Problem Of Unmanned Aerial Vehicles Research Articles

Multi-UAV Path Planning for Inspection of Target Points with Stable Monitoring Frequencies

In this study, we focus on the path-planning problem of unmanned aerial vehicles (UAVs) deployed for inspection missions at target points. The goal is to visit each target point, provide revisits to important target points, and ultimately meet the monitoring requirements with regular and stable monitoring frequencies. Herein, we present MTSP-R, a novel variant of the multiple traveling salesmen problem (MTSP), in which revisits to important target points are allowed. We address the path-planning problem of multi-UAV in two stages. First, we propose a nearest insertion algorithm with revisits (NIA-R) to determine the number of required UAVs and initial inspection paths. We then propose an improved genetic algorithm (IGA) with two-part chromosome encoding to further optimize the inspection paths of the UAVs. The simulation results demonstrate that the IGA can effectively overcome the shortcomings of the original genetic algorithm, providing shorter paths for multiple UAVs and more stable monitoring frequencies for the target points.

Collision-free trajectory planning for UAVs based on sequential convex programming

This paper proposes an optimization-based approach to solve the collision-free trajectory planning problem for unmanned aerial vehicles (UAVs) with nonlinear dynamical systems. The problem is modeled as a non-convex optimization problem, and then iteratively solved by sequential convex programming algorithm which approximates non-convex constraints by convexification methods. We linearize the dynamical equations of the UAV and penalize collision-free constraints with a hinge loss. Furthermore, a variable trust region method is employed to ensure the convergence and stability of the algorithm. We apply the algorithm to three-degree-of-freedom UAV case studies. Numerical results validate that our method can provide collision-free trajectories with a 27.19% reduction in total flight time and a 93.61% reduction in computation time on average compared with the genetic algorithm method.

Distributed time-varying Nash equilibrium in resilient multi-objective formation control for cyber–physical systems

This paper investigates the problem of multi-objective time-varying formation control in cyber–physical systems (CPSs) with saturated constraints and malicious agents. By incorporating the concept of multi-agent systems and employing objective weight allocation method, the problem is reformulated as the seeking for the Nash equilibrium solution in a time-varying non-cooperative game. To evaluate the trustworthiness of agent behavior, we propose an adaptive weight learning algorithm based on reinforcement learning. Furthermore, we develop a novel smooth saturation control strategy and a predefined time controller to design a distributed resilient algorithm for CPSs with both known and unknown parameters. Additionally, we apply the designed algorithm to the resilient distributed multi-objective time-varying formation control problem of unmanned aerial vehicles (UAVs) to verify its effectiveness.

A constrained control framework for unmanned aerial vehicles based on Explicit Reference Governor

This paper tackles the constrained control problem of unmanned aerial vehicles with planar multirotors. The proposed solution splits the constrained control problem into two separate tasks, i.e. stabilization and constraint enforcement. It is shown that the problems addressed by each individual layer is much simpler than the original combined problem. For the unconstrained control of UAVs we consider a control scheme based on a cascade structure. The Lyapunov function for the stabilized cascaded system is then derived. Using this Lyapunov function, we develop an Explicit Reference Governor for constraint enforcement. Numerical simulation shows the effectiveness of the proposed approach.

UAVs Path Planning based on Combination of Rapidly Exploring Random Tree and Rauch-Tung-Striebel Filter

Aiming at the problem of Unmanned Aerial Vehicle(UAV) formation path planning under complex constraints, a UAV formation path planning method based on the combination of Rapidly exploring Random Tree (RRT) and Rauch-Tung-Striebel (RTS) filter is proposed. Firstly, a path planning algorithm based on the improved RRT algorithm with adaptive step size is de-signed to solve the problem that the RRT algorithm is easy to fall into local optimum. Then, an RTS filter is introduced to smooth the trajectory planned by the improved RRT algorithm to achieve curvature continuity. Finally, taking the smooth trajectory as the reference, a UAV formation path planning algorithm over the Artificial Potential Field (APF) method is designed. The simulation results show that the designed UAV formation path planning algorithm can solve the planning problems of single trajectory and formation trajectories in complex constrained space, and can plan the formation trajectory with continuous curvature, to facilitate the UAV trajectory tracking control.

Mathematical Optimization in Innovation Productivity: A Framework and A Case Study on UAV Border Patrolling in Türkiye

Purpose: In this paper, the potential of mathematical optimization (MO) in enhancing innovation productivity is explored. Innovation is a process that converts new ideas and methods into products and services, MO can contribute to innovation management by improving productivity across all stages, from pre-innovation to post-innovation. This paper establishes a connection between MO and innovation productivity while demonstrating an application for a post-innovation phase problem of unmanned aerial vehicles (UAVs). Methodology: A framework for incorporating MO into the design problems of innovation processes is developed. Additionally, a MO model is developed for a case study concerning UAV border patrolling in Türkiye. Findings: Computational experiments demonstrate MO's effectiveness in optimizing UAV routes and strategies, enhancing operational efficiency, and innovation productivity. Optimal recommendations and trade-offs among different mission considerations are obtained in 18 minutes on average (with a median of 5 seconds) over 210 runs. Originality: A link is established between MO and innovation productivity. An operations research problem is introduced for UAV operations in border patrolling in Türkiye. The codebase and data are openly provided for readers to apply the model in their research.

