Image sequence processing applied to autonomous aerial navigation

Unmanned Aerial Vehicles (UAVs) have a major issue in autonomous navigation, particularly in dynamic or GPS-denied environments. Conventional rule-based or GPS-dependent strategies lack the flexibility and resilience to support real-time decision-making, especially in difficult terrain. We propose a novel deep learning-based framework, DeepFusion-NavNet, that couples reinforcement learning with semantic segmentation to perform end-to-end autonomous UAV navigation in an attempt to overcome these limitations. It employs a Deep Q-Network (DQN) to learn the optimal trajectory of the UAV based on policies learned once spatial features are extracted from aerial images by a U-Net-based semantic segmentation model. The proposed method was experimented with in both urban and forest environments utilizing the AirSim simulation tool. Relative to existing deep reinforcement learning baselines, experimental validation proves that DeepFusion-NavNet results in a navigation success rate of 97.5%, reduces collision incidents by 91.2%, and improves route efficiency by 22.8%. These results indicate that DeepFusion-NavNet significantly enhances UAV safety and autonomy under challenging operating conditions.

Visual odometry (VO) has become a popular technique for autonomous naviga-tion of unmanned aerial vehicles (UAVs) in GPS-denied environments. Although, the concept of VO is not new, it has been successfully implemented for on-ground vehi-cles only. There are several product level solutions of autonomous navigation, which rely on VO or contain VO as an important subpart. Meanwhile, a proper system of VO for UAVs is not yet achieved and continues to be a subject of an active research. In this paper, we show the comparison of the latest VO techniques for UAV nav-igation. We begin by describing the basic principles of VO, then review the different types of VO algorithms commonly used in UAV navigation, such as monocular, stereo, and RGB-D approaches. For each type of algorithm, we discuss the advantages and limitations, and provide examples of recent research and applications. Finally, we dis-cuss some of the emerging trends and future directions in visual odometry for UAV navigation, such as Visual inertial odometry approaches and real-time implementa-tions. This review paper provides a comprehensive overview of the current state-of-the-art in visual odometry for UAV autonomous navigation, highlighting the ad-vantages and limitations of different types of algorithms and identifying key challeng-es and future research directions.

Autonomous navigation of unmanned aerial vehicle (UAV) is one of the fundamental yet completely solved problems in automatic control. In this paper, an option-based hierarchical reinforcement learning approach is proposed for UAV autonomous navigation. Specifically, the proposed method consists of a high-level and two low-level model, where the high level behavior selection model learns a stable and reliable behavior selection strategy automatically, while the low-level obstacle avoidance model and target-driven control model implement two behavior strategies, obstacle avoidance and target approach, respectively, thus avoiding the dependence on manually designed control rules. Furthermore, the proposed model is pre-trained on large public dataset, allowing the model to converge quickly in various complex unstructured flight environments. Extensive experiments show that the proposed method indicates an overall advantage in various evaluation metrics, which indicating that the proposed method has a strong generalization capability in autonomous navigation task of UAV.

This paper discusses the algorithms of vision systems for automatic UAV (Unmanned Aerial Vehicle) landing. The basic algorithms for finding helipad and adapting the UAV control system to an autonomous UAV landing are presented. The authors consider algorithms to solve navigation problems, building block diagrams of automatic UAV control. Also, the developed algorithms make it possible to implement an automatic landing system for UAVs using technical vision systems, using various camera parameters, algorithms allowing research, as part of the autonomous navigation of unmanned aerial vehicles. The article discusses the developed algorithm that allows you to highlight certain characteristic points for visual navigation. Also, a search system for characteristic points has been developed that allows automatic UAV landing. These algorithms may be useful for the autonomous landing UAV systems. Algorithms can also be used to track a trajectory of the UAV system.

This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges.

