Autonomous Unmanned Aerial Vehicles Research Articles

Comparative Review of Navigation Systems for Indoor Autonomous Unmanned Aerial Vehicles

Comparative Review of Navigation Systems for Indoor Autonomous Unmanned Aerial Vehicles

UAV Autonomous Navigation Based on Deep Reinforcement Learning in Highly Dynamic and High-Density Environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) based on deep reinforcement learning (DRL) has made great progress. However, most studies assume relatively simple task scenarios and do not consider the impact of complex task scenarios on UAV flight performance. This paper proposes a DRL-based autonomous navigation algorithm for UAVs, which enables autonomous path planning for UAVs in high-density and highly dynamic environments. This algorithm proposes a state space representation method that contains position information and angle information by analyzing the impact of UAV position changes and angle changes on navigation performance in complex environments. In addition, a dynamic reward function is constructed based on a non-sparse reward function to balance the agent’s conservative behavior and exploratory behavior during the model training process. The results of multiple comparative experiments show that the proposed algorithm not only has the best autonomous navigation performance but also has the optimal flight efficiency in complex environments.

Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development

Autonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVs’ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithm’s efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability.

An Adaptive Missing Data Restoration Method for UAV Confrontation Based on Deep Regression Model

Completing missions with autonomous decision-making unmanned aerial vehicles (UAV) is a development direction for future battlefields. UAV make decisions based on battlefield situation information collected by sensors and can quickly and accurately perform complex tasks such as path planning, cooperative reconnaissance, cooperative pursuit and attacks. Obtaining real-time situation information of enemy is the basis for realizing autonomous decision-making of the UAV. However, in practice, due to internal sensor failure or interference of enemy, the acquired situation information is prone to be missing, which affects the training and decision-making of autonomous UAV. In this paper, an adaptive missing situation data restoration method for UAV confrontation is proposed. The UAV confrontation situation data are acquired through JSBSim, an open-source UAV simulation platform. By fusing temporal convolutional network and long short-term memory sequences, we establish a deep regression method for missing data restoration and introduce an adaptive mechanism to reduce the training time of the restoration model in response to dynamic changes in the enemy’s strategy during UAV confrontation. In addition, we evaluate the reliability of the proposed method by comparing with different baseline models under different degrees of data missing conditions. The performance of our method is quantified by five metrics. The performance of our proposed method is better than the other benchmark algorithms. The experimental results show that the proposed method can solve the missing data restoration problem and provide reliable situation data while effectively reducing the training time of the restoration model.

Anytime algorithm based on adaptive variable-step-size mechanism for path planning of UAVs

For autonomous Unmanned Aerial Vehicles (UAVs) flying in real-world scenarios, time for path planning is always limited, which is a challenge known as the anytime problem. Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out, making UAVs more adaptable to different mission scenarios. However, current anytime algorithms based on A* have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles. This paper proposes a novel anytime path planning algorithm, Anytime Radiation A* (ARaA*), which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient. The ARaA* features two main innovations: an adaptive variable-step-size mechanism and elliptic constraints based on waypoints. The former helps achieve fast path searching in various environments. The latter allows ARaA* to control the suboptimality bounds of paths and further enhance search efficiency. Simulation experiments show that the ARaA* outperforms Anytime Repairing A* (ARA*) and Anytime D* (AD*) in controlling suboptimality bounds and planning time, especially in environments with large concave obstacles. Final flight experiments demonstrate that the paths planned by ARaA* can ensure the safe flight of quadrotors.

Autonomous UAV navigation using deep learning-based computer vision frameworks: A systematic literature review

The increasing use of unmanned aerial vehicles (UAVs) in both military and civilian applications, such as infrastructure inspection, package delivery, and recreational activities, underscores the importance of enhancing their autonomous functionalities. Artificial intelligence (AI), particularly deep learning-based computer vision (DL-based CV), plays a crucial role in this enhancement. This paper aims to provide a systematic literature review (SLR) of Scopus-indexed research studies published from 2019 to 2024, focusing on DL-based CV approaches for autonomous UAV applications. By analyzing 173 studies, we categorize the research into four domains: sensing and inspection, landing, surveillance and tracking, and search and rescue. Our review reveals a significant increase in research utilizing computer vision for UAV applications, with over 39.5 % of studies employing the You Only Look Once (YOLO) framework. We discuss the key findings, including the dominant trends, challenges, and opportunities in the field, and highlight emerging technologies such as in-sensor computing. This review provides valuable insights into the current state and future directions of DL-based CV for autonomous UAVs, emphasizing its growing significance as legislative frameworks evolve to support these technologies.