Grouping Formation and Obstacle Avoidance Control of UAV Swarm Based on Synchronous DMPC

This paper focuses on the grouping formation control problem of unmanned aerial vehicle (UAV) swarms in obstacle environments. A grouping formation and obstacle avoidance control algorithm based on synchronous distributed model predictive control (DMPC) is proposed. First, the UAV swarm is divided into several groups horizontally and into a leader layer and a follower layer vertically. Second, tracking is regarded as the objective, and collision avoidance and obstacle avoidance are considered as constraints. By combining the velocity obstacle method with synchronous DMPC and providing corresponding terminal components, a leader layer control law is designed. The control law can enable the UAV swarm to track the target while avoiding collisions and dynamic obstacles. Then, considering the formation maintenance term, based on different priorities, member-level obstacle avoidance and group-level obstacle avoidance strategies are proposed, and the corresponding follower layer control laws are provided. Furthermore, the stability of the UAV swarm system under the control algorithm is demonstrated based on the Lyapunov theory. Finally, the effectiveness of the designed algorithm and its superiority in obstacle avoidance are verified through simulations.

Generative adversarial network based resource optimization for UAV jamming assisted cognitive covert communication

This paper discusses a resource optimization problem for unmanned aerial vehicle (UAV) jamming assisted cognitive covert communication. Specially, the secondary users adopt underlay strategy for spectrum access and the UAV transmits the noise signal to confuse the underlay detection of eavesdropper. According to the dynamic game characteristics in the covert communication process, a new generative adversarial network-based covert communication resource optimization algorithm (GAN-RO) is proposed for the joint optimization of UAV’s trajectory and power in cognitive covert communication assisted by UAV’s interference. The generator simulates the legitimate user to generate the resource allocation scheme, while the discriminator imitates the eavesdropper to detect covert communication. The dynamic game process between the generator and discriminator can obtain covert communication schemes by achieving Nash equilibrium. Simulation results indicate that the proposed GAN-RO algorithm can obtain a near-optimal resource optimization scheme with a rapid converge speed for non-ideal UAV jamming assisted cognitive covert communication system.

Disturbance rejection based on discrete-time adaptive preview control for unmanned aerial vehicle shipboard landing under structure damage

This article studies the shipboard landing control problem for unmanned aerial vehicle with external disturbance and structure damage. A novel discrete-time adaptive preview architecture is presented, where the model reference adaptive control is improved by introducing the preview control method, so that both the uncertainties (caused by the airwake and structure damage) and the control lag could be well compensated simultaneously. The proposed scheme guarantees a more rapid, accurate, and robust performance. For application, the reference input signal is generated by fusing the deck motion prediction information and glide path command. The effectiveness of the algorithm is validated by a longitudinal landing simulation.

Dynamic output feedback control for UAVs with limited network bandwidth: A GA-assisted design approach

This paper studies the dynamic output feedback (OF) control problem for unmanned aerial vehicles (UAVs) with limited network bandwidth by a genetic-algorithm-assisted design approach. In order to make full use of the limited network bandwidth, only partial measurement information is transmitted to the controller via the sensor-to-controller network. Round-Robin protocol is introduced to administrate the transmission rule. Sufficient conditions are presented to make sure that the closed-loop UAV system is exponentially stable with the required L2-gain performance. However, due to the nonlinear coupling terms of the dynamic OF controller parameters and some redundant variables in the system modeling, the established conditions are non-convex and difficult to be solved. Fortunately, genetic algorithms (GA) show good performance in solving optimization problem with nonlinear and non-convex constraints. Therefore, this paper skillfully combines GA with linear matrix inequality techniques to solve the established conditions. Finally, all the dynamic OF controller parameters and the minimal L2-gain can be obtained simultaneously. At the end of this paper, an UAV networked control example and a numerical example are performed to verify the effectiveness and superiorities of our method.

