UAV Trajectory Research Articles

Sequential Convex Programming for Nonlinear Optimal Control in UAV Trajectory Planning

In this paper, an algorithm is proposed to solve the non-convex optimization problem using sequential convex programming. An approximation method was used to solve the collision avoidance constraint. An iterative approach was utilized to estimate the non-convex constraints, replacing them with their linear approximations. Through the simulation, we can see that this method allows for quadcopters to take off from a given initial position and fly to the desired final position within a specified flight time. It is guaranteed that the quadcopters will not collide with each other in different scenarios.

Cloud Shape and Attenuation Based UAV Trajectory Optimization for FSO Communication

Cloud Shape and Attenuation Based UAV Trajectory Optimization for FSO Communication

Energy-efficient data collection in UAV-assisted semantic awareness IoT network

Semantic communication shows great potential in reducing data redundancy and improving data collection efficiency in the Internet of Things (IoT) networks. However, IoT devices have limited energy storage capacity, so it is not possible to transmit semantic information to cloud center all the time. To this end, we propose an energy-efficient uploading mechanism in the UAV-assisted semantic awareness data collection system. Specially, we minimize the maximum energy consumption of IoT devices via jointly optimizing user scheduling and UAV trajectory. Since the formulated optimization problem is non-convex, we propose an efficient iterative algorithm to obtain a feasible solution. As the number of iterations increases, the user min–max energy consumption gradually converges to a steady state. Simulation results show that the proposed algorithm has good convergence. In addition, the simulation results of our proposed scheme show that the system performance is greatly improved compared with the conventional bit information transmission and non-optimization schemes.

Secure Communication in UAV–RIS-Empowered Multiuser Networks: Joint Beamforming, Phase Shift, and UAV Trajectory Optimization

Secure Communication in UAV–RIS-Empowered Multiuser Networks: Joint Beamforming, Phase Shift, and UAV Trajectory Optimization

UAV Trajectory Prediction Based on Flight State Recognition

UAV Trajectory Prediction Based on Flight State Recognition

Deep Reinforcement Learning-Based Distributed 3D UAV Trajectory Design

Deep Reinforcement Learning-Based Distributed 3D UAV Trajectory Design

Research on a Multi-Strategy Improved Sand Cat Swarm Optimization Algorithm for Three-Dimensional UAV Trajectory Path Planning

In response to the issues of premature convergence, lack of population diversity, and poor convergence accuracy in the traditional Sand Cat Swarm Optimization (SCSO) algorithm, a Multi-Strategy Improved SCSO (MISCSO) algorithm is proposed. Firstly, multiple population strategies are used to avoid premature convergence and falling into local optima traps. Secondly, a distribution estimation learning strategy is introduced to represent the relationships between individuals, using probability models to improve algorithm performance. Next, the diversity of candidate solutions in the elite pool is utilized to expand the search space and enhance the algorithm’s ability to avoid local solutions. Lastly, a Cauchy disturbance strategy is adopted to accelerate the convergence speed of the algorithm, thereby improving the search efficiency and convergence accuracy. The experimental results of CEC2017 tests show that the improved algorithm balances convergence speed and global search capabilities effectively. Finally, the algorithm is applied to actual drone path planning and compared with six other intelligent algorithms, demonstrating the practicality and effectiveness of the improved algorithm.

Reinforcement learning-based unmanned aerial vehicle trajectory planning for ground users’ mobility management in heterogeneous networks

The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets.

Multi-Drone Cooperation for Improved LiDAR-Based Mapping.

This paper focuses on mission planning and cooperative navigation algorithms for multi-drone systems aimed at LiDAR-based mapping. It aims at demonstrating how multi-UAV cooperation can be used to fulfill LiDAR data georeferencing accuracy requirements, as well as to improve data collection capabilities, e.g., increasing coverage per unit time and point cloud density. These goals are achieved by exploiting the CDGNSS/Vision paradigm and properly defining the formation geometry and the UAV trajectories. The paper provides analytical tools to estimate point density considering different types of scanning LIDAR and to define attitude/pointing requirements. These tools are then used to support centralized cooperation-aware mission planning aimed at complete coverage for different target geometries. The validity of the proposed framework is demonstrated through numerical simulations considering a formation of three vehicles tasked with a powerline inspection mission. The results show that cooperative navigation allows for the reduction of angular and positioning estimation uncertainties, which results in a georeferencing error reduction of an order of magnitude and equal to 16.7 cm in the considered case.

Complex hilly terrain agricultural UAV trajectory planning driven by Grey Wolf Optimizer with interference model

Complex hilly terrain agricultural UAV trajectory planning driven by Grey Wolf Optimizer with interference model

UAV Trajectory Planning via Viewpoint Resampling for Autonomous Remote Inspection of Industrial Facilities

UAV Trajectory Planning via Viewpoint Resampling for Autonomous Remote Inspection of Industrial Facilities

Multi-Agent Deep Reinforcement Learning Based UAV Trajectory Optimization for Differentiated Services

Multi-Agent Deep Reinforcement Learning Based UAV Trajectory Optimization for Differentiated Services

Dijkstra algorithm based minimum acceleration/snap quadrotor UAV trajectory planning

This paper focuses on path planning using the Dijkstra/A* algorithm and addresses the trajectory generation problem using the minimum acceleration/snap approach. Firstly, the mathematical model of the optimization problem is formulated, specifically utilizing the Euler-Lagrange equation to derive the necessary conditions for achieving minimum acceleration/snap trajectories, which are represented by either 3rd or 7th order polynomials. The coefficients of these polynomials are determined by imposing appropriate constraints on velocity, acceleration, and higher-order derivatives. Subsequently, a detailed comparison of the performances of the two optimization methods in complex environments is conducted, evaluating differences in position and velocity. Based on the deterministic analysis, conclusive results are obtained.

Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models

Traffic Learning and Proactive UAV Trajectory Planning for Data Uplink in Markovian IoT Models

UAV trajectory tracking under wind disturbance based on novel antidisturbance sliding mode control

With the widespread application of unmanned aerial vehicles, harsh environments require higher control performance. Numerous existing works offer solutions for handling external disturbances during flight; however, the timeliness of the disturbance compensation is rarely discussed. This study focuses on trajectory tracking under external wind disturbances, specifically under two types of interference: average wind and wind shear. After compensating for the disturbance, a novel antidisturbance sliding mode control is designed based on reference model, to realize accurate, robust, and chatterless trajectory tracking under wind disturbance. Firstly, we addressed the limitations of conventional extended state observers, nonderived forms, and inadequate use of information by introducing state compensation function observers to enhance estimation accuracy for states and disturbances that are challenging to measure directly. Moreover, the disturbance variation is estimated using tracking differentiator and then compensated in the controller to enhance antidisturbance sensitivity. In addition, the effectiveness and stability of the proposed observer and controller are analyzed. Finally, a two-step verification is presented, including simulation with input disturbances and actual flight under two wind field types; multiple industrial fans are used to generate the average wind and wind shear during the experiment. Two robust control methods, state-of-the-art active disturbance rejection control and sliding mode control, are also implemented for comparison, and the results indicate that more than 61.2% accuracy improvement over comparison methods is achieved in simulation and actual experiments.

UAV Trajectory Optimization for Large-Scale and Low-Power Data Collection: An Attention-Reinforced Learning Scheme

UAV Trajectory Optimization for Large-Scale and Low-Power Data Collection: An Attention-Reinforced Learning Scheme

Research on Joint Optimization of Task Offloading and UAV Trajectory in Mobile Edge Computing Considering Communication Cost Based on Safe Reinforcement Learning

Due to CPU and memory limitations, mobile IoT devices face challenges in handling delay-sensitive and computationally intensive tasks. Mobile edge computing addresses this issue by offloading tasks to the wireless network edge, reducing latency and energy consumption. UAVs serve as auxiliary edge clouds, providing flexible deployment and reliable wireless communication. To minimize latency and energy consumption, considering the limited resources and computing capabilities of UAVs, a multi-UAV and multi-edge cloud system was deployed for task offloading and UAV trajectory optimization. A joint optimization model for computing task offloading and UAV trajectory was proposed. During model training, a UAV communication mechanism was introduced to address potential coverage issues for mobile user devices through multiple UAVs or complete coverage. Considering the fact that decisions made by UAVs during trajectory planning may lead to collisions, a MADDPG algorithm with an integrated safety layer was adopted to obtain the safest actions closest to the joint UAV actions under safety constraints, thereby avoiding collisions between UAVs. Numerical simulation results demonstrate that the optimization method based on safety reinforcement learning considering communication cost outperforms other optimization methods. Communication between UAVs effectively addresses the issue of redundant or incomplete coverage for mobile user devices, reducing computation latency and energy consumption for task offloading. Additionally, the introduction of safety reinforcement learning effectively avoids collisions between UAVs.

UAV Trajectory, User Association, and Power Control for Multi-UAV-Enabled Energy-Harvesting Communications: Offline Design and Online Reinforcement Learning

UAV Trajectory, User Association, and Power Control for Multi-UAV-Enabled Energy-Harvesting Communications: Offline Design and Online Reinforcement Learning

Real-Time UAV Trajectory Prediction for UTM Surveillance Using Machine Learning

The Unmanned Aerial System (UAS) Traffic Management (UTM) surveillance plays an important role in monitoring the safety compliance of UAV flights within specific operational zones. However, the quality of data reception from UAVs depends on transmission signal quality, leading to variable data latency during flights. As a means of addressing this issue and improving UAV operational safety, trajectory prediction emerges as a promising solution. This study aims to implement real-time trajectory prediction through the integration of machine learning techniques within the UTM surveillance system using Broadcast Remote ID. To facilitate this, a homemade receiver for broadcast Remote ID is developed using the ESP32 microcontroller. Subsequently, two distinctive flight tests are executed using a DJI Phantom 4 UAV. In the first flight, UAV trajectory data are used to serve as training input for the machine learning algorithms. The second flight focuses on the implementation of real-time trajectory prediction. The trajectory prediction model uses inputs such as latitude, longitude, height, speed, direction, latency time, and the Received Signal Strength Indicator (RSSI). Through an analysis of the first flight’s offline data, the Gated Recurrent Unit (GRU) algorithm is preferred to the Long Short-Term Memory (LSTM) algorithm. The outcomes of the second flight test prove the feasibility of the GRU-based trajectory prediction. The GRU model successfully produces real-time predictions during UAV flight, showcasing accuracy levels similar to those derived from offline prediction analyses with short processing time. This real-time trajectory prediction could improve the safety of UAV operation by providing the estimated position when the latency time is higher than the threshold.

Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multiagent PPO Method

Cooperative UAV Trajectory Design for Disaster Area Emergency Communications: A Multiagent PPO Method