Optimal Flight Path Research Articles

High-Precision Monitoring during the Installation of Large Steel Structures by UAV Nap-of-the-Object Photogrammetry

Abstract. This paper proposes and validates a method based on UAV nap-of-the-object photogrammetry for refined 3D digital modeling to achieve high-precision monitoring during the installation of large steel structures, offering a comparative analysis with traditional oblique photogrammetry methods. Through field tests and data processing on the steel structure of a sports stadium in Zhejiang Province, the results demonstrate that nap-of-the-object photogrammetry can capture high-resolution images, offering significant advantages in detail representation and modeling of complex structures. Experimental data reveal that the model's root mean square error (RMSE) generated by nap-of-the-object photogrammetry is 14.56mm, markedly lower than the 51.99mm achieved by traditional methods, meeting the Level II accuracy standard specified in the "Technical Specifications for Nap-of-the-Object Photogrammetry." The study further shows that, when combined with optimized flight path planning, nap-of-the-object photogrammetry enhances both the efficiency and accuracy of data collection while improving operational safety. This technique is highly applicable and flexible, making it suitable for data acquisition of complex structures and high-rise buildings. In summary, the proposed method exhibits significant advantages in terms of accuracy, efficiency, safety, and applicability, providing a practical solution for high-precision, comprehensive digital modeling of large steel structures.

Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm.

The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.

Neural network algorithm with transfer learning and dropout for using a UAV to search the lost target in motion

When performing the rescue mission, how to find a lost target in motion as soon as possible is a very valuable and interesting research topic. To find the optimal flight path of the UAV used to search the lost target in motion under different prior knowledge and interference intensity, this paper proposes a novel optimization technique called neural network algorithm with transfer learning and dropout (TLDNNA), which is a variant of neural network algorithm (NNA) inspired by artificial neural networks. To improve the global search ability and convergence performance of NNA, the multiple transfer mechanism, the generalized mean transfer position, and the random dropout operator driven by the principles of transfer learning and dropout are introduced to TLDNNA. To investigate the performance of TLDNNA, TLDNNA and 12 powerful population-based optimization algorithms are applied to the problem of using a UAV to search the lost target in motion in the designed 9 scenarios. Experimental results show that TLDNNA is remarkably better than the other 12 algorithms in most of the scenarios in terms of solution quality, stability, and convergence performance. In addition, TLDNNA can improve the overall search performance by 18.14% and overall computational efficiency by 3.41 times compared to NNA. The source code of TLDNNA can be obtained by https://github.com/jsuzyy/TLDNNA.

Optimized structural inspection path planning for automated unmanned aerial systems

Automation in Unmanned Aerial Systems (UAS)-based structural inspections has gained significant traction given the scale and complexity of infrastructure. A core problem in UAS-based inspection is electing an optimal flight path to achieve the mission objectives while minimizing flight time. This paper presents an effective two-stage method that guarantees coverage as a constraint to ensure damage detectability, while minimizing path length as an objective. A genetic algorithm first determines viewpoint positions, and a greedy algorithm calculates the camera poses, as opposed to directly optimizing all degrees of freedom (DOF) simultaneously. A sensitivity analysis demonstrates the range of applicability and superiority of this formulation over direct 5-DOF optimization by at least 30 % shorter path length. Applied examples, including focused and partial space inspections, are also presented, demonstrating the flexibility of the proposed method to meet real-world requirements. The results highlight the feasibility of the approach and contribute to incorporating automation into UAS-based structural inspections.