Chapter 21 - Event-driven programming-based path planning and navigation of UAVs around a complex urban environment

The aim of this paper is to explore the possibility of using geo-referenced aerial images to augment an Unmanned Aerial Vehicle (UAV) navigation system in case of GPS failure. The proposed method is based on image matching between the current view from a monocular video camera and a previously known database of geo-referenced images. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or artificial landmarks. The novel idea is to perform global localization, position tracking, localization failure recovery and safe landing. Key points of current video frame and reference database are extracted by implementing Speeded-Up Robust Features (SURF) algorithm on a NVIDIA Geforce 240M graphics processing unit (GPU). The proposed system has been tested on real flight data and testing results confirmed that the proposed system is real-time and sufficiently robust to scale, rotation, illumination, noise and affine projection, thus it can be an ideal scheme for autonomous navigation system for UAVs under GPS blockage conditions.

Assisting elderly individuals in indoor environments is crucial, as they often encounter mobility and safety challenges. Autonomous Unmanned Aerial Vehicles (UAVs) navigation offers a transformative solution, significantly enhancing safety, efficiency, and quality of life. These UAVs can perform tasks such as monitoring, delivering essential items, and responding to emergencies, providing invaluable support and promoting greater independence for elderly individuals. To achieve effective assistance, autonomous UAV navigation relies on robust path planning with obstacle avoidance to ensure optimal performance. A newly developed path planning, based on a new hybrid Water Cycle Algorithm (WCA), stands out by addressing the challenge of avoiding obstacles while adhering to non-holonomic constraints and conserving energy in complex, cluttered indoor environments. In Population-Based Algorithms (PBAs), the initial population plays a crucial role, as it greatly impacts the algorithm’s efficiency in exploring the search space and its rate of convergence. The new developed hybrid algorithm, called WCA-HS, is based on using the Harmony Search (HS) algorithm to identify the optimal initial population for the WCA. The results indicate that the hybrid WCA-HS outperforms classical WCA and other metaheuristic algorithms, such as HS, Firefly Algorithm (FA), Cuckoo Search (CS), Genetic Algorithm (GA), Differential Evolution (DE), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), over 20 independent runs, highlighting the effectiveness of the population initialization technique. Additionally, the developed indoor path planning system was evaluated in highly cluttered and low-light environments, showcasing its robustness and real-world applicability, making it highly effective for assisting elderly individuals in complex indoor environments.

Accurate trajectory prediction is essential for ensuring flight safety, autonomous navigation, and efficient control of unmanned aerial vehicles (UAVs). However, existing neural network approaches often focus on a single platform, limiting their generalizability across UAVs with different dynamics. This study evaluates three neural architectures for spatiotemporal trajectory prediction using high-frequency flight data. Two UAV configurations, such as a fixed-wing (Cessna) and a multirotor (Phantom), were simulated in XPlane 11 under identical environmental conditions. Flight data of latitude, longitude, and altitude sampled at 80 Hz were preprocessed using a sliding-window segmentation method to preserve temporal dependencies. Backpropagation Neural Network (BPNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) models were trained using standardised preprocessing and tuned hyperparameters. Results show that recurrent models outperform the feedforward BPNN in capturing temporal patterns. For the Cessna dataset, the RNN achieved balanced accuracy with a mean-squared error (MSE) of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5.5 \times 10^{-8}$</tex>, while the LSTM obtained the lowest MSE (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.7 \times 10^{-8}$</tex>). In the more dynamic Phantom dataset, the BPNN remained competitive (MSE <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\approx 1.8 \times 10^{-7}$</tex>), whereas RNN and LSTM maintained superior altitude prediction. These results highlight the suitability of lightweight recurrent architectures for real-time UAV trajectory prediction.

This paper proposes a framework for unmanned aerial vehicle (UAV) navigation using carrier phase differential cellular (CD-cellular) measurements. The framework requires no prior knowledge of the UAV’s position and achieves centimeter-level position accuracy. A batch weighted nonlinear least-squares estimator is developed to solve for the integer ambiguities and an extended Kalman filter is formulated to initialize the batch estimator. Monte Carlo simulations are presented to characterize the performance of the proposed framework as a function of the total number of hearable cellular base transceiver stations (BTSs) and the size of the batch estimator. Experimental results are presented demonstrating a UAV navigating exclusively using the proposed framework over a 1.72 km trajectory completed in 3 minutes with 62.11 cm root mean-square error (RMSE).