Research on Motion Control and Compensation of UAV Shipborne Autonomous Landing Platform

As an important interface between unmanned aerial vehicles (UAVs) and ships, the stability and motion control compensation technology of the shipborne UAV landing platform are paramount for successful UAV landings. This paper has designed a new control compensation method for an autonomous UAV landing platform to address the impact of complex sea conditions on the stability of UAV landing platforms. Firstly, the parallel Stewart platform was introduced as the landing platform, and its structure was analyzed with forward and inverse kinematic calculations conducted in Matlab to verify its accuracy. Secondly, a least-squares recursive AR prediction algorithm was designed to predict the future attitudes of ships under varying sea conditions. Finally, the prediction algorithm was combined with the platform’s control strategy and a dual-sensor system was adopted to ensure the stability of the UAV landing process. The experimental results demonstrate that these innovative improvements enhanced the compensation accuracy by 59.6%, 60.3%, 48.4%, and 47.9% for the rolling angles of 5° and 10° and the pitching angles of 5° and 10°, respectively. Additionally, the compensation accuracy for the roll and pitch in sea states 2 and 5 improved by 51.2%, 59.4%, 58.7%, and 55.9%, respectively, providing technical support for UAV missions such as maritime rescue and exploration.

UPKD: Unsupervised pylon keypoint detection from 3D LiDAR data for autonomous UAV power inspection

The automatic extraction of inspection points for pylons is essential for intelligent Unmanned Aerial Vehicle (UAV) power inspections, especially in generating inspection route. However, current researches primarily focus on power scene perception and power component fault detection, with little attention paid to the inspection point detection. The primary reason are the lack of public pylon datasets for keypoint detection and the neglect of distinguish the inspection points from the keypoints used in feature extraction. Regarding that, this paper proposes a two-stage unsupervised pylon keypoint detection (UPKD) method to improve the efficiency of power inspections. In the first stage, the UPKD Network processes the point cloud to generate candidate keypoints, which comprises two main components: a data normalization module and an unsupervised keypoint detection network (UKD-Net). The data normalization module compresses information based on the symmetric structure of pylons, thereby reducing instability in inspection point detection. The UKD-Net incorporates a Point Transformer layer that uses self-attention mechanisms to extract features from the point cloud. In the second stage, a convex optimization strategy is applied to filter and acquire inspection points. These inspection points are then interconnected using a shortest-path strategy to generate the UAV inspection route. Our dataset is obtained using the Riegl VUX-1 laser measurement system and comprises 3,296 pylons of 10 types. Each pylon’s point cloud contains up to 25,000 points, with a high point density of 100 pts/m2. Extensive experiments show that the UPKD Network achieved state-of-the-art performance on our dataset, with repeatability achieving 90.39%, effectiveness (the ratio of effective keypoints to annotation points) achieving 69.95%, and completeness (the ratio of detected annotation points to keypoints) achieving 79.55%.

Learning continuous multi-UAV controls with directed explorations for flood area coverage

Real time on-ground information is of critical value during any natural disaster such as floods. The disaster response teams require the latest ground information of the flooded areas to effectively plan and execute rescue operations. Unmanned Aerial Vehicles (UAVs) are increasingly becoming a tool to perform quick surveys of larger areas such as flood disasters. In this paper, we propose a method to perform critical area coverage of flood-struck regions using multiple autonomous UAVs. A Deep Reinforcement Learning algorithm is proposed to learn continuous multi-UAV controls, incorporating a directed exploration strategy for the DDPG’s target actor, which relies on the D-infinity (DINF) algorithm. The DINF water flow estimation technique utilizes surface elevation data to understand and predict the directed discharge of floodwater. Further, we introduce a Path scatter strategy for the multi-UAV system that inhibits the clustered formation of the UAVs over low-elevated regions. The performance of the proposed D3S (DDPG+DINF+Path Scatter) algorithm is evaluated using various performance metrics, such as average cumulative rewards, number of collisions, and UAVs’ spread observed over the environment. In comparison to the baseline algorithms and other prevalent approaches in the literature, the proposed method is found to be better placed as the results highlight a significantly improved performance by D3S across different metrics.

