UAV Trajectory Research Articles

A Physically Hybrid Strategy-Based Improved Snow Ablation Optimizer for UAV Trajectory Planning

A Physically Hybrid Strategy-Based Improved Snow Ablation Optimizer for UAV Trajectory Planning

Efficient UAV Hovering, Resource Allocation, and Trajectory Design for ISAC With Limited Backhaul Capacity

Efficient UAV Hovering, Resource Allocation, and Trajectory Design for ISAC With Limited Backhaul Capacity

Joint Resource Allocation and UAV Trajectory Design for D2D-Assisted Energy-Efficient Air–Ground Integrated Caching Network

Joint Resource Allocation and UAV Trajectory Design for D2D-Assisted Energy-Efficient Air–Ground Integrated Caching Network

Differential evolution with multi-strategies for UAV trajectory planning and point cloud registration

The present study introduces a novel adaptive algorithm, MELSHADE-cnEpSin, which aims to enhance the performance of LSHADE-cnEpSin, which is not only stands out as one of the most competitive versions of differential evolution but also holds the distinction of being one of the CEC winner algorithms. Compared to the original methodology, four main distinctions are presented. To begin with, we adopt an adaptive selection mechanism (ASM) of crossover rate Cr value based on the external archive to rechoose a suitable value. In the next place, a nonlinear population reduction strategy using Sigmoid function is employed to improve population distribution. Additionally, a restart strategy is implemented to mitigate the risk of algorithmic convergence towards suboptimal solutions. Furthermore, the performance of MELSHADE-cnEpSin was evaluated using standard CEC2017 and CEC2022 test suites in conjunction with nine CEC-winning algorithms (L-SHADE, EBOwithCMAR, AGSK, LSHADE-SPACMA, LSHADE-cnEpSin, ELSHADE-SPACMA, EA4eig, MadDE and APGSK-IMODE) as well as two novel algorithms (ACD-DE and MIDE). Furthermore, MELSHADE-cnEpSin was effectively employed to address the challenge of UAV trajectory planning in intricate mountainous terrain and underwent simulation with point cloud registration cases utilizing a rapid global registration dataset, thereby showcasing the potential of MELSHADE-cnEpSin in tackling real-world optimization problems.

An Energy-Efficient Scheme Design for NOMA-Based UAV-Assisted MEC Systems

UAV-assisted MEC networks provide extensive communication coverage and massive computation services for mobile terminals (MTs), which are considered a promising edge paradigm to support future air–ground integrated communications. In this paper, an energy-efficient scheme in NOMA-based UAV-assisted MEC systems is proposed to address the system’s energy constraints and its inability to support massive MT access. Our goal is to minimize system-weighted energy consumption by jointly optimizing the allocation of transmission power, computation resources, and UAV trajectory scheduling. As the formulated problem is non-convex and difficult to solve directly, we decompose it into two manageable sub-problems and propose an iterative algorithm based on successive convex approximations (SCA) to solve each sub-problem alternatively. Simulation results show that the proposed joint optimization algorithm achieves a significant performance improvement compared to other benchmark approaches.

Joint UAV Trajectory and Power Allocation With Hybrid FSO/RF for Secure Space–Air–Ground Communications

Joint UAV Trajectory and Power Allocation With Hybrid FSO/RF for Secure Space–Air–Ground Communications

Joint Optimization of UAV Trajectory and Communication Resources With Complete Avoidance of No-Fly-Zones

Joint Optimization of UAV Trajectory and Communication Resources With Complete Avoidance of No-Fly-Zones

ISAC From the Sky: UAV Trajectory Design for Joint Communication and Target Localization

ISAC From the Sky: UAV Trajectory Design for Joint Communication and Target Localization

Distributed Safe Multi-Agent Reinforcement Learning: Joint Design of THz-Enabled UAV Trajectory and Channel Allocation

Distributed Safe Multi-Agent Reinforcement Learning: Joint Design of THz-Enabled UAV Trajectory and Channel Allocation

Addressing Constraint Coupling and Autonomous Decision-Making Challenges: An Analysis of Large-Scale UAV Trajectory-Planning Techniques

