A cooperative perception system for multiple UAVs: Application to automatic detection of forest fires

Aerial robots can be considered as an evolution of the Unmanned Aerial Vehicles (UAVs). This book provides a quite complete overview of issues related to aerial robotics, addressing problems ranging from flight control to terrain perception and mission planning and execution. The major challenges and potentials of heterogeneous UAVs are comprehensively explored. It builds on the results of the European project COMETS, and highlights a number of key research topics in the area of UAV development, including communication, perception, teleoperation and decision making. The monograph emphasizes the current state of technology, the existing problems and potentialities of systems consisting of multiple UAVs which are heterogeneous in view of the different characteristics of the aerial vehicles, the different on-board payloads, and the different on-board information processing capabilities. The book also examines potential applications of UAVs and details a relevant application case: forest fire detection and monitoring.

PurposeThe purpose of this paper is to present the research efforts of the Center of Aeronautical Studies of the Federal University of Minas Gerais – Brazil to develop a low‐cost flight test data acquisition system for light aircraft and unmanned aerial vehicles (UAEs).Design/methodology/approachThe development of this system was based on a microcontroller, chosen in accordance with main requirements of light aircrafts flight tests. The system uses the microcontroller in order to communicate with different kinds of sensors, including a GPS, and organize this information to be sent to a PDA device, which is used to control the acquisition process and storage the data acquired. Details about the development of this system, including firmware algorithm and sensors development, are presented and discussed in the paper.FindingsThe paper presents example results obtained with this system in applications such as performance evaluation and stability and control derivatives estimation problems. Take into account all the aspects of the system and the quality of the results, the main conclusion is that this system can efficiently support the demands of the aerospace industry for light aircraft and UAEs development programs as well as the necessities of the research centers and universities developing aeronautical research and didactic programs.Practical implicationsRecently, results confirm the applicability of this system in order to perform flight tests of aircrafts in accordance with FAR‐Part 23 or CS‐VLA or Light Sport Aircrafts as required by FAA Order 8130.2F and ASTM Designation F2245‐04.Originality/valueThis paper presents details about the construction of a low‐cost data acquisition system for flight tests of light aircrafts. The main advantage of this system is the use of a PDA device in order to control and storage the acquisition, which reduce costs, weight and size of the system and permits its installation in light aircrafts or UAVs.

The classification of drones - both military and civilian according to the relevant criteria - is presented. Block diagrams of a typical unmanned aerial vehicle and its power system are shown. Typically, a drone power system of the "micro", "mini" or "short range" category, according to the UVS international classification, is a set of DC / DC converters with a microprocessor control system. The power source for such aircraft is usually a battery, less often - fuel cells. During the flight, the engine speed is variable, and its change depends not only on changes in flight speed or direction, but also on weather conditions, such as wind, as the stabilization system constantly aligns the drone. This leads to the fact that the power supply system of such a device is actually a significant part of the time in transition condition. This leads to a significant content of the component of exchange energy that battery consumed which in turn will increase losses, and thus reduces the range of the unmanned aerial vehicle. The phenomenon of the occurrence of exchange power in the power supply systems of electric vehicles which powered from DC sources and specifically in the power supply system of an unmanned aerial vehicle is analyzed. The time diagrams of current, voltage, active and inactive components of power consumed from the mains power supply network of electric locomotive DE1 are illustrated. It can be seen that with a sharp change in the modes of operation of traction units there is a significant component of exchange power in the motor-network system. Such processes are typical for almost any electric vehicle and are associated with the presence of a significant number of reactive elements in the power supply systems, as well as frequent changes in load parameters. A block diagram of a typical unmanned aerial vehicle is presented. The unmanned aerial vehicle system consists of three parts: the air part, the unmanned aerial vehicle itself, the ground control station, which can be autonomous or manned, the control system, which provides communication and data transmission. The block diagram of the power system of a typical unmanned aerial vehicle is presented A simplified schematic diagram of the DC motor power supply system is presented. The relations for determining the amount of exchange power in the power supply system of an unmanned aerial vehicle are derived. It is concluded that to reduce the impact of this phenomenon, it is necessary to modify the power supply system by adding compensation units of inactive power component.