Calculation of the main operational characteristics of a tethered high-altitude ship-based system

Objectives. Currently, UAVs are actively used in many military and civilian fields such as object surveillance, telecommunications, radar, photography, video recording, and mapping, etc. The main disadvantage of autonomous UAVs is their limited operating time. The long-term operation of UAVs on ships can be ensured by tethered high-altitude systems in which the power supply of engines and equipment is provided from the onboard energy source through a thin cable tether. This paper aims to select and justify the appearance of such system, as well as to calculate the required performance characteristics.Methods. The study used methods of systemic and functional analysis of tethered system parameters, as well as methods and models of the theory of relations and measurement.Results. The issues of design and implementation of new generation tethered high-altitude ship-based systems were considered. A rational type of aerodynamic design for unmanned aerial vehicles was determined based on existing tethered platforms. The optimal architecture of the tethered system was defined and justified. The paper presents the appearance and solution for placement onboard the ship, and describes its operation. The main initial parameters for designing high-altitude systems such as take-off weight, optimal lift altitude, maximum power required for operation, structure of the energy transfer system, as well as deployment and lift time to the design altitude were selected and calculated.Conclusions. The methodology for calculating the necessary characteristics described in the paper can be used for developing and evaluating tethered high-altitude systems. These systems are capable of performing a wide range of tasks, without requiring a separate storage and launch location, which is especially important in the ship environment. The system presented herein possesses significant advantages over well-known analogues.

Verification and Validation for a Digital Twin for Augmenting Current SORA Practices with Air-to-Air Collision Hazards Prediction from Small Uncooperative Flying Objects

Future autonomous Unmanned Aerial Vehicles (UAV) missions will take place in highly cluttered urban environments. As a result, the UAV must be able to autonomously evaluate risks and react to unforeseen hazards. The current regulatory framework for missions implements SORA guidelines for hazard detection, but its application to air-to-air collision is limited. This research defined a rigorous verification and validation framework (V&V) for digital twins for use in future autonomous UAV missions. The researchers designed a sentry mission for a UAV to evaluate its capacity to detect small uncooperative flying objects. A digital twin of the DJI M300 vision system was built using a game engine and a V&V framework was developed to assure the quality of results in both virtual and real-world scenarios. The results showed the capability of the digital twin to identify vulnerabilities and worst-case scenarios in UAV mission operations, and how it can assist remote pilots in identifying air-to-air collision hazards. Furthermore, the probability of air-to-air collision was calculated for three sentry patterns, and the results were validated in the field. This research demonstrated the capability to identify vulnerabilities and worst-case scenarios in UAV mission operations. We present how the digital twin of an operational theatre can be exploited to assist remote pilots with the identification of air-to-air collision hazards of small uncooperative objects. Furthermore, we discuss how these results can be used to enhance current SORA-based risk assessment practices.

Mission-critical UAV swarm coordination and cooperative positioning using an integrated ROS-LoRa-based communications architecture

Over the recent years unmanned aerial vehicles (UAVs) have been utilized extensively in mission-critical operations, especially as it relates to disaster management scenarios. Clearly, during these missions, the UAVs should be able to communicate effectively (amongst themselves and with the ground control station (GCS)) in order to transmit and receive commands and other related information. Moreover, accurate positioning is paramount during this type of operations. This has motivated the exploration of a number of alternative navigation methods to address robustness issues that arise when the global positioning system (GPS) becomes unavailable, due to GNSS disruption or sensor malfunction. This work addresses these issues by initially developing and implementing an integrated LoRa-ROS-based (long range communication - robot operating system) system for UAV-to-X communications. Subsequently, it presents a novel cooperative positioning approach, where a group of autonomous UAVs employ various algorithms (detection, tracking, communication, and localization) for cooperative positioning in order to counter any GPS/sensor malfunction. For evaluation purposes, a prototype multi-agent system is designed and implemented, utilizing the proposed integrated ROS-LoRa-based communication architecture, as well as sensor (inertial measurement unit - IMU) fusion. Specifically, LoRA mesh networking (using a custom printed circuit board - BALORA), is incorporated to maintain communication and distribute the sensor information between the UAVs. The prototype of the proposed communications architecture and cooperative relative positioning system (CRPS) is subsequently tested in a real-world environment, demonstrating the feasibility and effectiveness of the proposed communications solution, as well as the robust and accurate localization that is analogous to the ground truth (GPS+IMU).

