UAV Trajectory Research Articles

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.

Resource Optimization Technology Using Genetic Algorithm in UAV-Assisted Edge Computing Environment

As fixed edge computing systems can hardly meet the demand of mobile users for massive data processing, a computational resource allocation strategy using the genetic algorithm in UAV-assisted edge computing environment is proposed. First, a UAV-assisted mobile edge computing (MEC) system is designed to help users execute computation tasks through the UAV or relaying to the ground base station. Then, a communication model and a computation model are constructed to minimize the total system energy consumption by jointly optimizing the UAV offloading ratio, user scheduling variables, and UAV trajectory. Finally, the minimization of total system energy consumption is modeled as a nonconvex optimization problem and solved by introducing an improved genetic algorithm, so as to achieve a rational allocation of computational resources. Based on the experimental platform, the simulation of the proposed method is carried out. The results show that the total energy consumption is 650 J when the execution time is 110 s and the execution time is 17.5 s when the number of users is 50, which are both better than other comparison methods.

Dynamic responses due to the Dryden gust of an autonomous quadrotor UAV carrying a payload

AbstractUnmanned Aerial Vehicles (UAVs) have been used extensively in many applications, such as surveillance, medical transportation and delivery tasks. These applications usually involve an attachment system to connect a payload. However, when connected to such a coupled system, the UAV exhibits lower flight performance mainly due to gust loads. Mathematical modeling is used in this paper to illustrate the comparison between an UAV with a coupled system and a conventional UAV. Proportional Derivative (PD) and Sliding Mode Control (SMC) approaches are used to evaluate the vibrations of the payload, and the UAV trajectory subjected to Dryden gust. This work therefore uses these methods to investigate the dynamic response of a quadrotor-type UAV equipped with a payload and a flexible attachment system.

Hybrid Uplink and Downlink Transmissions for Full-Duplex UAV Communication With RIS

This letter proposes a reconfigurable intelligent surface (RIS)-aided full-duplex unmanned aerial vehicle (FD-UAV) communication system. The FD-UAV performs its mission to communicate with an uplink (UL) user and a downlink (DL) user simultaneously over the same time-frequency dimension. The RIS is deployed to enhance the signal strength to the UL and DL users while suppressing the co-channel interference (CCI) from the user side. We aim to maximize the weighted sum rate of UL and DL transmissions by jointly optimizing the transmit power of the UL user, RIS’s phase-shift, and UAV trajectory. Although the formulated problem is difficult to solve due to its joint non-convexity, we propose an efficient algorithm based on the successive convex approximation and fractional programming techniques to solve the problem suboptimally. Simulation results show that the proposed RIS-aided communication system can achieve a significant sum rate gain over various bechmark schemes.

FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments

Planning high-speed trajectories for UAVs in unknown environments requires algorithmic techniques that enable fast reaction times to guarantee safety as more information about the environment becomes available. The standard approaches that ensure safety by enforcing a “stop” condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown. Moreover, the ad-hoc time and interval allocation scheme usually imposed on the trajectory also leads to conservative and slower trajectories. This work proposes FASTER (Fast and Safe Trajectory Planner) to ensure safety without sacrificing speed. FASTER obtains high-speed trajectories by enabling the local planner to optimize in both the free-known and unknown spaces. Safety is ensured by always having a safe back-up trajectory in the free-known space. The MIQP formulation proposed also allows the solver to choose the trajectory interval allocation. FASTER is tested extensively in simulation and in real hardware, showing flights in unknown cluttered environments with velocities up to 7.8 m/s, and experiments at the maximum speed of a skid-steer ground robot (2 m/s).

Research on Cooperative Search Trajectory Planning for Rotorcraft UAVs Based on Scrolling Planning Algorithm

Abstract The UAV trajectory planning algorithm for scrolling planning is proposed for the objective of multi-UAVs searching for multiple targets in an uncertain environment. Information about the unknown environment is described using infographic, while for the huge computational effort in multi-UAVs scrolling planning, an improved genetic algorithm is used to solve the optimal solution for the UAV population. Simulation results show that the scrolling planning algorithm proposed in this paper can achieve a better search for the target in the uncertain region, and the algorithm framework is reasonable and feasible with high planning efficiency, which can be used for the cooperative search task of multi-UAVs in the uncertain area.

