter, we analyze several pretrained deep learning models, such as VGG-D, InceptionV3, DenseNet, Inception-ResNetV4, and Xception. The experimental assessment demonstrates nearly 100% accuracy values using pretrained VGG16- and VGG19-based deep networks. This paper provides a compilation of machine learning-based classifiers and pretrained deep learning models and a comprehensive classification report for the respective performance measures.

Motor-Level N-MPC for Cooperative Active Perception With Multiple Heterogeneous UAVs

This letter introduces a cooperative control framework based on Nonlinear Model Predictive Control (NMPC) for solving an Active Information Acquisition problem (AIA) using a system of multiple multirotor UAVs equipped with onboard sensors. The observation task of the NMPC is a minimum-uncertainty pose estimation of a moving feature which is observed by the multi-UAV system, using a cooperative Kalman filter. The controller considers a full nonlinear model of the multirotors – including the motor-level actuation units and their real constraints in terms of maximum torque – and embeds the Kalman filter estimation uncertainty in its prediction. The framework allows and exploits heterogeneity in the actuation and sensing systems by considering a generic model of UAV – including both quadrotors and tilted-propeller multirotors – and a generic model of range-and-bearing sensor with arbitrary rate and field of view. The capability of the proposed framework to reduce the cooperative estimation uncertainty of a static or a moving feature, thus leading the system to optimal sensing configurations, is demonstrated through Gazebo simulations and real experiments. The software is provided open-source.

Radial basis function-based vector field algorithm for wildfire boundary tracking with UAVs

This paper tackles the problem of dynamic wildfire boundary tracking with UAVs. Wildfire boundary is treated as the zero-level set curve of an implicit function and is approximated with radial basis functions. Its propagation is modeled with the Hamilton–Jacobi equation with an arbitrary initial boundary as the input. To navigate UAVs to the wildfire boundary, an analytical velocity vector field, whose integral curves converge to the wildfire boundary, is constructed on the basis of the typical radial basis function thin-plate spline. Computer simulations with a single UAV and multiple UAVs have been conducted for the evaluation of the proposed solution, and numerical results show that the proposed algorithm can ensure the successful tracking of an arbitrarily shaped wildfire boundary.

Safe, Fast, and Efficient Distributed Receding Horizon Constrained Control of Aerial Robot Swarms

This letter proposes a reactive, safe, and high-performance distributed constrained control law for ensuring safety in multi-UAV systems. The UAVs possess higher-order nonlinear dynamics and are subject to actuation and collision avoidance constraints. To address the case of real-time multi-UAV collision avoidance, we propose a novel Trajectory-Tube Distributed Explicit Reference Governor (TT-D-ERG) approach based on the idea of embedding future state trajectories into safety tubes. The control law is ideally suited for systems with limited on-board hardware for computation and communication. The safety, efficacy, performance, and scalability of the method are demonstrated and benchmarked against state-of-the-art real-time collision avoidance methods by simulations and field experiments with multiple quadrotor UAVs (videos at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/V4P1O8vhKlo</uri> ).

Dynamic Task Allocation of Multiple UAVs Based on Improved A-QCDPSO

With the rapid changes in the battlefield situation, the requirement of time for UAV groups to deal with complex tasks is getting higher, which puts forward higher requirements for the dynamic allocation of the UAV group. However, most of the existing methods focus on task pre-allocation, and the research on dynamic task allocation technology during task execution is not sufficient. Aiming at the high real-time requirement of the multi-UAV collaborative dynamic task allocation problem, this paper introduces the market auction mechanism to design a discrete particle swarm algorithm based on particle quality clustering by a hybrid architecture. The particle subpopulations are dynamically divided based on particle quality, which changes the topology of the algorithm. The market auction mechanism is introduced during particle initialization and task coordination to build high-quality particles. The algorithm is verified by constructing two emergencies of UAV sudden failure and a new emergency task.

Fast Multi-UAV Path Planning for Optimal Area Coverage in Aerial Sensing Applications.

This paper deals with the problems and the solutions of fast coverage path planning (CPP) for multiple UAVs. Through this research, the problem is solved and analyzed with both a software framework and algorithm. The implemented algorithm generates a back-and-forth path based on the onboard sensor footprint. In addition, three methods are proposed for the individual path assignment: simple bin packing trajectory planner (SIMPLE-BINPAT); bin packing trajectory planner (BINPAT); and Powell optimized bin packing trajectory planner (POWELL-BINPAT). The three methods use heuristic algorithms, linear sum assignment, and minimization techniques to optimize the planning task. Furthermore, this approach is implemented with applicable software to be easily used by first responders such as police and firefighters. In addition, simulation and real-world experiments were performed using UAVs with RGB and thermal cameras. The results show that POWELL-BINPAT generates optimal UAV paths to complete the entire mission in minimum time. Furthermore, the computation time for the trajectory generation task decreases compared to other techniques in the literature. This research is part of a real project funded by the H2020 FASTER Project, with grant ID: 833507.