Enhancing vehicle detection in intelligent transportation systems via autonomous UAV platform and YOLOv8 integration

This study highlights the evolving landscape of object detection methodologies, emphasizing the superiority of deep learning-based approaches over traditional methods. Particularly in intelligent transportation systems-related applications requiring robust image processing techniques, such as vehicle identification, localization, tracking, and counting within traffic scenarios, deep learning has gained substantial traction. The YOLO algorithm, in its various iterations, has emerged as a popular choice for such tasks, with YOLOv5 garnering significant attention. However, a more recent iteration, YOLOv8, was introduced in early 2023, ushering in a new phase of exploration and potential innovation in the field of object detection. Consequently, due to its recent emergence, the number of studies on YOLOv8 is extremely limited, and an application in the field of Intelligent Transportation Systems (ITS) has not yet found its place in the existing literature. In light of this gap, this study makes a noteworthy contribution by delving into vehicle detection using the YOLOv8 algorithm. Specifically, the focus is on targeting aerial images acquired through a modified autonomous UAV, representing a unique avenue for the application of this cutting-edge algorithm in a practical context. The dataset employed for training and testing the algorithm was curated from a diverse collection of traffic images captured during UAV missions. In a strategic effort to enhance the variability of vehicle images, the study systematically manipulated flight patterns, altitudes, orientations, and camera angles through a custom-designed and programmed drone. This deliberate approach aimed to bolster the algorithm's adaptability across a wide spectrum of scenarios, ultimately enhancing its generalization capabilities. To evaluate the performance of the algorithm, a comprehensive comparative analysis was conducted, focusing on the YOLOv8n and YOLOv8x submodels within the YOLOv8 series. These submodels were subjected to rigorous testing across diverse lighting and environmental conditions using the dataset. Through tests, it was observed that YOLOv8n achieved an average precision of 0.83 and a recall of 0.79, whereas YOLOv8x attained an average precision of 0.96 and a recall of 0.89. Furthermore, YOLOv8x also outperformed YOLOv8n in terms of F1 score and mAP, achieving values of 0.87 and 0.83 respectively, compared to YOLOv8n's 0.81 and 0.79. These outcomes of the evaluation illuminated the relative strengths and weaknesses of YOLOv8n and YOLOv8x, leading to the conclusion that YOLOv8n is well-suited for real-time ITS applications, while YOLOv8x exhibits superior detection capabilities.

Autonomous UAV Safety Oriented Situation Monitoring and Evaluation System

In this paper, a LabVIEW-based online monitoring and safety evaluation system for UAVs is designed to address the deficiencies in UAV flight state parameter monitoring and safety evaluation. The system consists of a lower unit for UAV recording and an upper unit on the ground. The lower unit collects and detects flight data and connects to the upper unit through a wireless digital transmission module via a serial port. The upper unit receives the data and carries out the monitoring and safety situation evaluation of the UAV. The lower unit of the system adopts multi-sensors to collect UAV navigation information in real time to achieve flight detection, while the upper unit adopts LabVIEW to design the UAV online monitoring and safety situation prediction system, enabling monitoring and safety situation prediction during UAV navigation. The test results show that the system can detect and comprehensively display the navigation information of the UAV in real time, and realize the safety evaluation and warning function of the UAV.

Research on experimental evaluation model of unmanned aerial vehicle autonomy based on digital twin

Abstract The current focus of unmanned aerial vehicle (UAV) research lies in the scientific evaluation of UAV autonomy and the enhancement of belief in UAVs. Researchers are required to conduct numerous tests to assess the ability of UAVs to perform missions in complex environments. However, conducting real-world tests for UAVs costs a lot. Therefore, digital twin virtual simulation serves as a viable alternative to actual flight tests. Firstly, this paper establishes a digital twin framework that enables comprehensive perception and integration within a virtual environment, proposing a UAV autonomy experimental evaluation model. Additionally, an effective method for evaluating UAV autonomy is constructed, and test tasks targeting autonomous capability assessment are designed by using simulation software and verified through system examples. Finally, based on the simulation results, the autonomous capability of the UAV twin is evaluated and utilized as a reference for assessing the autonomy of physical UAVs, thereby providing support for scientific evaluation.

Flow Field Analysis on Critical Feedback Information for UAV Autonomous High Speed Descent in Power Line Scenario

Abstract To improve the UAV efficiency in power line inspection, it must autonomously and rapidly descend to a specific height to complete the charging or landing task. This task requires the UAV to contain a stable control loop to face the specific airflow during autonomous and rapid descent: the vortex ring problem, and this specific airflow will reduce the UAV controllability. To implement the UAV autonomous high-speed descent, a control method must provide feedback information on the affected airflow and output a stable rotational speed. Hence, this paper designs a UAV autonomous high-speed descent control loop and proposes a variable rotor speed method to solve the feedback information in the control loop. This method reveals the relationship between the air movement caused by the rotor rotation and the controllability of the UAV high-speed descent. Therefore, with this method, the UAV control loop can obtain stable feedback on the air movement and achieve autonomous high-speed descent. To solve the UAV feedback information, we built the model to calculate the rotor speed in the autonomous high-speed descent UAV control loop and verified it by the CFD. The verification results showed that the model could reflect the actual fly condition. The proposed method solves the problem of UAV autonomous high-speed descent feedback information, improving the work efficiency of UAVs in power line inspection operations.

