Unmanned Aerial Vehicle Mission Research Articles

Research on the Messenger UAV Mission Planning Based on Sampling Transformation Algorithm

In recent years, there has been a significant development in unmanned platform technologies, specifically unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs). As a result, their application scenarios have expanded considerably. Unmanned platforms are considered integral components of the Internet of Things system. However, certain challenges arise when dealing with specialized tasks, such as navigating complex urban low-altitude terrain with multiple obstacles and limited communication capabilities. These challenges can greatly impact the efficiency of the system due to information isolation. To address this issue, a messenger drone mechanism is introduced in this paper, which utilizes air superiority to facilitate indirect communication between unmanned platforms. Additionally, a task sequence planning algorithm based on sampling transformation is designed. This algorithm efficiently assigns the drone to mobile UGVs by discretely sampling their paths and considering the UAV-UGV motion relationship. By transforming the problem into an asymmetric traveler problem, it allows for a fast solution. Finally, the effectiveness of the algorithm is verified through comparative analysis in different scenarios.

Deep Reinforcement Learning-Based 3D Trajectory Planning for Cellular Connected UAV

To address the issue of limited application scenarios associated with connectivity assurance based on two-dimensional (2D) trajectory planning, this paper proposes an improved deep reinforcement learning (DRL) -based three-dimensional (3D) trajectory planning method for cellular unmanned aerial vehicles (UAVs) communication. By considering the 3D space environment and integrating factors such as UAV mission completion time and connectivity, we develop an objective function for path optimization and utilize the advanced dueling double deep Q network (D3QN) to optimize it. Additionally, we introduce the prioritized experience replay (PER) mechanism to enhance learning efficiency and expedite convergence. In order to further aid in trajectory planning, our method incorporates a simultaneous navigation and radio mapping (SNARM) framework that generates simulated 3D radio maps and simulates flight processes by utilizing measurement signals from the UAV during flight, thereby reducing actual flight costs. The simulation results demonstrate that the proposed approach effectively enable UAVs to avoid weak coverage regions in space, thereby reducing the weighted sum of flight time and expected interruption time.

Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning

In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.

A semantic SLAM-based method for navigation and landing of UAVs in indoor environments

Autonomous navigation and landing of Unmanned aerial vehicles (UAVs) are critical functions towards full intelligence in unknown environments. However, current methods heavily rely on positioning devices or geometric maps, leading to two limitations: (1) UAVs fail to work normally indoors when signals are blocked, and (2) Navigation systems are not capable of achieving high-level decision-making missions. In this paper, we propose a novel and complete framework to realize the autonomous landing of UAVs in unknown indoor scenes based on visual SLAM, semantic segmentation, terrain estimation, and a decision-making model. Firstly, our 3D map is built on top of the visual SLAM system with semantic features, and then multiple types of maps (e.g., texture map, octo-map, grid-map, and semantic topology) are created for different function requirements. To achieve high-level scene understanding, we design a data association rule to fuse semantic features extracted by a deep learning model into the mapping process of topology structure. Next, a terrain estimation strategy is performed in the lightweight grid-map, which is modeled only via low-level elevation representation. Through multiple terrain constraints factor, a few optimized landing sites are selected in safe regions via clustering algorithm. Finally, based on the guidance of semantic topology, we construct a decision-making model for UAV landing that effectively integrates terrain safety, environment perception, and path planning. We perform extensive autonomous landing experiments on multiple indoor scenes from the TUM RGB-D dataset, and the full unknown environment maps are created simultaneously. The experiment proves that the proposed framework can select an optimal landing site for the advanced mission of UAVs effectively.