Forest fires pose a significant threat to ecosystems and economic development. In recent years, unmanned aerial vehicles (UAVs) have emerged as a critical technology for forest fire monitoring due to their high mobility, low cost, and real-time surveillance capabilities. This paper provides a systematic review of research progress on UAV-based forest fire monitoring, focusing on three key aspects: hardware design, fire detection algorithm improvement, and multi-sensor data fusion technologies. First, it summarizes optimization strategies for UAV hardware, including sensor configurations, wing design, and power supply improvements, aimed at enhancing flight stability and environmental adaptability. Second, it analyzes advancements in fire detection algorithms, particularly the performance enhancement and lightweight modifications of deep learning models, and explores their applicability in high-noise environments. Finally, it evaluates the potential of multi-sensor data fusion techniques to improve fire detection accuracy by integrating temperature, smoke, and image data. Despite the significant advantages of UAVs in fire monitoring, challenges remain, such as hardware performance limitations, the trade-off between algorithm accuracy and real-time processing, and the complexity of multi-sensor coordination. Future research should focus on flight stability optimization, the development of novel detection algorithms, and the refinement of sensor integration techniques to further advance UAV applications in forest fire monitoring.

In this paper, a quadcopter unmanned aerial vehicle (UAV) system based on neural-network enhanced dynamic inversion control is proposed for multiple real-world application scenarios. A sigma-pi neural network (SPNN) is used as the compensator to reduce the model error and improve the system performance in the presence of the uncertainties of UAV dynamics, payload, and environment. Besides, we present a technical framework for fast and robust implementation of multipurpose UAV systems and develop a testbed for the evaluation of UAV control system by using a high-precision optical motion capture system. Both simulation results and experiment results demonstrate that the SPNN can reduce the inversion errors related to UAV parameter uncertainties as well as tracking errors related to unknown disturbances and unmodeled dynamics. With the help of an online neural network (NN) learning mechanism, the entire system can achieve much higher accuracy in attitude and trajectory control than that achieved by conventional proportional-integral derivative based control systems under varying flight conditions.

In this paper, we develop an alternative navigation system for Unmanned Aerial Vehicle (UAV) in Global Positioning Systems (GPS)-denied environment. We use two image inputs, one is acquired with an on-board camera placed on the UAV (which is the large-area image) and the other is from satellite images (which is small known image) with GPS information. We use a convolutional neural network (CNN) architecture based on Oxford’s Visual Geometry Group network (VGG-16) and utilize normalized variant mutual information between these two images to obtain position of the UAV. Satellite images are labelled and given to the UAV. When GPS information is lost, our algorithm starts to function and images from UAV camera are searched whether satellite image is seen by cameras on UAV image or not. If the UAV is in that area, our algorithm finds the GPS information from satellite image data.

NONLINEAR CONTROL FOR UAV FORMATION FLYING

The paper presents the analysis on stability of digital control system for unmanned vehicle with numerical analysis. The objective of this study is mainly emphasized on the fulfillment of the advanced control techniques according to the fundamental concepts of digital control system approaches for unmanned aerial vehicle system design. The targeted unmanned aerial vehicle system was designed based on the simple construction under the idea of fixed wing flight system approaches. The stability analyses on unmanned aerial vehicle are vital role to enhance the real world applications. The background concepts on digital control system for stability analysis on dynamic control system like unmanned aerial vehicle system. The appropriate controller design for dynamic control system of unmanned aerial vehicle is vital role to analyze the accurate stability condition for reality. The implementation of numerical analysis on compensator design has been developed by using MATLAB. The physical parameters in these analyses are based on the experimental outcomes from the recent research works in dynamical system implementations. The mathematical approaches are very helpful for numerical analysis on compensator design for the unmanned aerial vehicles schemes. The simulation results confirm that the high performance stability test on unmanned aerial vehicle system has been met with the theoretical works.

