UAV Trajectory Research Articles

Research on UAV Trajectory Planning Algorithm Based on Adaptive Potential Field

For multi-obstacle complex scenarios, the traditional artificial potential field method suffers from the defects of potential field imbalance, its capability to easily fall into the local minima, and encounter unreachable targets in complex navigation environments. Therefore, this paper proposes a three-dimensional adaptive potential field algorithm (SAPF) based on multi-agent reinforcement learning. First, in this paper, the gravitational function in the artificial potential field (APF) is modified to weaken the gravitational effect on the UAV in the region far away from the target point in order to reduce the risk of collision between the UAV and the obstacles during the moving process. Second, in the region close to the target point, this paper improves the artificial potential field function to ensure that the UAV can reach the target point smoothly and realize path convergence by considering the relative distance between the UAV’s current position and the target point. Finally, for the characteristics of UAV trajectory planning, a 3D state space is designed based on the 3D coordinates of the UAV, the distance between the UAV and the nearest obstacle, and the distance between the UAV and the target point; an action space is designed based on the displacement increment of the UAV in the three coordinate axes; and the specific formulas for collision penalties and path optimization rewards are re-designed, which effectively avoids the UAV from entering the local minimal points. The experimental results show that the artificial potential field method designed with reinforcement learning can plan shorter paths and exhibit better planning results. In addition, the method is more adaptable in complex scenes and has better anti-interference.

3D UAV Trajectory Planning With Obstacle Avoidance for UAV-Enabled Time-Constrained Data Collection Systems

3D UAV Trajectory Planning With Obstacle Avoidance for UAV-Enabled Time-Constrained Data Collection Systems

Attitude Control and Trajectory Tracking of a Shipboard Quadrotor Unmanned Aerial Vehicle Based on Self-Improvement Algorithm

With the rapid development of Industry 4.0 and urbanisation around the world, computer control technology and electromechanical systems, the development of UAVs around the world has entered a period of rapid development. As UAVs enter the rapid development stage, higher requirements are placed on the precise control rate during UAV missions. Multi-rotor UAVs have been used on ships in large scale due to their easy-to-manoeuvre characteristics. However, shipboard UAVs are affected by many factors during flight, such as crosswinds, gusts and wave effects at sea. Therefore, it is necessary to optimise the robustness and safety of the control process of shipborne UAVs through self-immunity algorithms. In the research process of UAV, modern control theory has developed very rapidly, but it is difficult to be applied in practice, and there is a certain gap. The reason for this is that advanced control theory techniques require an accurate mathematical model of the controlled object.In this study, the position and attitude equations of motion of the quadrotor UAV are firstly obtained according to the rigid body mechanics, the UAV dynamics model is deduced, and for the UAV attitude control and trajectory tracking requirements, the inner loop adopts the self-immobilisation control and the outer loop adopts the PID control, and the relevant control structure is designed. Through relevant Simulink simulation and comparison with the effect of traditional PID control, the optimisation effect of the self-immunity algorithm on the control of shipborne quadrotor UAV is verified.

MARL-Based UAV Trajectory and Beamforming Optimization for ISAC System

MARL-Based UAV Trajectory and Beamforming Optimization for ISAC System

Jointly Optimize Throughput and Localization Accuracy: UAV Trajectory Design for Multiuser Integrated Communication and Sensing

Jointly Optimize Throughput and Localization Accuracy: UAV Trajectory Design for Multiuser Integrated Communication and Sensing

Research on flight intention recognition of UAVs-swarm

Abstract Most existing threat level assessments for aerial targets rely on numerical analysis and typically address only single or a few incoming targets without incorporating intention recognition. Such methods are inadequate for anti-UAV(Unmanned aerial vehicle) tasks involving large numbers of flight targets. To accurately and efficiently identify the flight intentions of UAVs-swarm, this paper proposes a method based on formation recognition and trajectory prediction. Initially, the trajectories of UAVs are obtained and clustered using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm. For each cluster, a Bayesian classifier is employed to recognize its flight formation. Subsequently, a minimum enclosing sphere is established for each cluster, and trajectory fusion and prediction are conducted based on the sphere’s center. Finally, a flight intention recognition model is constructed. The results demonstrate that this method effectively infers the flight intentions of each UAVs-cluster, providing a robust basis for the threat level assessment in anti-UAVs-swarm tasks.

Distributed Optimization of Multi-Role UAV Functionality Switching and Trajectory for Security Task Offloading in UAV-Assisted MEC

Distributed Optimization of Multi-Role UAV Functionality Switching and Trajectory for Security Task Offloading in UAV-Assisted MEC

Symmetry-Augmented Multi-Agent Reinforcement Learning for Scalable UAV Trajectory Design and User Scheduling

Symmetry-Augmented Multi-Agent Reinforcement Learning for Scalable UAV Trajectory Design and User Scheduling

Efficient Generation of Optimal UAV Trajectories With Uncertain Obstacle Avoidance in MEC Networks

Efficient Generation of Optimal UAV Trajectories With Uncertain Obstacle Avoidance in MEC Networks

Finite-time trajectory tracking control of quadrotor UAVs based on neural network disturbance observer and command filter

The paper proposes a novel finite-time control strategy for quadrotor UAV trajectory tracking using a neural network disturbance observer and a command filter. This method is used to address input saturation and disturbances, ensuring that the UAV can accurately follow the desired trajectory in finite time. The neural network disturbance observer is crucial for approximating external disturbance signals within a finite time, while the finite-time backstepping scheme accelerates the convergence of tracking errors. The command filtering technique is employed to avoid the complex derivation of virtual control laws, simplifying the controller design. The importance of this method lies in its ability to achieve fast, disturbance-resistant trajectory tracking for UAVs, making the control system more robust in practical applications. Simulations were conducted, showing that the proposed control strategy enables the quadrotor UAV to track its desired trajectory effectively, with improved anti-jamming capability. Both filtering and observation errors converged to the equilibrium point, validating the effectiveness of the approach. However, internal factors like actuator failure were not considered, pointing to future work in refining the method and applying it in real-world UAV experiments.

Joint Resource Allocation and UAV Trajectory Design for Data Collection in Air-Ground Integrated IoRT Sensors Network With Clustered NOMA

Joint Resource Allocation and UAV Trajectory Design for Data Collection in Air-Ground Integrated IoRT Sensors Network With Clustered NOMA

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, three 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.

Enhancing QoE in Large-Scale U-MEC Networks via Joint Optimization of Task Offloading and UAV Trajectories

Enhancing QoE in Large-Scale U-MEC Networks via Joint Optimization of Task Offloading and UAV Trajectories

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 Cooperative Caching and UAV Trajectory Optimization Based on Mobility Prediction in the Internet of Connected Vehicles

Joint Cooperative Caching and UAV Trajectory Optimization Based on Mobility Prediction in the Internet of Connected Vehicles

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

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

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