Collision avoidance in target encirclement and tracking of unmanned aerial vehicles under a dynamic event-triggered formation control

This paper addresses the dynamic event-triggered fixed-time distributed control problem of unmanned aerial vehicle (UAV) formation for target encirclement and tracking with collision avoidance. A fixed-time radial basis function (RBF) neural network (NN) adaptive disturbance observer is designed for faster estimation and compensation of the lumped disturbances from the UAV formation system. By extending the lemmas of interconnected systems and fixed-time, a novel fixed-time interconnected system lemma is proposed. Then, a dynamic event-triggered control algorithm is proposed to establish the stability of the complex closed-loop interconnected system for target encirclement and formation tracking in fixed time and to demonstrate that the convergence time of the system is independent of the initial state. Subsequently, a risk avoidance mechanism is incorporated into the event-triggered fixed-time control framework to avoid collisions within the UAV formation. The proposed control strategy has been rigorously mathematically demonstrated. Finally, using a visual simulation platform, several simulations are presented to illustrate the remarkable performance and superiority of the proposed control method.

Credible Gaussian sum cubature Kalman filter based on non-Gaussian characteristic analysis

Toward the pose estimation problem of unmanned aerial vehicles (UAVs) flight over water, a credible Gaussian-sum cubature Kalman filter (CGSCKF) based on non-Gaussian characteristic analysis for nonlinear non-Gaussian system is proposed. In the framework of the proposed scheme, a data characteristic analysis based on multi-peak distribution degree, skewness and kurtosis is applied to evaluate the non-Gaussian intensity of noise, and a Gaussian mixture reduction (GMR) method based on the improved monarch butterfly optimization (IMBO) is proposed to improve the filtering accuracy in the case of weak non-Gaussian noise (Gaussian-like noise). In addition, considering the problem of inconsistency between the practical and the theoretic system model, the process noise and measurement noise are estimated online, and the credibility of filtering result, which is obtained by Gaussian-sum cubature Kalman filter (GSCKF), is measured by comparing the filtering error covariance with the true error covariance. Some comparison simulations and experiments with classical algorithms are provided, justifying that the proposed scheme has better performance in the pose estimation of UAV.

Pursuit Problem of Unmanned Aerial Vehicles

The study examines scenarios involving a single pursuer tracking a single evader, as well as situations where multiple pursuers are involved in chasing multiple evaders. We formulate this problem as a search and pursuit problem for unmanned aerial vehicles (UAVs). Game theory offers a mathematical framework to model and examine strategic interactions involving multiple decision-makers. By employing game theory principles to address the search and pursuit problem, our objective is to optimize the efficiency of strategies for detecting and capturing unmanned aerial vehicles (UAVs).

Analysis of UAV path planning effectiveness and evaluation index scores combined with urban logistics scenarios

Using logistics drones for distribution is an optional solution to the problems of saturated logistics industry, insufficient transportation capacity, and high work pressure on employees in real cities. In a realistic urban environment, it is of practical significance to establish a logistics drone path planning model to improve the efficiency of logistics drones by fully considering the multiple requirements of both logistics service parties, such as time and location, and aiming at minimizing transportation costs. Aiming at this problem, this paper analyzes and studies the path planning problem of unmanned aerial vehicles based on the factors that need to be considered in actual life logistics distribution. Firstly, the actual scenario requirements of logistics UAV applications are analyzed and a path planning algorithm for this scenario is enumerated. Subsequently, mathematical models are established from both path planning and customer satisfaction to assess the performance of logistics drones. Then, a comparative analysis of four existing classical path planning algorithms is conducted, and simulated annealing algorithm is used to optimize logistics delivery scenarios for path planning. Finally, combining the mathematical model and simulated annealing algorithm constructed in the previous article, logistics UAV simulation experiments are conducted in urban distribution scenarios and an ideal path planning model is obtained. The work of this article provides a certain reference basis for logistics companies in the application of logistics UAVs and the selection of path planning methods.

Autonomous aerial obstacle avoidance using LiDAR sensor fusion.

The obstacle avoidance problem of unmanned aerial vehicle (UAV) mainly refers to the design of a method that can safely reach the target point from the starting point in an unknown flight environment. In this paper, we mainly propose an obstacle avoidance method composed of three modules: environment perception, algorithm obstacle avoidance and motion control. Our method realizes the function of reasonable and safe obstacle avoidance of UAV in low-altitude complex environments. Firstly, we use the light detection and ranging (LiDAR) sensor to perceive obstacles around the environment. Next, the sensor data is processed by the vector field histogram (VFH) algorithm to output the desired speed of drone flight. Finally, the expected speed value is sent to the quadrotor flight control and realizes autonomous obstacle avoidance flight of the drone. We verify the effectiveness and feasibility of the proposed method in 3D simulation environment.

Adaptive attitude control of UAV based on neural network compensation

Aiming at the problem of unmanned aerial vehicle (UAV) attitude control with insufficient robustness and anti-disturbance, this paper proposes a UAV attitude control method combining incremental nonlinear dynamic inverse (INDI) and neural network (NN) adaptive compensation. Firstly, the attitude model of UAV with time scale separation is proposed. On this basis, the INDI control law is derived, and the angular acceleration information required by INDI method is feedback by the filter. Then, the adaptive NN compensation structure is introduced to eliminate the uncertainty caused by model mismatch, sensor delay and external disturbance. Finally, the UAV simulation is carried out. The result shows that this method improves robustness and anti-disturbance of original INDI method without changing structure and parameters of controller, and has a certain adaptive compensation ability for system uncertainty.