Evolutionary computation for unmanned aerial vehicle path planning: a survey

Unmanned aerial vehicle (UAV) path planning aims to find the optimal flight path from the start point to the destination point for each aerial vehicle. With the rapid development of UAV technology, UAVs are required to tackle missions in increasingly complex environments. Consequently, UAV path planning encounters more challenges, causing traditional deterministic algorithms to struggle to find the optimal path within a certain time. Evolutionary computation (EC) is a series of nature-inspired methodologies and algorithms, which have shown effectiveness and efficiency in solving many complex optimization problems in real-world applications. Recently, EC algorithms have been effectively applied in UAV path planning and have shown encouraging performance in obtaining high-quality solutions. Therefore, it is crucial to review the related research experience and literature in the field of using EC for UAV path planning. This paper presents a comprehensive survey to showcase the existing studies on EC in UAV path planning, especially in complex environments. The paper first proposes a novel taxonomy to categorize the relevant studies into three different categories according to the complex environmental properties of the application scenarios. These environmental properties include complex search space, complex time control, and complex optimization objectives. Then, the EC algorithms for UAV path planning in these complex environments are further systematically surveyed as constrained search space and large-scale search space in complex search space, dynamic UAV path planning and multi-UAV concurrent path planning in complex time control, and expensive objective and multiple objectives in complex optimization objectives. Finally, some potential future research directions for applying EC algorithms to UAV path planning are presented and discussed.

Trajectory planning with multiplatform spacetime RRT*

The article presents a method of planning the flight trajectory of a swarm of drones using a modified RRT (Rapidly-exploring Random Tree) algorithm. The version of the RRT algorithm presented in the article is called Multiplatform Spacetime RRT*. The proposed modifications make it possible to determine the flight trajectory of UAVs taking into account time constraints related to the area occupied by each platform. Additionally, the proposed algorithm ensures the avoidance of potential collisions of platforms in the air by using a collision avoidance algorithm used in practice based on geometric methods. Two designed and tested modifications of RRT were presented, based on the basic RRT* and Informed RRT* algorithms. The algorithm used in both tested versions guarantees the determination of the optimal flight path for unmanned platforms in a finite, small number of steps, which solely depends on the number of UAVs involved. This algorithm takes into account the dynamic model of the fixed-wing UAV. The simulation results presented by planning the flight trajectory of a swarm, consisting of three and four UAVs using the Multiplatform Spacetime RRT* algorithm, are significantly better than the algorithms that were compared to achieve these results.

Bionic 3D Path Planning for Plant Protection UAVs Based on Swarm Intelligence Algorithms and Krill Swarm Behavior.

The protection of plants in mountainous and hilly areas differs from that in plain areas due to the complex terrain, which divides the work plot into many narrow plots. When designing the path planning method for plant protection UAVs, it is important to consider the generality in different working environments. To address issues such as poor path optimization, long operation time, and excessive iterations required by traditional swarm intelligence algorithms, this paper proposes a bionic three-dimensional path planning algorithm for plant protection UAVs. This algorithm aims to plan safe and optimal flight paths between work plots obstructed by multiple obstacle areas. Inspired by krill group behavior and based on group intelligence algorithm theory, the bionic three-dimensional path planning algorithm consists of three states: "foraging behavior", "avoiding enemy behavior", and "cruising behavior". The current position information of the UAV in the working environment is used to switch between these states, and the optimal path is found after several iterations, which realizes the adaptive global and local convergence of the track planning, and improves the convergence speed and accuracy of the algorithm. The optimal flight path is obtained by smoothing using a third-order B-spline curve. Three sets of comparative simulation experiments are designed to verify the performance of this proposed algorithm. The results show that the bionic swarm intelligence algorithm based on krill swarm behavior reduces the path length by 1.1~17.5%, the operation time by 27.56~75.15%, the path energy consumption by 13.91~27.35%, and the number of iterations by 46~75% compared with the existing algorithms. The proposed algorithm can shorten the distance of the planned path more effectively, improve the real-time performance, and reduce the energy consumption.

Three-dimensional unmanned aerial vehicle path planning utilizing artificial gorilla troops optimizer incorporating combined mutation and quadratic interpolation operators