GA-Net: Accurate and Efficient Object Detection on UAV Images Based on Grid Activations

Object detection plays a crucial role in unmanned aerial vehicle (UAV) missions, where captured objects are often small and require high-resolution processing. However, this requirement is always in conflict with limited computing resources, vast fields of view, and low latency requirements. To tackle these issues, we propose GA-Net, a novel approach tailored for UAV images. The key innovation includes the Grid Activation Module (GAM), which efficiently calculates grid activations, the probability of foreground presence at grid scale. With grid activations, the GAM helps filter out patches without objects, minimize redundant computations, and improve inference speeds. Additionally, the Grid-based Dynamic Sample Selection (GDSS) focuses the model on discriminating positive samples and hard negatives, addressing background bias during training. Further enhancements involve GhostFPN, which refines Feature Pyramid Network (FPN) using Ghost module and depth-wise separable convolution. This not only expands the receptive field for improved accuracy, but also reduces computational complexity. We conducted comprehensive evaluations on DGTA-Cattle-v2, a synthetic dataset with added background images, and three public datasets (VisDrone, SeaDronesSee, DOTA) from diverse domains. The results prove the effectiveness and practical applicability of GA-Net. Despite the common accuracy and speed trade-off challenge, our GA-Net successfully achieves a mutually beneficial scenario through the strategic use of grid activations.

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.

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning.

With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm's global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work.

Towards Autonomous Firefighting UAVs: Online Planners for Obstacle Avoidance and Payload Delivery

Drone technology is advancing rapidly and represents significant benefits during firefighting operations. This paper presents a novel approach for autonomous firefighting missions for Unmanned Aerial Vehicles (UAVs). The proposed UAV framework consists of a local planner module that finds an obstacle-free path to guide the vehicle toward a target zone. After detecting the target point, the UAV plans an optimal trajectory to perform a precision ballistic launch of an extinguishing ball, exploiting its kinematics. The generated trajectory minimises the overall traversal time and the final state error while respecting UAV dynamic limits. The performance of the proposed system is evaluated both in simulations and real tests with randomly positioned obstacles and target locations. The proposed framework has been employed in the 2022 UAV Competition of the International Conference on Unmanned Aircraft Systems (ICUAS), where it successfully completed the mission in several runs of increasing difficulty, both in simulation and in real scenarios, achieving third place overall. A video attachment to this paper is available on the website https://www.youtube.com/watch?v=_hdxX2xXkVQ.

STEAM: Spatial Trajectory Enhanced Attention Mechanism for Abnormal UAV Trajectory Detection

Accurate unmanned aerial vehicle (UAV) trajectory tracking is crucial for the successful execution of UAV missions. Traditional global positioning system (GPS) methods face limitations in complex environments, and visual observation becomes challenging with distance and in low-light conditions. To address this challenge, we propose a comprehensive framework for UAV trajectory verification, integrating a range-based ultra-wideband (UWB) positioning system and advanced image processing technologies. Our key contribution is the development of the Spatial Trajectory Enhanced Attention Mechanism (STEAM), a novel attention module specifically designed for analyzing and classifying UAV trajectory patterns. This system enables real-time UAV trajectory tracking and classification, facilitating swift and accurate assessment of adherence to predefined optimal trajectories. Another major contribution of our work is the integration of a UWB system for precise UAV location tracking, complemented by our advanced image processing approach that includes a deep neural network (DNN) for interpolating missing data from images, thereby significantly enhancing the model’s ability to detect abnormal maneuvers. Our experimental results demonstrate the effectiveness of the proposed framework in UAV trajectory tracking, showcasing its robust performance irrespective of raw data quality. Furthermore, we validate the framework’s performance using a lightweight learning model, emphasizing both its computational efficiency and exceptional classification accuracy.

Towards sustainable UAV operations: Balancing economic optimization with environmental and social considerations in path planning

Unmanned Aerial Vehicles (UAVs), i.e., drones, are expected to be widely used in various applications, such as parcel delivery and passenger transport, with the benefits of mitigating traffic congestion and reducing carbon emissions. In this paper, we study a UAV path planning problem under uncertain weather conditions, and design a data-driven dynamic decision support system for multiple types of UAVs. To this end, we categorize all relevant costs into three types, namely, economic, environmental, and social costs, and formulate a nonlinear two-stage stochastic programming model to establish optimal paths for UAV missions under weather uncertainty. We then discretize the nonlinear model and propose a tight linear approximation for the discretized problem to allow for a near real-time implementation. To quantify weather uncertainty, we propose a weather scenario generation algorithm to map ensemble-based weather forecast information to airspace blockage maps. With comprehensive computational studies through simulations, we show that our proposed stochastic approach can lower operating costs by an average of around 6%, where the savings increase as weather conditions become more severe and complex. We also find that, for missions operated by small UAVs, it is not sufficient to determine a path solely based on economic cost minimization, but it should rather be through total cost minimization, which involves environmental and social costs. Considering only the economic cost in the optimization may lead to much higher non-economic costs. However, for missions operated by large UAVs, it is sufficient to determine paths through economic cost optimization, as including environmental and social costs in the optimization process does not result in solutions that are much different from those obtained by considering only the economic costs. For both small and large UAVs, a path established solely through environmental or social cost minimization may not be economically sustainable, as doing so would imply very high economic costs.