Learning-Based UAV Coverage-Aware Path Planning in Large-scale Urban Environments

In our research, the coverage-aware path planning problem of unmanned aerial vehicles (UAVs) in large-scale urban environments is studied. The goal of our research is to train the intelligent UAV to autonomously cover the area with satisfactory communication quality and finally arrive at the objective place without colliding with numerous skyscrapers. Such a multi-objective path planning problem is regularly non-convex and thus hard to converge. To this end, a distributed deep reinforcement learning (DRL)-based approach called Distributed-RPPO is developed to address the problem. To be specific, firstly we formulate our proposed problem as a Markov decision process (MDP). Since communication connectivity is the core constraint, a proximal policy optimization (PPO)-based SIR prediction network is designed for guiding the UAV to cover the area with superior communication connectivity, using only the UAV’s current location. Then we combine the PPO algorithm with the Long short-term memory (LSTM) structure to solve the basic navigation problem while only the historical interactive data is exploited for updating the parameter vector of the entire network. Simulation results demonstrate that our approach is capable of allowing the UAV to execute coverage-aware path planning tasks in large-scale urban environments. Furthermore, the learning efficiency is greatly improved compared to existing DRL-based approaches.

Reinforcement learning-based multi-strategy cuckoo search algorithm for 3D UAV path planning

Unmanned aerial vehicles are applied extensively in various fields due to their advantages of low-cost, high-maneuverability, and easy-operation. However, the path planning problem of unmanned aerial vehicles, which directly determines the flight safety and efficiency, still remains challenging when building and optimizing the path model. To further study the path planning problem, we firstly construct it as a constrained optimization problem. The objective function considers the costs of path length and threat, and the constraints involve the collision and turning angle. Additionally, we employ the theory of B-Spline curve to represent the planned paths to facilitate the optimization of established model. Then, aiming at the poor searchability and slow convergence speed of current optimization methods, we propose a reinforcement learning-based multi-strategy cuckoo search algorithm. Specifically, we establish an innovative reinforcement learning-based multi-strategy mechanism and a reinforced switch parameter based on the theory of reinforcement learning. To verify the effectiveness of the proposed algorithm, extensive experiments are carried out on the CEC’17 benchmark test and different three-dimensional path planning problems. Detailed statistical analysis of the experimental results confirm the superiority of our proposed algorithm to the other well-established algorithms.

Adaptive cylinder vector particle swarm optimization with differential evolution for UAV path planning

Particle swarm optimization (PSO) algorithm has a potential to solve route planning problem for unmanned aerial vehicle (UAV). However, the traditional PSO algorithm is easy to fall into local optimum under the complicated environments with multiple threats. In order to improve the performance in different complicated environments, a novel and effective PSO algorithm with adaptive adjustment of the parameters, cylinder vector and different evolution operator, named ACVDEPSO, is proposed and demonstrated to be effective for route planning problem for UAV. In the proposed ACVDEPSO, the velocity of the particle is converted to its cylinder vector for the convenience of the path search. It is worth highlighting that the parameters of ACVDEPSO algorithm are automatically chosen by the time and the fitness values of the particles. Furthermore, a challenger based on differential evolution operator is introduced to reduce the probability of falling into local optimum and accelerate the algorithm convergence speed. The simulation experiments have been conducted in real digital elevation model (DEM) maps to test the performance of the ACVDEPSO. The experiment results validate that the optimization performance of the ACVDEPSO outperforms the other comparison methods, which can efficiently generate a higher quality path for UAV under the complicated 3D environments.

Secure Control Design for Networked Control Systems With Nonlinear Dynamics Under Time-Delay-Switch Attacks

A continuous controller is developed for a centralized network control system (NCS), which is composed of agents with nonlinear dynamics subject to a time-delay-switch (TDS) attack and additive disturbances. Since the state tracking error is unmeasurable during TDS attacks, controllers cannot use the state tracking error to coordinate the NCS. Therefore, a novel error signal is designed to address this unique challenge and enable the NCS to achieve a formation control objective. Furthermore, a TDS attack mitigation strategy is developed, which uses both learning- and model-based approaches to estimate an agent’s state for TDS attack detection and compensation. Lyapunov–Krasovskii functionals are used in the stability analysis to prove that the tracking errors converge to a steady-state residual, which is a function of the system uncertainty and TDS attack properties. The leader–follower formation control problem for unmanned aerial vehicles based on a pure pursuit guidance law is selected for a simulation study to validate the performance of the proposed method.