With the rapid development of intelligent information technology, human–computer interaction for unmanned aerial vehicle (UAV) systems has gradually developed from single-mode interaction to multi-modal interaction. Due to the problems of information ambiguity and operational redundancy in multi-modal interaction, and the inability to effectively realize the coupling of human–computer interaction of different modalities, a priority strategy for multi-modal interaction for UAV systems is proposed. Living cells or tissues, whether in a static or active state, will produce regular electrical phenomena called bioelectricity. Human–computer interaction intention recognition technology based on bioelectrical signals can significantly enhance the real-time performance and flexibility of human–computer interaction and has great development potential. The characteristics of human–computer interaction intention recognition methods based on bioelectrical signals, such as electroencephalograms (EEGs), electromyograms (EMGs), electrooculograms (EOGs), and other conventional methods are introduced and analyzed, and then the basic characteristics of single-mode interaction and multi-modal interaction are compared. Furthermore, the priority strategies for UAV systems under different interaction modes are studied based on the analysis of task value evaluation parameters. The cumulative value, urgency, and task complexity are comprehensively analyzed and evaluated, and a set of priority strategies for multi-modal interaction is integrated. Through the simulation and verification of the multi-modal human–computer interaction software of the UAV system, it is determined that the proposed priority strategy conforms to the operation intention of the user, which improves the interaction efficiency and the reliability. In noisy environments, the UAV system control can be combined with the complementary information of various interaction methods to achieve precise control, which has obvious advantages.

Analysis of a hydrogen fuel cell-PV power system for small UAV

Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption.

The study focuses on the analysis and optimization of energy supply systems for unmanned aerial vehicles (UAVs), specifically FPV drones equipped with 10-inch propellers, to increase flight duration and operational efficiency under various civilian application scenarios. With the rapid expansion of drone usage in fields such as agriculture, environmental monitoring, logistics, and technical inspection, the issue of selecting the most efficient power source has become particularly relevant. The research aims to determine the optimal type of battery in terms of weight, energy density, endurance, and cost-effectiveness, ensuring reliable operation and sufficient energy reserve for different mission profiles. The article presents a comparative analysis between a lithium-polymer (LiPo) battery pack (6S, 10Ah) and three lithium-ion (Li-Ion) battery assemblies based on Molicel P42A cells with different configurations: 6S2P, 6S3P, and 6S4P. The analysis includes both theoretical calculations and practical considerations derived from known empirical data. Each configuration was evaluated based on three main criteria – flight time, maximum range, and overall economic viability. Additionally, the influence of payload mass (0–3 kg) on flight duration and efficiency was modeled for six key mission scenarios: observation and video surveillance, light and medium cargo delivery, heavy cargo delivery, one-way missions, and high-speed flight operations. Methodologically, the work takes into account motor efficiency (0.8–0.85), aerodynamic drag, and the voltage behavior of both LiPo and Li-Ion systems under load. The results demonstrate that Li-Ion batteries offer a higher specific energy density (up to 180 Wh/kg) and lower cost per watt-hour compared to LiPo alternatives, though their discharge characteristics limit maximum current output. Among the tested configurations, the Li-Ion 6S3P assembly showed the most balanced performance, providing a flight time of up to 45 minutes without payload and maintaining stable voltage throughout the discharge cycle. The LiPo 6S 10Ah pack remains advantageous for high-current, short-duration missions such as racing or rapid-response operations due to its superior instantaneous power output. The study concludes that no single battery configuration is universally optimal for all use cases. However, the Li-Ion 6S3P pack can be considered the most versatile and practical solution for multi-purpose civilian FPV platforms, combining reasonable cost, low weight, and extended endurance. The article also provides recommendations for selecting power systems depending on the mission profile and discusses the practical aspects of battery assembly, including the use of bi-metal copper and nickel-plated steel strips (0.15×10 mm) to ensure low resistance and reliable current transfer during high-load operation. The findings highlight the need for further experimental validation under real-world flight conditions, as well as future research focused on optimizing propeller geometry, assessing temperature impacts, and integrating hybrid power systems for small UAVs. The proposed analytical framework and results can serve as a methodological foundation for engineers, drone developers, and energy system designers seeking to enhance the performance and energy efficiency of modern FPV drones