Evolutionary game analysis of behaviour strategy for UAV swarm in communication‐constrained environments

AbstractIn the process of unmanned aerial vehicles (UAV) cluster coordination, communication obstruction may cause situational misjudgement and increase the risk of decision‐making errors. Therefore, based on evolutionary game theory, this research aimed to study the behaviour decision‐making selection problem in the process of UAV swarm missions in limited communication networks. First, the evolution process of the UAV swarm confrontation game is analyzed and the decision‐making risks associated with the communication network of the UAV swarm during coordination, which are caused by the complex confrontation environment are identified. Second, the decision‐making risks and mechanisms of UAV swarm communication is discussed and an evolutionary game mathematical model for the cooperative strategy of a UAV swarm under communication constraints is established. Finally, the influence of various key parameters is simulated and analyzed, such as the initial strategy probability of both parties, amount of platform information data, information analysis and utilization ability, and information interaction coefficient, on the evolution of the UAV swarm strategy. This research provides a theoretical basis and feasible reference for the improvement of UAV mission decision response mechanisms and system model construction, which have important theoretical value and practical significance.

UAV mission scheduling with completion time, flight distance, and resource consumption constraints

Unmanned aerial vehicles (UAVs) are widely used in various military and civilian applications. UAV mission scheduling is a key issue in UAV applications and a central topic in UAV research. UAV task scheduling should include several constraints into consideration, such as completion time constraint, flight distance constraint, and resource consumption constraint. Furthermore, UAV task scheduling should be studied within the traditional framework of combinatorial optimisation. In this paper, we consider optimal mission scheduling for heterogeneous UAVs with completion time, flight distance, and resource consumption constraints. The contributions of the paper are summarised as follows. We define two combinatorial optimisation problems, namely, the NFTM (number of finished tasks maximisation) problem and the RFTM (reward of finished tasks maximisation) problem. We construct an algorithmic framework for both NFTM and RFTM problems, so that our heuristic algorithms (four for NFTM and two for RFTM) can be presented in a unified way. We derive upper bounds for optimal solutions, so that our heuristic solutions can be compared with optimal solutions. We experimentally evaluate the performance of our heuristic algorithms. To the best of our knowledge, this is the first paper studying UAV mission scheduling with time, distance, and resource constraints as combinatorial optimisation problems.

Finite-Time Anti-Saturated Formation Tracking Control of Multiple Unmanned Aerial Vehicles: A Performance Tuning Way

A highly effective control method is very important to guarantee the safety of the formation of flying missions for multiple unmanned aerial vehicles (UAVs), especially in the presence of complex flying environments and actuator constraints. In this regard, this paper investigates the formation tracking control problem of multiple UAVs in the presence of actuator saturation. Firstly, a brand-novel finite-time anti-saturated control scheme is proposed for multiple UAVs to track the desired position commands, wherein the tracking performance is tuned by introducing a logarithmic function-based state-mapping policy. Then, an adaptive scheme based on projection rules is devised to compensate for the negative effects brought by the actuator saturation. Based on the proposed formation tracking controller, the finite-time formation tracking performance tuning and control saturation problems can be addressed simultaneously with a comparatively allowable system robustness. Finally, three groups of illustrative examples are organized to verify the effectiveness of the proposed formation tracking control scheme.