With the increase in UAV scale and mission diversity, trajectory planning systems faces more and more complex constraints, which are often conflicting and strongly coupled, placing higher demands on the real-time and response capabilities of the system. At the same time, conflicts and strong coupling pose challenges the autonomous decision-making capability of the system, affecting the accuracy and efficiency of the planning system in complex environments. However, recent research advances addressing these issues have not been fully summarized. An in-depth exploration of constraint handling techniques and autonomous decision-making issues will be of great significance to the development of large-scale UAV systems. Therefore, this paper aims to provide a comprehensive overview of this topic. Firstly, the functions and application scenarios of large-scale UAV trajectory planning are introduced and classified in detail according to the planning method, realization function and the presence or absence of constraints. Then, the constraint handling techniques are described in detail, focusing on the priority ranking of constraints and the principles of their fusion and transformation methods. Then, the importance of autonomous decision-making in large-scale UAV trajectory planning is described in depth, and related dynamic adjustment algorithms are introduced. Finally, the future research directions and challenges of large-scale UAV trajectory planning are outlooked, providing directions and references for future research in the fields of UAV clustering and UAV cooperative flight.

A Joint UAV Trajectory, User Association, and Beamforming Design Strategy for Multi-UAV-Assisted ISAC Systems

A Joint UAV Trajectory, User Association, and Beamforming Design Strategy for Multi-UAV-Assisted ISAC Systems

Edge-Intelligence-Powered Joint Computation Offloading and Unmanned Aerial Vehicle Trajectory Optimization Strategy

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes.

Non-overshooting sliding mode for UAV control

AbstractFor a class of uncertain systems, a non-overshooting sliding mode control is presented to make them globally exponentially stable and without overshoot. Even when the unknown stochastic disturbance exists, and the time-variant reference trajectory is required, the strict non-overshooting stabilisation is still achieved. The control law design is based on a desired second-order sliding mode (2-sliding mode), which successively includes two bounded-gain subsystems. Non-overshooting stability requires that the system gains depend on the initial values of system variables. In order to obtain the global non-overshooting stability, the first subsystem with non-overshooting reachability compresses the initial values of the second subsystem to a given bounded range. By partitioning these initial values, the bounded system gains are determined to satisfy the robust non-overshooting stability. In order to reject the chattering in the controller output, a tanh-function-based sliding mode is developed for the design of smoothed non-overshooting controller. The proposed method is applied to a UAV trajectory tracking when the disturbances and uncertainties exist. The control laws are designed to implement the non-overshooting stabilisation in position and attitude. Finally, the effectiveness of the proposed method is demonstrated by the flying tests.

UAV path planning based on bird flock migration

Unmanned aerial vehicle, generally referred to as UAV, due to its high-performance and low-cost characteristics, it is particularly widely used in both military and civil applications, and is often used to perform a variety of complex missions. Trajectory planning is the basis for UAVs to realize autonomous flight, which ensures that UAVs avoid obstacles and threatening areas when performing tasks, and guarantees the safe execution of tasks. The operation of a UAV requires finding the safest and most efficient trajectory from a starting point to a specified end point based on several specific constraints. Current UAV trajectory planning algorithms, such as swarm algorithm, particle swarm algorithm, and ant colony algorithm, have been widely recognized, but still have many limitations when they are applied to complex and variable terrain. Therefore, this paper proposes an improved UAV trajectory planning algorithm based on simulated bird migration, and uses matlab for simulation and algorithm evaluation. The simulation results indicate that the improved search strategy is effective, the algorithm can find a better solution in each iteration, and the search strategy of the algorithm can effectively guide the algorithm towards a more optimized region. Overall, this algorithm has high research value in both military and civilian fields.

Towards Autonomous Operation of UAVs Using Data-Driven Target Tracking and Dynamic, Distributed Path Planning Methods

A hybrid real-time path planning method has been developed that employs data-driven target UAV trajectory tracking methods. It aims to autonomously manage the distributed operation of multiple UAVs in dynamically changing environments. The target tracking methods include a Gaussian mixture model, a long short-term memory network, and extended Kalman filters with pre-specified motion models. Real-time vehicle-to-vehicle communication is assumed through a cloud-based system, enabling virtual, dynamic local networks to facilitate the high demand of vehicles in airspace. The method generates optimal paths by adaptively employing the dynamic A* algorithm and the artificial potential field method, with minimum snap trajectory smoothing to enhance path trackability during real flights. For validation, software-in-the-loop testing is performed in a dynamic environment composed of multiple quadrotors. The results demonstrate the framework’s ability to generate real-time, collision-free flight paths at low computational costs.