A system of unmanned aerial vehicles (UAVs) can be used to provide uninterrupted service to customers by handing the mission from a weary UAV to a fresh one in an automated manner. Toward such a system, we discuss the development of two components: ROS GPS waypoint tracking package and centralized task allocation network system (CTANS). The ROS GPS waypoint tracking package is the first open source software developed for the AR Drone 2.0 UAV that enables integration with a larger system. CTANS connects the UAV host computers to a central computer, receives the UAV status information and distributes GPS flight directives. CTANS is responsible for distributing the commands to the UAVs to ensure mission handoffs are conducted as required. This multi-UAV control software testbed was used to implement a small scale, outdoor, persistent, GPS based patrol mission with two UAVs.

In recent years, Unmanned Aerial Vehicles (UAVs) have attracted a lot of attention due to their flexibility and mobility. However, due to the increasingly complex environments faced by UAVs and the rising demands on UAV systems, traditional UAV control methods can no longer efficiently control the UAV under multi-constraint situations. Reinforcement Learning (RL), as an emerging robot control technology, is well suited to the needs of UAV systems in terms of its ability to interact with and learn from the environment. Therefore, RL-based UAV systems are gradually becoming a new trend in research. Nonetheless, as a new research field, it faces some challenges. To fully grasp the landscape of RL-based UAV systems, it is paramount to provide a comprehensive overview and analysis of the existing specific RL methods applied to UAV systems. In this survey, we first provide a comprehensive overview and summary of the application of RL in different UAV scenarios based on the classification of RL methods. After that, based on the existing relevant literature, we conduct a systematic analysis of the challenges and recent advancements when applying RL to UAV systems. Finally, we discuss the potential research directions for RL-based UAV systems.

In this paper a multiple object detection, recognition, and tracking system for unmanned aerial vehicles (UAVs) has been studied. The system can be implemented on any UAVs platform, with the main requirement being that the UAV has a suitable onboard computational unit and a camera. It is intended to be used in a maritime object tracking system framework for UAVs, which enables a UAV to perform multiobject tracking and situational awareness of the sea surface, in real time, during a UAV operation. Using machine vision to automatically detect objects in the camera's image stream combined with the UAV's navigation data, the onboard computer is able to georeference each object detection to measure the location of the detected objects in a local North‐East (NE) coordinate frame. A tracking algorithm which uses a Kalman filter and a constant velocity motion model utilizes an object's position measurements, automatically found using the object detection algorithm, to track and estimate an object's position and velocity. Furthermore, a global‐nearest‐neighbor algorithm is applied for data association. This is achieved using a measure of distance that is based not only on the physical distance between an object's estimated position and the measured position, but also how similar the objects appear in the camera image. Four field tests were conducted at sea to verify the object detection and tracking system. One of the flight tests was a two‐object tracking scenario, which is also used in three scenarios with an additional two simulated objects. The tracking results demonstrate the effectiveness of using visual recognition for data association to avoid interchanging the two estimated object trajectories. Furthermore, real‐time computations performed on the gathered data show that the system is able to automatically detect and track the position and velocity of a boat. Given that the system had at least 100 georeferenced measurements of the boat's position, the position was estimated and tracked with an accuracy of 5–15 m from 400 m altitude while the boat was in the camera's field of view (FOV). The estimated speed and course would also converge to the object's true trajectories (measured by Global Positioning System, GPS) for the tested scenarios. This enables the system to track boats while they are outside the FOV of the camera for extended periods of time, with tracking results showing a drift in the boat's position estimate down to 1–5 m/min outside of the FOV of the camera.