UAV-assisted cooperative offloading energy efficiency system for mobile edge computing

Reliable communication and intensive computing power cannot be provided effectively by temporary hot spots in disaster areas and complex terrain ground infrastructure. Mitigating this has greatly developed the application and integration of UAV and Mobile Edge Computing (MEC) to the Internet of Things (IoT). However, problems such as multi-user and huge data flow in large areas, which contradict the reality that a single UAV is constrained by limited computing power, still exist. Due to allowing UAV collaboration to accomplish complex tasks, cooperative task offloading between multiple UAVs must meet the interdependence of tasks and realize parallel processing, which reduces the computing power consumption and endurance pressure of terminals. Considering the computing requirements of the user terminal, delay constraint of a computing task, energy constraint, and safe distance of UAV, we constructed a UAV-Assisted cooperative offloading energy efficiency system for mobile edge computing to minimize user terminal energy consumption. However, the resulting optimization problem is originally nonconvex and thus, difficult to solve optimally. To tackle this problem, we developed an energy efficiency optimization algorithm using Block Coordinate Descent (BCD) that decomposes the problem into three convex subproblems. Furthermore, we jointly optimized the number of local computing tasks, number of computing offloaded tasks, trajectories of UAV, and offloading matching relationship between multi-UAVs and multiuser terminals. Simulation results show that the proposed approach is suitable for different channel conditions and significantly saves the user terminal energy consumption compared with other benchmark schemes.

Hybrid georeferencing of images and LiDAR data for UAV-based point cloud collection at millimetre accuracy

During the last two decades, UAV emerged as standard platform for photogrammetric data collection. Main motivation in that early phase was the cost effective airborne image collection at areas of limited size. This was already feasible by rather simple payloads like an off-the-shelf, compact camera and a navigation-grade GNSS sensor. Meanwhile, dedicated sensor systems enable applications that have not been feasible in the past. One example is the airborne collection of dense 3D point clouds at millimetre accuracies, which will be discussed in our paper. For this purpose, we collect both LiDAR and image data from a joint UAV platform and apply a so-called hybrid georeferencing. This process integrates photogrammetric bundle block adjustment with direct georeferencing of LiDAR point clouds. By these means georeferencing accuracy is improved for the LiDAR point cloud by an order of magnitude. We demonstrate the feasibility of our approach in the context of a project, which aims on monitoring of subsidence of about 10 mm/year. The respective area of interest is defined by a ship lock and its vicinity of mixed use. In that area, multiple UAV flights were captured and evaluated for a period of three years. As our main contribution, we demonstrate that 3D point accuracies at sub-centimetre level can be achieved. This is realized by joint orientation of laser scans and images in a hybrid adjustment framework, which enables accuracies corresponding to the GSD of the captured imagery.

A new hybrid algorithm based on golden eagle optimizer and grey wolf optimizer for 3D path planning of multiple UAVs in power inspection

A new hybrid algorithm based on golden eagle optimizer and grey wolf optimizer for 3D path planning of multiple UAVs in power inspection

Autonomous Cooperative Multi-Vehicle System for Interception of Aerial and Stationary Targets

This paper presents the design, development, and testing of hardware-software systems by the IISc-TCS team for Challenge 1 of the Mohamed Bin Zayed International Robotics Challenge 2020. The goal of Challenge 1 was to grab a ball suspended from a maneuvering UAV and pop balloons anchored to the ground, using suitable manipulators. The important tasks carried out to address this challenge include the design and development of a hardware system with efficient grabbing and popping mechanisms, considering the restrictions in volume and payload, design of accurate target interception algorithms using visual information suitable for outdoor environments, and development of a software architecture for dynamic, multi-agent, aerial systems performing complex tasks. In this paper, we discuss the design of a custom end-effector mounted on a single degree of freedom manipulator, and robust algorithms for the interception of targets in an uncertain environment. Vision-based guidance and tracking strategies are developed based on the concept of pursuit engagement and artificial potential function. The software architecture presented in this work develops an Operation Management System (OMS) architecture that allocates static and dynamic tasks collaboratively among multiple UAVs to perform any given task. An important aspect of this work is that all the systems developed were designed to operate in completely autonomous mode. A detailed description of the architecture along with simulations of complete challenge in the Gazebo environment and field experiment results are also included in this work. The developed hardware-software system is useful for counter-UAV systems and can be used for other applications.