In real terrain and dynamic obstacle scenarios, the complexity of the 3D UAV path planning problem greatly increases. Thus, to procure the optimal flight path for UAVs in such scenarios, an augmented Artificial Gorilla Troops Optimizer, denoted as OQMGTO, is proposed. The proposed OQMGTO algorithm introduces three strategies: combination mutation, quadratic interpolation, and random opposition-based learning, aiming to enhance the ability to timely escape from local optimal path areas and rapidly converge to the global optimal path. Given the flight distance, smoothness, terrain collision, and other five realistic factors of UAVs, specific constraint conditions are proposed to address complex scenarios, aiming to construct a path planning model. By optimizing this model, OQMGTO algorithm solves the path planning problem in complex scenarios. The extensive validation of OQMGTO algorithm on CEC2017 test suite enhances its credibility as a powerful optimization tool. Comparison experiments are conducted in simulated terrain scenarios, including six multi-obstacle terrain scenarios and three dynamic obstacle scenarios. The experimental findings validate OOMGTO algorithm can assist UAV in searching for excellent flight paths, featuring high safety and reliability characteristics, which confirms the superiority of OOMGTO algorithm for path planning in simulated terrain scenarios. Furthermore, in four flight missions carried out in real terrains, OQMGTO algorithm demonstrates superior search performance, planning smooth trajectories without mountain collision.

Path Planning for Fixed-Wing Unmanned Aerial Vehicles: An Integrated Approach with Theta* and Clothoids

Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes a crucial aspect to increase mission efficiency, although it is inherently complex due to various factors such as obstacles, no-fly zones, non-cooperative aircraft, and flight mechanics limitations. This paper presents a path-planning technique for fixed-wing unmanned aerial vehicles (UAVs) based on the Theta* algorithm. The approach introduces innovative features, such as the use of Euler spiral, or clothoids, to serve as connection arcs between nodes, mitigating trajectory discontinuities. The design of clothoids can be linked to the aircraft performance model, establishing a connection between curvature constraints and the specific characteristics of the vehicle. Furthermore, to lower the computational burden, the implementation of an adaptive exploration distance and a vision cone was considered, reducing the number of explored solutions. This methodology ensures a seamless and optimized flight path for fixed-wing UAVs operating in static environments, showcasing a noteworthy improvement in trajectory smoothness. The proposed methodology has been numerically evaluated in several complex test cases as well as in a real urban scenario to prove its effectiveness.

МЕТОДИКА СИНТЕЗА ОПТИМАЛЬНОЙ ПО ВРЕМЕНИ ТРАЕКТОРИИ ПОЛЕТА БЕСПИЛОТНОГО ВОЗДУШНОГО СУДНА

The article considers the method of synthesis of an optimal UAV flight path and an algorithm of the flight control system. The system and algorithm are designed for four-dimensional (4D) trajectory-based operations (TBO) in the context of the CNS/ATM and PBN concepts, which will increase the operational efficiency of the UAV navigation and piloting processes. The paper presents a mathematical model of the control object and algorithms for forming a time-optimal flight path. An assessment of the proposed methodology is also presented by verifying and validating the system software using simulation modeling. The results obtained demonstrate the functional capabilities of the control system to create time-optimal profiles of trajectories that meet operational requirements.

DEVELOPMENT OF CONTROL LAWS OF UNMANNED AERIAL VEHICLES FOR PERFORMING GROUP FLIGHT AT THE STRAIGHT-LINE HORIZONTAL FLIGHT STAGE

The article proposes an improved approach to controlling groups of unmanned aerial vehicles (UAVs) aimed at increasing the overall efficiency and flexibility of the control process. The use of a heterogeneous external field, which varies both in magnitude and direction, allows achieving greater adaptability and accuracy in controlling a group of UAVs. A vector field for unmanned aerial vehicles determines the direction and intensity of the vehicles' movement in space. Such vector fields can be used to develop UAV control laws, including determining optimal flight paths, controlling speed, avoiding obstacles, and ensuring coordination of a group of UAVs. The subject of the study is the methods of controlling groups of autonomous UAVs, where each vehicle may have different speeds and flight directions. To solve this problem, various methods of using a heterogeneous field have been developed and proposed. Instead of using a homogeneous field that provides a constant flight speed, a vector field is used that adapts to different conditions and characteristics of the vehicles in the group. This method allows for effective group management, ensuring the necessary coordination and interaction between the vehicles. An analysis of recent research and publications in the field of autonomous system control indicates the feasibility of using machine learning, vector fields, and a large amount of data to successfully coordinate the movement of autonomous systems. These approaches make it possible to create efficient and reliable control systems. The aim of the study is to develop laws for controlling the movement of a group of autonomous unmanned aerial vehicles at the stage of straight-line horizontal flight based on natural analogues to improve the efficiency and reliability of their coordinated movement in different conditions. The main conclusions of the research are that the proposed method of controlling groups of UAVs based on a heterogeneous field can be implemented. It takes into account a variety of vehicle characteristics and environmental conditions that are typical for real-world use scenarios. This work opens up prospects for further improving the management of UAV groups and their use in various fields of activity. The article emphasises the relevance of technology development for autonomous unmanned systems, especially in the context of autonomous transport systems.

