Autonomous Unmanned Aerial Vehicles Research Articles

Joint Design of Transmit Waveform and Altitude for Unmanned Aerial Vehicle-Enabled Integrated Sensing and Wireless Power Transfer Systems

Recently, unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) has received great attention as a promising technology for providing stable power to energy-constrained devices by navigating three-dimensional (3D) space, particularly in challenging environments such as maritime networks and smart cities. Additionally, UAV-enabled radar sensing has gained significant attention as a key technology for future 6G networks, as it enables high-accuracy sensing for various applications, such as target detection and tracking, surveillance, and environmental monitoring, as well as autonomous UAV operation. In this regard, we investigated UAV-enabled integrated sensing and wireless power transfer (ISWPT) systems that combine radar sensing and WPT operations on a unified hardware platform, sharing the same spectrum of resources. In order to accurately sense multiple targets and efficiently transfer power to multiple devices at the same time, we propose a method for jointly designing the transmit waveform and UAV altitude, taking into account the fundamental trade-off between radar sensing performance with the desired beam pattern and WPT performance with the desired harvested power of the devices. We first developed an effective method to obtain the optimal waveform and altitude by solving a challenging non-convex optimization problem. Based on this, we developed another efficient, low-complexity method by exploring a novel transmit waveform and optimizing its parameters to reduce computational complexity and thereby lower power consumption in UAVs. The numerical results verify that the proposed method significantly improves both radar sensing and WPT performance, as well as substantially reduces computational complexity.

Retraction Note: An agent architecture for autonomous UAV flight control in object classification and recognition missions

Retraction Note: An agent architecture for autonomous UAV flight control in object classification and recognition missions

Multi-UAV Escape Target Search: A Multi-Agent Reinforcement Learning Method

The multi-UAV target search problem is crucial in the field of autonomous Unmanned Aerial Vehicle (UAV) decision-making. The algorithm design of Multi-Agent Reinforcement Learning (MARL) methods has become integral to research on multi-UAV target search owing to its adaptability to the rapid online decision-making required by UAVs in complex, uncertain environments. In non-cooperative target search scenarios, targets may have the ability to escape. Target probability maps are used in many studies to characterize the likelihood of a target’s existence, guiding the UAV to efficiently explore the task area and locate the target more quickly. However, the escape behavior of the target causes the target probability map to deviate from the actual target’s position, thereby reducing its effectiveness in measuring the target’s probability of existence and diminishing the efficiency of the UAV search. This paper investigates the multi-UAV target search problem in scenarios involving static obstacles and dynamic escape targets, modeling the problem within the framework of decentralized partially observable Markov decision process. Based on this model, a spatio-temporal efficient exploration network and a global convolutional local ascent mechanism are proposed. Subsequently, we introduce a multi-UAV Escape Target Search algorithm based on MAPPO (ETS–MAPPO) for addressing the escape target search difficulty problem. Simulation results demonstrate that the ETS–MAPPO algorithm outperforms five classic MARL algorithms in terms of the number of target searches, area coverage rate, and other metrics.

Optimal path planning for unmanned aerial vehicles in power line inspection in rechargeable scenario

To address the issue that a single charge of UAVs (Unmanned Aerial Vehicles) do not satisfy task requirements, this paper proposes a solution involving the deployment of charging stations in the mission area. An energy-efficient path planning method is designed for UAVs with charging stations, which simultaneously determines the deployment locations and quantities of charging stations. The method formulates the energy optimization path planning problem of UAVs as an integer linear programming and an algorithm based on greedy strategy is designed for solving the model. A numerical example including simulations across various scenarios demonstrate that the proposed method can search a more energy-efficient paths compared to single-agent multi-sortie schemes with a greedy strategy. This research contributes to the development of intelligent and autonomous UAV systems for power line inspection, enhancing grid reliability and safety.

LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

Abstract. With the advancement of technology, unexposed spaces have emerged as a new type of strategic area, attracting significant attention from researchers. These spaces often present complex environments, such as extreme lighting variations, irregular geometries, and the lack of external positioning signals, making human entry hazardous. Fortunately, advancements in robotics and artificial intelligence have enabled unmanned systems to mitigate these challenges, allowing for safer exploration of unexposed spaces. UAVs, as vital unmanned system platforms, are extensively used across various industrial scenarios and are particularly effective for detecting unexposed spaces. To facilitate automated exploration of these areas, we propose the autonomous UAV system, LUOJIA Explorer. Equipped with a LiDAR sensor, data transmission module, and external anti-collision devices to protect the aircraft, LUOJIA Explorer is designed for effective unexposed space detection. We have integrated a laser point cloud positioning and mapping subsystem, along with a planning and control subsystem, into the LUOJIA Explorer. Through both simulation and actual experiments, the Luojia Explorer has demonstrated the capability to achieve stable flight based on self-positioning in unexposed spaces, with an exploration efficiency of 88.83 m3/s.

Simulation and real-life implementation of UAV autonomous landing system based on object recognition and tracking for safe landing in uncertain environments

The use of autonomous Unmanned Aerial Vehicles (UAVs) has been increasing, and the autonomy of these systems and their capabilities in dealing with uncertainties is crucial. Autonomous landing is pivotal for the success of an autonomous mission of UAVs. This paper presents an autonomous landing system for quadrotor UAVs with the ability to perform smooth landing even in undesirable conditions like obstruction by obstacles in and around the designated landing area and inability to identify or the absence of a visual marker establishing the designated landing area. We have integrated algorithms like version 5 of You Only Look Once (YOLOv5), DeepSORT, Euclidean distance transform, and Proportional-Integral-Derivative (PID) controller to strengthen the robustness of the overall system. While the YOLOv5 model is trained to identify the visual marker of the landing area and some common obstacles like people, cars, and trees, the DeepSORT algorithm keeps track of the identified objects. Similarly, using the detection of the identified objects and Euclidean distance transform, an open space without any obstacles to land could be identified if necessary. Finally, the PID controller generates appropriate movement values for the UAV using the visual cues of the target landing area and the obstacles. To warrant the validity of the overall system without risking the safety of the involved people, initial tests are performed, and a software-based simulation is performed before executing the tests in real life. A full-blown hardware system with an autonomous landing system is then built and tested in real life. The designed system is tested in various scenarios to verify the effectiveness of the system. The code is available at this repository: https://github.com/rnjbdya/Vision-based-UAV-autonomous-landing.

Технологии и методы планирования перемещения БПЛА по маршрутным точкам

Unmanned aerial vehicles (UAVs) occupy a special place in our world. Their ability to navigate along set routes opens up prospects in various fields. The purpose of the study is to review and analyze navigation systems and UAV routing algorithms, methods that allow UAVs to follow the route with high accuracy. GPS and inertial navigation (INS) systems that provide accurate location determination are being investigated. The capabilities of sensor systems — cameras, lidars and ultrasonic sensors — are analyzed to detect obstacles and adjust the trajectory; voxel maps for three-dimensional modeling of the environment and methods of simultaneous localization and mapping (SLAM); algorithm A* (A-star); genetic routing algorithm, potential-based obstacle avoidance algorithms and RRT. Practical significance: the use of these methods and technologies can significantly improve the safety and accuracy of UAV routing, the ability to navigate autonomously in complex and dynamically changing landscapes. In conclusion, the advantages and limitations of navigation approaches and technologies are discussed; the importance of integrating sensor systems and SLAM methods to increase the autonomy and efficiency of UAVs; directions for further research.

Autonomous Multirotor UAV Search and Landing on Safe Spots Based on Combined Semantic and Depth Information From an Onboard Camera and LiDAR

Autonomous Multirotor UAV Search and Landing on Safe Spots Based on Combined Semantic and Depth Information From an Onboard Camera and LiDAR

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).