Computation Offloading in Multi-UAV-Enhanced Mobile Edge Networks: A Deep Reinforcement Learning Approach

In this paper, we investigate an unmanned aerial vehicle- (UAV-) enhanced mobile edge computing network (MUEMN), where multiple UAVs are deployed as aerial edge servers to provide computing services for ground moving equipment (GME). Each GME is trained to simulate movement by a Gauss-Markov random model in this MUEMN. Under the condition of limited energy cost, UAV dynamically plans its flight position according to the movement trend of GME. Our objective is to minimize the total energy consumption of GME by jointly optimizing the offloading decisions of GME and the flight positions of UAVs. More explicitly, we model the optimization problem as a Markov decision process and achieve real-time offloading decisions via deep reinforcement learning algorithm according to the dynamic system state, where the asynchronous advantage actor-critic (A3C) framework with asynchronous characteristics is leveraged to accelerate the learning process. Finally, numerical results confirm that our proposed A3C-based offloading strategy can effectively reduce the total of energy consumption of GME and ensure the continuous operation of the GME.

Three-dimensional piecewise guidance strategy for multi-UAVs guaranteeing simultaneous arrival and field-of-view constraint

This paper presents a three-dimensional (3D) piecewise guidance scheme for multiple UAVs) guaranteeing simultaneous arrival even under the field-of-view (FOV) constraint. The guidance law does not require to consider the estimation of the time-to-go and can be divided into two stages: cooperative stage and proportional navigation guidance (PNG) stage. In the cooperative stage, a decentralized consensus control approach is proposed for multi-UAV systems and does not rely on global information about the communication topography. Moreover, the neighbors’ new auxiliary states are introduced to ensure the states achieving consensus under FOV constrain. The guidance strategy transfers to the PNG stage while the UAV formation is close enough to the target and the simultaneous arrival to hit the target is guaranteed for UAVs via the PNG guidance law. Numerical simulation results demonstrate the effectiveness of the proposed cooperative guidance law.

Real-time formation control and obstacle avoidance algorithm for fixed-wing UAVs

AbstractThis paper proposes a novel real-time formation control and obstacle avoidance algorithm for multiple fixed-wing UAVs. A formation control algorithm is designed by a combination of the virtual structure, leader-follower, and artificial potential fields methods and harnessing the advantages of those approaches. The kinematic and dynamic constraints of fixed-wing UAVs are considered in the path planning. The performance of the proposed algorithm is examined through simulation in Matlab software by applying the translational dynamics of fixed-wing UAVs. Simulations of different complex scenarios demonstrate the effectiveness of the presented formation flight algorithm through generating multiple efficient paths, which are fully consistent with the functional constraints of the UAVs in the presence of obstacles.

Towards Design of an Efficient Sensing Data Acquisition Scheme for UAVs-Assisted Wireless Sensor Networks

This paper investigates sensing data acquisition issues from large-scale hazardous environments using UAVs-assisted WSNs. Most of the existing schemes suffer from low scalability, high latency, low throughput, and low service time of the deployed network. To overcome these issues, we considered a clustered WSN architecture in which multiple UAVs are dispatched with assigned path knowledge for sensing data acquisition from each cluster heads (CHs) of the network. This paper first presents a non-cooperative Game Theory (GT)-based CHs selection algorithm and load balanced cluster formation scheme. Next, to provide timely delivery of sensing information using UAVs, hybrid meta-heuristic based optimal path planning algorithm is proposed by combing the best features of Dolphin Echolocation and Crow Search meta-heuristic techniques. In this research work, a novel objective function is formulated for both load-balanced CHs selection and for optimal the path planning problem. Results analyses demonstrate that the proposed scheme significantly performs better than the state-of-art schemes.

Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping.

Unmanned aerial vehicle-based remote sensing technology has recently been widely applied to crop monitoring due to the rapid development of unmanned aerial vehicles, and these technologies have considerable potential in smart agriculture applications. Field phenotyping using remote sensing is mostly performed using unmanned aerial vehicles equipped with RGB cameras or multispectral cameras. For accurate field phenotyping for precision agriculture, images taken from multiple perspectives need to be simultaneously collected, and phenotypic measurement errors may occur due to the movement of the drone and plants during flight. In this study, to minimize measurement error and improve the digital surface model, we proposed a collaborative driving system that allows multiple UAVs to simultaneously acquire images from different viewpoints. An integrated navigation system based on MAVSDK is configured for the attitude control and position control of unmanned aerial vehicles. Based on the leader–follower-based swarm driving algorithm and a long-range wireless network system, the follower drone cooperates with the leader drone to maintain a constant speed, direction, and image overlap ratio, and to maintain a rank to improve their phenotyping. A collision avoidance algorithm was developed because different UAVs can collide due to external disturbance (wind) when driving in groups while maintaining a rank. To verify and optimize the flight algorithm developed in this study in a virtual environment, a GAZEBO-based simulation environment was established. Based on the algorithm that has been verified and optimized in the previous simulation environment, some unmanned aerial vehicles were flown in the same flight path in a real field, and the simulation and the real field were compared. As a result of the comparative experiment, the simulated flight accuracy (RMSE) was 0.36 m and the actual field flight accuracy was 0.46 m, showing flight accuracy like that of a commercial program.