Change in community annoyance at a vertiport by applying different approach/departure paths

The resent research on urban air mobility and its related air traffic noise is still focused on the understanding of air taxis' noise sources but already includes studies about potential future noise situations in the vicinity of vertiports. While the assessment of noise levels on the ground is still connected to unknowns by the lack of reliable noise source data and missing numbers of real air traffic movements, questions about the potential noise impact due to these novel air transport systems are already being asked, also under the aspect of a public acceptance. This study was performed to provide first answers. A realistic UAM use-case was investigated by noise simulations for a German community, delivering an insight how the annoyance in the small-town changes when applying different paths to connect the vertiport with its neighbor communities. The grade of annoyance and the way it can be influenced through flight path optimization is also compared to the change of the population's ratio, exposed to certain noise levels on the ground.

Research on logistics of intelligent unmanned aerial vehicle integration system

As Unmanned Aerial Vehicle (UAV) is more and more frequently used in farming and logistics, civil and military alike, researches involving UAVs also starts to boom. In the civil field, UAV is generally flown in urban areas, so buildings are the main factors hindering the normal flight of UAV. Therefore, it is necessary to calculate an optimal flight path of UAV under constraints. The intelligent logistics UAV proposed in this paper can be used to replace special couriers to deliver small goods. It is a quadcopter integrated with a webcam, ultrasound Ground Proximity Warning System (GPWS), and is controllable through a mobile APP. Gradient descent algorithm, Linear Quadratic Regulator (LQR) and improved Proportion-Integral-Derivative (PID) controllers are applied in its flight control system, MATLAB and wavelet transform to handle fuzzy image. In addition, ant colony algorithm and adaptive strategies are used in the path planning process. As a result, it can detect surrounding obstacles in-flight, and the ground control station can receive feedback information and prepare for emergency operations.

A Hybrid Improved Symbiotic Organisms Search and Sine–Cosine Particle Swarm Optimization Method for Drone 3D Path Planning

Given the accelerated advancement of drones in an array of application domains, the imperative of effective path planning has emerged as a quintessential research focus. Particularly in intricate three-dimensional (3D) environments, formulating the optimal flight path for drones poses a substantial challenge. Nonetheless, prevalent path-planning algorithms exhibit issues encompassing diminished accuracy and inadequate stability. To solve this problem, a hybrid improved symbiotic organisms search (ISOS) and sine–cosine particle swarm optimization (SCPSO) method for drone 3D path planning named HISOS-SCPSO is proposed. In the proposed method, chaotic logistic mapping is first used to improve the diversity of the initial population. Then, the difference strategy, the novel attenuation functions, and the population regeneration strategy are introduced to improve the performance of the algorithm. Finally, in order to ensure that the planned path is available for drone flight, a novel cost function is designed, and a cubic B-spline curve is employed to effectively refine and smoothen the flight path. To assess performance, the simulation is carried out in the mountainous and urban areas. An extensive body of research attests to the exceptional performance of our proposed HISOS-SCPSO.