An UAV-Assisted Edge Computing Resource Allocation Strategy for 5G Communication in IoT Environment

As the computing capacity of existing mobile devices cannot fully meet the needs of users for communication quality, a computing resource allocation strategy for 5G communication in the Internet of Things (IoT) environment is proposed by applying UAV-assisted edge computing. First, a system model is constructed with the UAV deployed with mobile edge computing (MEC) servers to provide assisted computing services for multiple users on the ground. Based on the optimization of the UAV trajectory, communication scheduling, and the energy consumption model of the UAV, the problem of the total computational cost minimization is formulated. Then, the genetic algorithm is improved by introducing a penalty function to solve this problem, in which selection, crossover, and mutation operations are iterated to obtain the optimal allocation strategy for computational resources. Finally, a simulation platform is constructed to analyze the proposed method. The results show that the total cost and total time of the proposed strategy are better than other comparison strategies.

Unmanned Aerial Vehicle Surveying and Mapping Trajectory Scheduling and Autonomous Control for Landslide Monitoring

Real-time and efficient monitoring of geological disasters has received extensive attention in the application of UAV surveying and mapping control technology. The application of traditional landslide monitoring methods lacks the accuracy of control algorithms, which has become a hot issue currently facing. Based on the landslide surface subsidence monitoring method, this article designs the UAV trajectory scheduling subsidence monitoring software, which can monitor the UAV’s flight status and navigation information, and draw the flight trajectory in real time. At the same time, the model solves the problem of storage and management of landslide inspection results by the landslide inspection management system, and realizes the functions of entering and querying landslide information, viewing inspection results, landslide safety judgment, generating reports, and autonomous control. The simulation results show that the global accuracy reaches 0.975, and the algorithm recognition degree reaches 99.8%, which promotes the reliability of the landslide monitoring data for the identification of the surveying and mapping trajectory, and provides a decision-making basis for landslide treatment.

Delay performance of priority-queue equipped UAV-based mobile relay networks: Exploring the impact of trajectories

UAV-based mobile relay communication systems have an important position in future wireless networks, such as Beyond 5G and 6G, which provide ubiquitous network services. Along this line, UAV-based single-hop relay systems have numerous applications in emergency situations, such as data ferry and assisting post-disaster areas. Although there have been numerous works on optimizing trajectories and other communication resources on such systems, there are few studies on delay performance at the granular packet level. In addition to having homogeneous traffic, such a system can be equipped with diverse traffic with different QoS requirements, owing to the emergence of various applications in today’s communication networks. Based on the traffic requirements, packets with high-priority traffic should be served ahead of those with low-priority ones. To this end, we aim to analytically study the average end-to-end packet delay and buffer overflow performance of such a system at the granular packet level. However, packet-level queuing analysis of heterogeneous traffic is not as straightforward as that of homogeneous traffic. While considering different traffic requirements, we establish a priority queuing model for the source and the UAV nodes based on the stochastic process. Then, on the basis of the established queuing model, the average end-to-end packet delay and buffer overflow probability of the system under different trajectories are studied. Through extensive numerical simulations, we verified the accuracy and effectiveness of the proposed analytical model. The results show that the average packet delay and packet drop probability of high-priority packets under all trajectories are significantly better compared to low-priority packets. UAV trajectory has a great impact on network performance, and an appropriate mobile trajectory that takes full use of channel capacity is superior to a static one.

Robust Resource Allocation and Trajectory Planning of UAV-Aided Mobile Edge Computing in Post-Disaster Areas