Optimization of unmanned aerial vehicle inspection strategy for infrastructure based on model-enabled diagnostics and prognostics

The use of unmanned aerial vehicles (UAVs) for structural health inspection has become a promising technique to perform labor-intensive, accessibility-challenged, and sometimes dangerous inspection tasks. This paper presents a novel physics-informed UAV inspection planning framework for infrastructure structural health assessment based on model-based diagnostics and prognostics enabled by physics-based probabilistic analysis. It bridges the gap between UAV mission planning and inspection with model-based probabilistic analysis, by allowing bidirectional information exchange, namely (1) structural damage state diagnostics using UAV inspection data and (2) UAV inspection optimization through model-based failure prognostics. Based on the bidirectional communication, the impacts of the three key UAV inspection parameters (i.e., inspection distance, inspection interval, and critical maintenance threshold) on structural life-cycle cost are analyzed. The optimization of key UAV inspection parameters is achieved by minimizing the cost per unit time (CPUT) through model-based pre-posterior analysis. In this analysis, synthetic observations are generated using predictive models according to the prior distributions of various uncertainty sources (e.g., detection rate, damage state evolution, etc.). The generated synthetic observations are then used to obtain the posterior distributions of uncertain parameters, enabling the integration of prior information and Bayesian model updating into inspection optimization through a cost function. The proposed model offers a robust method to accommodate the inherent uncertainties in failure prognostics, leading to a more effective optimization of the UAV inspection parameters. The practical application of the framework is demonstrated through a miter gate example. The results show that the proposed method is able to efficiently determine the optimal UAV inspection parameters and continuously update the information model.

Improved path planning method for unmanned aerial vehicles based on artificial potential field

With the advancement of unmanned aerial vehicle (UAV) technology and its widespread use in many facets of manufacturing and daily life, the need for UAV mission automation is becoming more and more practical. In order to improve the automatic obstacle avoidance and path planning performance of UAVs, this essay proposes an optimized route planning algorithm based on the artificial potential field (APF) method, which have solved the typical issue of the APF. This method chooses to conduct a pre planning trajectory of the UAV based on a rapidly expanding random tree (RRT). The pre-planned path will be split into continuous particles, and then generating intermediate waypoints. A nearby waypoint offers a gravitational force to aid the UAV in escaping the local minimum when it enters it. At the same time, taking the distance from the UAV to the obstacle and the radius of influence of the obstacle itself into consideration, dynamically adjust the gravitational and repulsive coefficients, set up a non-gravitational zone around the obstacle, which is beneficial for the UAV to elude the obstacle. And set up a repulsive limited action zone to reduce unnecessary turns in the trajectory to achieve the effect of path optimization. Considering the difficulty of single UAV missions in most cases, this paper discusses cooperative flight path planning for multiple UAVs into consideration.

The Application of UAVs in the Evaluation of Thermal Comfort Levels in Buildings Equipped with Internal Greenhouses

A greenhouse is used to improve thermal comfort (TC) levels for its occupants in winter conditions using solar radiation, which involves low energy consumption. The aim of this research is the application of unmanned aerial vehicles (UAVs) in the evaluation of thermal comfort levels in buildings equipped with internal greenhouses. The new building design is developed numerically, and a building thermal simulator (BTS) numerical model calculates the indoor environmental variables. A new alternative and expeditious method to measure occupants’ comfort levels using UAV technology is applied using a UAV dynamic simulator (UAV DS). The evolution of the measured variables used for evaluating the predicted mean vote (PMV) is compared using the two numerical methodologies: BTS and UAV DS. In the second one, the mean radiant temperature (MRT) measuring methodology, the floor temperature, the lateral walls’ temperatures, the ceiling temperatures, and the air temperature are applied. In the method presented in this paper, a new building design is developed numerically, which includes a central greenhouse equipped with a semispherical dome, four auditoriums distributed around the central greenhouse, occupant distribution, and a ventilation methodology. The building geometry, the solar radiation on transparent surfaces, the TC, and the UAV mission methods are presented. The results show that, in general, the central greenhouse and the ventilation methodologies provide acceptable TC levels. The UAV monitoring mission, which includes two vehicles, provides good environmental variable replication, particularly when the environmental variables present greater variations. In the auditorium and greenhouse, the ceiling and lateral surface temperatures, respectively, can be used as an MRT approximation. The BTS numerical model is also important for developing buildings using renewable energy sources to improve the TC levels.