Drone Stations-Aided Beyond-Battery-Lifetime Flight Planning for Parcel Delivery

This paper considers using drones to conduct the last-mile parcel delivery. To enable the beyond-battery-lifetime flight, drone stations are considered to replace or recharge the battery for drones. We focus on the flight planning problem with the goal of minimizing the total travel time from the depot to a customer, a key indicator of the quality of service. We investigate four typical ways for the drone to get extra energy at drone stations: 1) replacing the battery with a fresh one, 2) recharging the battery to the full capacity, 3) recharging the battery to the optimal level, and 4) recharging the battery to the optimal level accounting for the availability of drone stations (i.e., whether a drone station is occupied by other drones). While the first two scenarios can be formulated following the framework of integer linear programming, the last two scenarios turn into mixed-integer nonlinear programming problems. To address the later problems, we present a framework in which discretized state graphs are constructed first and then the optimal paths are found by graph searching algorithms. We propose a dynamic version of Dijkstra’s algorithm to deal with the unavailability issue of drone stations. The algorithm can quickly find the optimal flight path for a drone, and extensive computer-based experimental results have been presented to demonstrate the effectiveness of the proposed method. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Multi-rotary unmanned aerial vehicles (UAVs), also known as drones, have been regarded as a promising means to reshape future logistics. To save human labour and reduce cost, many giant logistics companies have been dedicated to developing various drones to deliver light and small parcels during the past decade. However, due to the limitation of payload, the battery capacity is constrained, which prevents drones from long-distance flights. Practitioners have tried the drone-vehicle collaboration method, but this still requires human labour to participate. In this paper, we present a framework where drones autonomously conduct long-distance delivery with the assistance of drone stations. It is worth pointing out that such a framework is not to replace the ground delivery method but to serve as an alternative to the ground counterpart for small and light parcels. A particular focus is on the flight planning from the depot to a destination, which includes not only a sequence of drone stations to stop at but also the corresponding rest time to recharge the battery. Several typical scenarios about battery recharging are discussed, and a dynamic version of Dijkstra’s algorithm is presented to deal with the challenging case where drone station resources are limited. The presented approach is able to find out the optimal flight plan quickly.

Optimal path planning using psychological profiling in drone‐assisted missing person search

AbstractSearch and rescue operations are all time‐sensitive and this is especially true when searching for a vulnerable missing person, such as a child or elderly person suffering dementia. Recently, Police Scotland Air Support Unit has begun the deployment of drones to assist in missing person searches with success, although the efficacy of the search relies upon the expertise of the drone operator. In this paper, several algorithms for planning the search path are compared to determine which approach has the highest probability of finding the missing person in the shortest time. In addition to this, the use of á priori psychological profile information of the subject to create a probability map of likely locations within the search area was explored. This map is then used within a nonlinear optimization to determine the optimal flight path for a given search area and subject profile. Two optimization solvers were compared; genetic algorithms, and particle swarm optimization. Finally, the most effective algorithm was used to create a coverage path for a real‐life location, for which Police Scotland Air Support Unit completed multiple test flights. The generated flight paths based on the predicted intent of the lost person were found to perform statistically better than those of the expert police operators.

A robust multiple Unmanned Aerial Vehicles 3D path planning strategy via improved particle swarm optimization

Path planning algorithms for Unmanned Aerial Vehicles (UAVs) are essential in various domains, such as search and rescue operations, agriculture, and delivery services. Nonetheless, finding optimal flight paths in complex environments with obstacles remains challenging. This paper proposes a novel path planning algorithm that uses a Nash equilibrium approach based on game theory to strike a balance between the exploitation and exploration capabilities of UAVs. The algorithm is designed in a simulated flight environment with obstacles, utilizing the self-awareness and group awareness coefficients from particle swarm optimization(PSO) as participants in the game theory model. The proposed path planning algorithm significantly improves the navigation of multiple UAVs in complex scenarios with obstacles by achieving a balance between their exploitation and exploration capabilities. Simulation experiments demonstrate that the proposed method surpasses traditional approaches, exhibiting an average improvement of 32.57%, 32.17%, and 29.33% in the algorithm convergence time, average flight distance, and average flight time, respectively. The significance of this research lies in its contribution to advancing UAV path planning algorithms. By integrating game theory and PSO, the proposed method optimizes the exploration and exploitation capabilities of UAVs, leading to improved navigation and resource utilization.