When natural disasters strike, users in the disaster area may be isolated and unable to transmit disaster information to the outside due to the damage of communication facilities. Unmanned aerial vehicles can be exploited as mobile edge servers to provide emergency service for ground users due to its mobility and flexibility. In this paper, a robust UAV-aided wireless-powered mobile edge computing (MEC) system in post disaster areas is proposed, where the UAV provides charging and computing service for users in the disaster area. Considering the estimation error of users’ locations, our target is to maximize the energy acquisition of each user by jointly optimizing the computing offloading process and the UAV trajectory. Due to the strongly coupled connectionbetween optimization variables and the non-convex nature for trajectory optimization, the problem is difficult to solve. Furthermore, the semi-infinity of the users’ possible location makes the problem even more intractable. To tackle these difficulties, we ignore the estimation error of users’ location firstly, and propose an iterative algorithm by using Lagrange dual method and successive convex approximation (SCA) technology. Then, we propose a cutting-set method to deal with the uncertainty of users’ location. In this method, we degrade the influence of location uncertainty by alternating between optimization step and pessimization step. Finally, simulation results show that the proposed robust algorithm can effectively improve the user energy acquisition.

UAVs cooperative task assignment and trajectory optimization with safety and time constraints

This paper proposes new methods and strategies for Multi-UAVs cooperative attacks with safety and time constraints in a complex environment. Delaunay triangle is designed to construct a map of the complex flight environment for aerial vehicles. Delaunay-Map, Safe Flight Corridor (SFC), and Relative Safe Flight Corridor (RSFC) are applied to ensure each UAV flight trajectory's safety. By using such techniques, it is possible to avoid the collision with obstacles and collision between UAVs. Bezier-curve is further developed to ensure that multi-UAVs can simultaneously reach the target at the specified time, and the trajectory is within the flight corridor. The trajectory tracking controller is also designed based on model predictive control to track the planned trajectory accurately. The simulation and experiment results are presented to verifying developed strategies of Multi-UAV cooperative attacks.

Task Offloading and Trajectory Scheduling for UAV-Enabled MEC Networks: An Optimal Transport Theory Perspective

In this letter, we propose a task offloading framework for UAV-enabled MEC networks, where all smart mobile devices are scattered randomly following an arbitrary distribution. By exploiting the hovering and mobilizing capability of a UAV, task offloading service can be provided to the devices in all sub-areas of the network. To extend UAV operating time and associated network lifetime, we formulate an optimization problem to minimize the total energy consumption of the UAV through joint region partitioning and UAV trajectory scheduling. To solve it, we decompose it into two independent sub-problems, i.e., region partitioning and UAV trajectory optimization. For the first sub-problem, we model it as a semi-discrete optimal transport problem by considering the traffic balance among different sub-areas and propose an iterative algorithm to achieve the optimal solution. Then, the UAV trajectory optimization sub-problem is modeled as a traveling salesman problem to determine the shortest route. Simulation results show that the proposed scheme can significantly reduce the energy consumption of the UAV while achieving proper load balance.

Joint Optimization of Trajectory and Communication in Multi-UAV Assisted Backscatter Communication Networks

This paper investigates a multiple unmanned aerial vehicle (multi-UAV) assisted backscatter communication network (BCN), where multiple UAVs are employed to transmit RF carriers to as well as collect data from multiple backscatter sensor nodes (BSNs) deployed on the ground. We formulate an optimization problem to maximize the max-min rate of the BCN by jointly optimizing three blocks of variables, i.e., the UAVs’ trajectories, the UAVs’ transmission power and the BSNs’ scheduling. The BSNs’ sequential energy constraints are innovatively considered in our work. However, the formulated optimization problem is difficult to be solved due to its non-convexity and combinatorial nature. To this end, we use the block coordinate descent (BCD) method and successive convex approximation (SCA) technique. Numerical results show the impact of the BSNs’ sequential energy constraint on the designed UAV trajectories and verify the gain of the proposed design in the max-min rate as compared to a benchmark scheme with UAV trajectory not optimized.

Joint task scheduling, resource allocation, and UAV trajectory under clustering for FANETs