An Efficient Framework for Autonomous UAV Missions in Partially-Unknown GNSS-Denied Environments

Nowadays, multirotors are versatile systems that can be employed in several scenarios, where their increasing autonomy allows them to achieve complex missions without human intervention. This paper presents a framework for autonomous missions with low-cost Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System-denied (GNSS-denied) environments. This paper presents hardware choices and software modules for localization, perception, global planning, local re-planning for obstacle avoidance, and a state machine to dictate the overall mission sequence. The entire software stack has been designed exploiting the Robot Operating System (ROS) middleware and has been extensively validated in both simulation and real environment tests. The proposed solution can run both in simulation and in real-world scenarios without modification thanks to a small sim-to-real gap with PX4 software-in-the-loop functionality. The overall system has competed successfully in the Leonardo Drone Contest, an annual competition between Italian Universities with a focus on low-level, resilient, and fully autonomous tasks for vision-based UAVs, proving the robustness of the entire system design.

A novel waypoint guidance and adaptive evolution strategy for unmanned aerial vehicle 3D route planning

In the field of unmanned aerial vehicles (UAVs) mission planning, autonomous route planning in complex 3D environments represents a crucial aspect. To enhance the utilization of valuable information and increase search efficiency when solving path planning problems in 3D environments, a novel UAV route planning algorithm named WG-GWO is proposed with a waypoint guidance and an adaptive evolution strategy. Initially, we introduce a novel waypoint extraction method based on cubic B-spline to harness crucial information from individuals eliminated during the evolutionary process. Subsequently, an adaptive evolutionary direction selection strategy is introduced, which utilizes historical information and a differential operator to improve the search ability. The WG-GWO also incorporates Le´vy flight to prevent premature convergence to local minimums. Simulation results validate the effectiveness of the proposed algorithm, demonstrating that WG-GWO outperforms several existing algorithms in generating feasible routes and achieving faster convergence speeds across various scenarios. These results suggest that WG-GWO holds potential for practical engineering applications.

A path planning approach for unmanned solar-powered aerial vehicles

Solar energy is an available high-quality source for the operation of many technological systems that fulfil the needs of human society. Among all the many applications, the use of solar energy in aircraft is extremely challenging, with a lot of potential advantages. The application of solar energy for flight is one of the promising uses of renewable energy, and in particular the possibility of improving the long endurance requirement in complex missions of unmanned aerial vehicles (UAVs) using solar energy is very attractive. In this context, the article deals with the problem of flight path planning in solar-powered UAVs that fly at a constant altitude. The UAV model incorporates kinematic and dynamic equations where the components of the input vector are the thrust force and the bank angle. Planning the flight path relies here on the flatness property of the system. In fact, his property allows us to plan a flight path without solving non-linear differential equations, and most importantly to link the dynamics of the system, the energy lost and the solar energy absorbed during the flight. Some relevant applications of the approach are discussed, and numerical results are demonstrated. Key words. Solar energy, UAV control, kinematic & dynamic equations, path planning, differentially flat system.

Resource-Efficient Coverage Path Planning for UAV-Based Aerial IoT Gateway

Unmanned Aerial Vehicles (UAVs) have a lot of potential for developing new applications in a variety of fields, such as traffic monitoring, security, and military applications. In the vast nature of the Internet of Things (IoT) network, UAVs could work as Aerial Gateway (AG) for communications among low-powered and distributed ground IoT Devices (IDs). This research concentrates on the path planning and deployment system that may facilitate decisionmaking and guaranteed resource-efficient UAV mission assignment in serving ground IDs. Due to limited resources, it is essential to take into account several factors when designing such a system, including the AG flight time, the coverage radius, and the ground-to-air system's achievable data rate. As a result, the Energy Efficient Coverage Path Planning (EECPP) algorithm has been proposed. The EECPP is composed of two algorithms: the Stop Point Prediction Algorithm using K-Means, and Path Planning Algorithm using Particle Swarm Optimization. The outcome demonstrates that, in terms of total flight distance, EECPP outperforms Close Enough Traveling Salesman Problem (CETSP) by 19.99%. EECPP reduced energy usage by an average of 56.15% as opposed to Energy-Efficient Path Planning (E2PP). Due to its mobility nature with the addition of effective path planning, the AG is able to hover at each stop point, making it ideal for usage in crowded regions with high demand, emergency circumstances, and distant locations with no access to fixed base stations.