INCEPTION SH: A NEW CNN MODEL BASED ON INCEPTION MODULE FOR CLASSIFYING SCENE IMAGES

In this study, a light-weight model with an optimum block structure that can be used in autonomous unmanned aerial vehicles (UAVs) was designed. The Inception SH model, which was developed based on the Inception V3 model, was compared on "Intel Image Dataset", a publicly available dataset in the literature. As a result of the comparison, values of 0.882, 0.883, 0.882 and 0.882 were obtained for the accuracy, precision, recall, and F1 score metrics for the Inception V3 model, respectively. In the Inception SH model, values of 0.958, 0.957, 0.974 and 0.967 were obtained for accuracy, precision, recall and F1 score metrics, respectively. As can be seen from these values, the proposed Inception SH model offers higher performance values than the underlying Inception V3 model. The Inception SH model was compared with different models in the literature using the same data set and was superior in accuracy, precision, recall and F1 score metrics compared to the compared models. According to the results obtained, it is predicted that the Inception SH model can be used as a lightweight model in various IoT devices, considering the popularity of autonomous UAVs.

Israeli Military Artificial Intelligence, Its Possible Use in the War in Gaza.

This paper is focused on examining selected Israeli technologies operating based on artificial intelligence and the possible use of these technologies by the Israeli Defense Forces in the war in Gaza. These technologies include the Besorah system, an AI technology for identifying and recommending targets suitable for aerial bombardment, as well as other forms of attacks carried out from a distance. The SMASH system is an automatic targeting and firing system for small arms. The Goshawk system is a fully autonomous UAV used for aerial protection. The IRIS robot can be used for the initial investigation of tunnels ahead of assaults. The alleged use of these technologies in the war in Gaza cannot be verified by independent sources, mainly due to the ongoing conflict. The paper strives to present a general theoretical assessment of these technologies and weapons from the perspective of international law and armed conflicts.

Path Planning for Unmanned Aerial Vehicles in Complex Environments

This paper introduces a comprehensive framework for generating obstacle-free flight paths for unmanned aerial vehicles (UAVs) in intricate 3D environments. The system leverages the Rapidly Exploring Random Tree (RRT) algorithm to design trajectories that effectively avoid collisions with structures of diverse shapes and sizes. Discussion revolves around the challenges encountered during development and the successful achievement of generating collision-free routes. While the system represents an initial iteration, it serves as a foundation for future projects aiming to refine and expand upon its capabilities. Future work includes simulation testing and integration into UAV missions for image acquisition and structure scanning. Additionally, considerations for swarm deployment and 3D reconstruction using various sensor combinations are outlined. This research contributes to the advancement of autonomous UAV navigation in real-world scenarios.

Autonomous UAV Navigation with Adaptive Control Based on Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) navigation plays a crucial role in its ability to perform autonomous missions in complex environments. Most of the existing reinforcement learning methods to solve the UAV navigation problem fix the flight altitude and velocity, which largely reduces the difficulty of the algorithm. But the methods without adaptive control are not suitable in low-altitude environments with complex situations, generally suffering from weak obstacle avoidance. Some UAV navigation studies with adaptive flight only have weak obstacle avoidance capabilities. To address the problem of UAV navigation in low-altitude environments, we construct autonomous UAV navigation in 3D environments with adaptive control as a Markov decision process and propose a deep reinforcement learning algorithm. To solve the problem of weak obstacle avoidance, we creatively propose the guide attention method to make a UAV’s decision focus shift between the navigation task and obstacle avoidance task according to changes in the obstacle. We raise a novel velocity-constrained loss function and add it to the original actor loss to improve the UAV’s velocity control capability. Simulation experiment results demonstrate that our algorithm outperforms some of the state-of-the-art deep reinforcement learning algorithms performing UAV navigation tasks in a 3D environment and has outstanding performance in algorithm effectiveness, with the average reward increasing by 9.35, the success rate of navigation tasks increasing by 14%, and the collision rate decreasing by 14%.