Planning the Optimal Reference Flight Path of an Aircraft Using a Terrain Map

In connection with the development of aviation technology, the expansion of the functionality of modern aircraft and the complication of the tasks being solved, additional stringent requirements are imposed on the accuracy of air navigation. Along with high accuracy, navigation autonomy is also required. The autonomy of aircraft navigation implies the navigation of an aircraft without the use of active radar facilities. In this article, to calculate the optimal reference flight path of an aircraft in a horizontal plane, the Bellman dynamic programming method is proposed using a digital terrain map. A technique, algorithms have been developed, and a numerical experiment has been carried out using fragments of a digital terrain map. High accuracy and autonomy of navigation are necessary, among other things, to ensure stealth (invisibility) of aircraft by ground-based radar facilities of a potential enemy in low-altitude flight mode. The best stealth of an aircraft flight in a low-altitude flight mode is achieved not only by flying around, but also by avoiding obstacles, due to the shielding properties of the earth’s surface. Autonomy of navigation provides periodic correction of the reference flight path during long-term flight, and high accuracy prevents aircraft collisions with the earth’s surface in low-altitude flight mode. The purpose of this work is to select the optimal reference trajectory (route) of an aircraft flight in the low-altitude flight mode. This article discusses a method developed based on the principle of dynamic programming for calculating the reference flight path in the horizontal plane depending on the DEM and the implementation of the algorithm based on the proposed method with a specific numerical example. The method makes it possible to calculate the optimal flight route, which provides the greatest secrecy of the aircraft in the low-altitude flight mode using the screening properties of the earth’s surface irregularities.

SGGTSO: A Spherical Vector-Based Optimization Algorithm for 3D UAV Path Planning

The application of 3D UAV path planning algorithms in smart cities and smart buildings can improve logistics efficiency, enhance emergency response capabilities as well as provide services such as indoor navigation, thus bringing more convenience and safety to people’s lives and work. The main idea of the 3D UAV path planning problem is how to plan to get an optimal flight path while ensuring that the UAV does not collide with obstacles during flight. This paper transforms the 3D UAV path planning problem into a multi-constrained optimization problem by formulating the path length cost function, the safety cost function, the flight altitude cost function and the smoothness cost function. This paper encodes each feasible flight path as a set of vectors consisting of magnitude, elevation and azimuth angles and searches for the optimal flight path in the configuration space by means of a metaheuristic algorithm. Subsequently, this paper proposes an improved tuna swarm optimization algorithm based on a sigmoid nonlinear weighting strategy, multi-subgroup Gaussian mutation operator and elite individual genetic strategy, called SGGTSO. Finally, the SGGTSO algorithm is compared with some other classical and novel metaheuristics in a 3D UAV path planning problem with nine different terrain scenarios and in the CEC2017 test function set. The comparison results show that the flight path planned by the SGGTSO algorithm significantly outperforms other comparison algorithms in nine different terrain scenarios, and the optimization performance of SGGTSO outperforms other comparison algorithms in 24 CEC2017 test functions.

Energy-Efficient Coverage and Capacity Enhancement With Intelligent UAV-BSs Deployment in 6G Edge Networks

With the development of 5G/6G networks, the number of wireless users is growing exponentially, and the application scenarios are increasingly diversified. Using unmanned aerial vehicles as base stations (UAV-BSs) to serve ground users has become a trend for wide area coverage and capacity enhancement for rapid access of service in 6G networks. However, as UAV-BSs have limited energy or battery storage, solutions to optimize energy efficiency while providing high-quality services are necessary. Therefore, this paper mainly concentrates on the energy-efficient deployment of coverage-aimed UAV-BSs (Co-UAV-BSs) and capacity-aimed UAV-BSs (Ca-UAV-BSs) for the coverage and capacity enhancement of ground communication under disaster areas or burst data traffic. First, Co-UAV-BSs are deployed with DQN algorithm to to get the UAV-BSs&#x2019; optimal flight paths, which mainly adopted to detect out of service users in such areas. Then the users are completely clustered based on the detection results. After that, Co-UAV-BSs and Ca-UAV-BSs are deployed hierarchically based on the user distribution and sought to optimize the energy efficiency with acceptable user services. Still, DQN algorithm and the A3C algorithm are used for obtaining all the UAV-BSs&#x2019; location deployment and users&#x2019; best connections. The simulation results show that the dynamic flying path requires less energy than the fixed path for user detecting. For the coverage and capacity enhancement, it reveals the solution we proposed could provide high-quality service for users with high energy efficiency comparing to traditional algorithms.