PSAO: An enhanced Aquila Optimizer with particle swarm mechanism for engineering design and UAV path planning problems

Metaheuristic algorithms have become increasingly significant in solving complex optimization problems. To address the limitations of the original Aquila Optimizer (AO), such as insufficient local exploitation ability, low optimization precision, and slow convergence rate, an enhanced Aquila Optimizer (PSAO) for global optimization has been proposed. PSAO uses a better ergodic good point set to initialize the Aquila population and modifies the search method by employing the golden sine operator and the mechanism of self-learning and social learning based on particle swarm, followed by designing a nonlinear balance factor γ as the switching condition of the algorithm. The simulation experiments on benchmark functions and CEC2017 functions have verified that the PSAO has better global optimization ability and stronger robustness compared with other intelligent algorithms. Meanwhile, the contribution of each component that belongs to PSAO has been validated by ablation experiments for the CEC2022 test functions. To further illustrate the practical application potential of PSAO, PSAO is successfully applied to four typical engineering design problems, three of which reach the best fitness values. In addition, utilizing PSAO to solve the UAV trajectory planning problem by considering the objectives of trajectory length, altitude and corner of the flight process, the total flight cost is reduced by 60.22 %, 27.94 %, and 22.41 % compared with AO, PSO and Gold-SA, respectively, which proves its applicability and superiority in solving the real optimization problems.

Trajectory Optimization to Enhance Observability for Bearing-Only Target Localization and Sensor Bias Calibration.

This study addresses the challenge of bearing-only target localization with sensor bias contamination. To enhance the system's observability, inspired by plant phototropism, we propose a control barrier function (CBF)-based method for UAV motion planning. The rank criterion provides only qualitative observability results. We employ the condition number for a quantitative analysis, identifying key influencing factors. After that, a multi-objective, nonlinear optimization problem for UAV trajectory planning is formulated and solved using the proposed Nonlinear Constrained Multi-Objective Gray Wolf Optimization Algorithm (NCMOGWOA). Simulations validate our approach, showing a threefold reduction in the condition number, significantly enhancing observability. The algorithm outperforms others in terms of localization accuracy and convergence, achieving the lowest Generational Distance (GD) (7.3442) and Inverted Generational Distance (IGD) (8.4577) metrics. Additionally, we explore the effects of the CBF attenuation rates and initial flight path angles.

Hybrid energy‐Efficient distributed aided frog leaping dynamic A* with reinforcement learning for enhanced trajectory planning in UAV swarms large‐scale networks

SummaryUAVs are emerging as a critical asset in the field of data collection from extensive wireless sensor networks (WSNs) on a large scale. UAVs can be used to deploy energy‐efficient nodes or recharge nodes, but it should not compromise the network's coverage and connectivity. This paper proposes a comprehensive approach to optimize UAV trajectories within large‐scale WSNs, utilizing Multi‐Objective Reinforcement Learning (MORL) to balance critical objectives such as coverage, connectivity, and energy efficiency. This research investigates the configuration of a Wireless Sensor Network (WSN) assisted by a pen_spark UAV. In this network, Cluster Heads (CHs) act as central points for collecting data from their assigned sensor nodes. A predefined path is established for the UAV to efficiently gather data from these CHs. The Hybrid Threshold‐sensitive Energy Efficient Network (Hy‐TEEN) encompasses sophisticated algorithms for CH selection, dynamic A* for 3D trajectory planning and leverages reinforcement learning for multi‐objective optimization. The experimental results and analysis demonstrate the effectiveness and efficiency of the proposed approach in improving UAV performance and energy efficiency. The results demonstrate that the proposed methodology's trajectories are capable of achieving a time savings of 3.52% in mission completion when contrasted with conventional baseline methods.

Multi-Unmanned Aerial Vehicles Cooperative Trajectory Optimization in the Approaching Stage Based on the Attitude Correction Algorithm

This study investigated the problem of multi-UAVs cooperative trajectory optimization for remote maritime targets in the approach phase. First, based on the precise location information of the cooperative target, a real-time algorithm for correcting UAV attitude angles is proposed to reduce the impact of UAV attitude angle errors and observation system errors on target positioning accuracy. Then, the attitude correction algorithm is integrated into the interacting multiple model-cubature information filter (IMM-CIF) algorithm to achieve the fusion of multi-UAVs observation information. Furthermore, an improved receding horizon optimization (RHO) method is employed to plan the cooperative observation trajectories for UAVs in real time at the target approaching stage. Finally, numerical simulations are conducted to examine the proposed attitude correction and trajectory optimization algorithm, verifying the effectiveness of the proposed method and enhancing the tracking accuracy of the remote target.

Hybrid Offline–Online UAV Trajectory Design and Subchannel Allocation in UAV Relaying OFDMA Networks

Hybrid Offline–Online UAV Trajectory Design and Subchannel Allocation in UAV Relaying OFDMA Networks