This paper establishes a new layered flying ad hoc networks (FANETs) system of mobile edge computing (MEC) supported by multiple UAVs, where the first layer of user UAVs can perform tasks such as area coverage, and the second layer of MEC UAVs are deployed as flying MEC sever for user UAVs with computing-intensive tasks. In this system, we first divide the user UAVs into multiple clusters, and transmit the tasks of the cluster members (CMs) within a cluster to its cluster head (CH). Then, we need to determine whether each CH' tasks are executed locally or offloaded to one of the MEC UAVs for remote execution (i.e., task scheduling), and how much resources should be allocated to each CH (i.e., resource allocation), as well as the trajectories of all MEC UAVs. We formulate an optimization problem with the aim of minimizing the overall energy consumption of all user UAVs, under the constraints of task completion deadline and computing resource, which is a mixed integer non-convex problem and hard to solve. We propose an iterative algorithm by applying block coordinate descent methods. To be specific, the task scheduling between CH UAVs and MEC UAVs, computing resource allocation, and MEC UAV trajectory are alternately optimized in each iteration. For the joint task scheduling and computing resource allocation subproblem and MEC UAV trajectory subproblem, we employ branch and bound method and continuous convex approximation technique to solve them, respectively. Extensive simulation results validate the superiority of our proposed approach to several benchmarks.

Algorithm for constructing the trajectory of UAV when patrolling rough terrain

Algorithm for constructing the trajectory of UAV when patrolling rough terrain

Cellular Network-Supported Machine Learning Techniques for Autonomous UAV Trajectory Planning

Cellular Network-Supported Machine Learning Techniques for Autonomous UAV Trajectory Planning

Completion Time Minimization for UAV-Enabled Surveillance Over Multiple Restricted Regions

This work examines a UAV-enabled surveillance mission over multiple restricted regions and aims to determine the optimal UAV trajectory that minimizes the mission completion time. The UAV is prohibited from entering the restricted regions due to government regulations or adversarial concerns. However, during the surveillance of a region, the UAV can move along the region's boundary to reduce its distance to the next region once the local task is completed. To exploit this advantage, we propose a minimum completion time (MinTime) algorithm that first determines the visiting order of the regions by employing an approximate solution of the traveling salesman problem (TSP) and then optimizes the UAV trajectory over the sequence of restricted regions using dynamic programming. In the presence of obstacles, we further propose an obstacle-aware MinTime (OA-MinTime) algorithm that treats each obstacle as an additional restricted region with zero surveillance duration, allowing the UAV to avoid the obstacles in a more efficient manner. A modified TSP solution is also proposed by taking into consideration the additional distance required to circumvent the obstacles on each inter-POI path. Simulation results show that the proposed MinTime and OA-MinTime algorithms can significantly reduce the total completion time compared to conventional minimum-distance approaches.

Optimized UAV Trajectory and Transceiver Design for Over-the-Air Computation Systems

Optimized UAV Trajectory and Transceiver Design for Over-the-Air Computation Systems

UAV trajectory tracking based on ADRC control algorithm

UAV flying in the air is affected by external airflow factors, and the system has the characteristics of underdrive, nonlinear and uncertainty, which makes the flight state of the UAV become complicated. The trajectory tracking of UAV is studied by using Active disturbance rejection control (ADRC) algorithm. ADRC control algorithms include tracking differentiator, extended state observer and error feedback control law. The active disturbance rejection and decoupling control of UAV track tracking is studied, The active disturbance rejection decoupling control introduces static decoupling matrix B to decouple the virtual control law U. The dynamically coupled disturbances are regarded as total disturbances, which are observed in real time by ESO of each main channel and compensated by control laws. Finally, the 2d trajectory tracking of UAV is realized.

L2‐gain robust trajectory tracking control for quadrotor UAV with unknown disturbance

AbstractIn this paper, the trajectory tracking problem of the position and yaw angle of an underactuated quadrotor UAV is studied. Considering that the quadrotor UAV often encounters unknown disturbance resulted by model parameter perturbations and environmental changes, a new L2‐gain robust control method is presented based on dissipation theory. First, the dynamic model of the quadrotor UAV with disturbances is transformed by introducing three virtual control variables. In this way, the trajectory tracking control can be implemented by the dual loop control, that is, the position control loop and the attitude control loop. Then, based on dissipation inequality, the robust position controller and attitude controller are developed to guarantee the L2‐gain of the system to the unknown disturbance. It is proved that all the tracking errors are uniformly ultimate boundedness. In addition, the controller is further reconstructed by introducing a new class K functions to improve the transient and steady‐state performances of the quadrotor UAV system. In the end, two simulation examples are provided to check the validity of the control method proposed and the convergence of the quadrotor UAV trajectory tracking error.