Autonomous navigation is a highly desirable capability for Unmanned Aerial Vehicle (UAV). In this paper, the problem of autonomous navigation of UAV in unknown dynamic environments is addressed. Specifically, we propose a visual-inertial multi-modal sensor data perception framework based on a hierarchical reinforcement learning paradigm. This model consists of a high-level behavior selection model and a low-level policy execution model. The high-level model learns a stable and reliable behavior selection strategy. The low-level model decomposes the UAV navigation task into two simpler subtasks, which respectively achieve obstacle avoidance and goal approximation, which effectively learns high-level semantic information about the scene and narrows the strategy space. Furthermore, extensive simulation results are provided to confirm the superiority of the proposed method in terms of convergence and effectiveness compared to state-of-the-art methods.

The autonomous UAV (unmanned aerial vehicle) navigation has recently gained an increasing interest from both academic and industrial sectors due to its potential uses in various fields and especially, the need for social distancing during the pandemic. Many works have adopted a deep reinforcement learning (RL) method with experience replay called deep deterministic policy gradient (DDPG) to control the motion of UAV, and gain high accuracy results in static and simplified environments. However, they are still far from being ready for real world adoption in that the UAVs have to operate under complex and dynamic conditions. We also found that using only DDPG makes the learning process prone to oscillation and is inefficient for tasks having high dimensional action-state spaces. Furthermore, the goal reward mechanism in traditional reward functions brings a bias to the state, which resembles the one at the goal area and leads to erroneous action selection. To get closer to being ready for real world adoption, we proposed a novel method that enables UAVs to be capable of handling motion control in realistic environments. The first component of our proposed method is point cloud data (PCD) simplification with truncated icosahedron structure which converts enormous PCD into a few essential data points. In the second component of our method, we replace the traditional goal reward mechanism with a new mechanism called Augmentative Backward Reward (ABR) function to dispense the goal reward to transitions proportionately to its participation. By integrating simplified PCD and ABR, we achieved significantly better results when compared with using only the-state-of-the-art, TD3. In addition, we tested the proposed method with another navigation task, BipedalWalkerHardcore, a testbed for RL, and the result is still better and steadier than of TD3. These results indicate that the proposed method is robust.

This paper considers the navigation of a solar-powered unmanned aerial vehicle (UAV) for securing the communication with an intended ground node in the presence of eavesdroppers in urban environments. To complete this task, the UAV needs to not only fly safely in the complex urban environment, but also take into account the communication performance with the intended node and eavesdroppers. To this end, we formulate a multi-objective optimization problem to plan the UAV path. This problem jointly considers the maximization of the residual energy of the solar-powered UAV at the end of the mission, the maximization of the time period in which the UAV can securely communicate with the intended node and the minimization of the time to reach the destination. We pay attention to the impact of the buildings in the urban environments, which may block the transmitted signals and also create some shadow region where the UAV cannot harvest energy. A Rapidly-exploring Random Tree (RRT) based path planning scheme is presented. This scheme captures the nonlinear UAV motion model, and is computationally efficient considering the randomness nature. From the generated tree, a set of possible paths can be found. We evaluate the security of the wireless communication, compute the overall energy consumption as well as the harvested amount for each path and calculate the time to complete the flight. Compared to a general RRT scheme, the proposed method enables a large time window for the UAV to securely transmit data.

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and positioning. In order to reduce the computational complexity and exclude the risk caused by Global Positioning System (GPS) outage, an autonomous navigation system based on distributed filtering is designed and realized. When GPS is available by monitoring the GPS integrity, sensor information from Strapdown Inertial Navigation System (SINS) and GPS is fused using a 7-state Conventional Kalman Filter (CKF) to estimate the full UAV state (position, velocity and attitude); when GPS is unavailable, sensor information from gyroscopes, accelerometers and magnetometer is fused using a 4-state Extended Kalman Filter (EKF) to estimate the attitude and heading of the UAV, and sensor information from SINS and vision positioning system is fused using a 7-state Incoordinate Interval Kalman Filter (IIKF) to estimate the position and velocity of the UAV. In addition, the second-order vertical channel damping loop is adopted to fuse measurements from a barometer with those of SINS, which suppresses the divergence of the vertical channel error and makes the altitude information calculated by SINS trustable. Both ground and flight experiments of the autonomous navigation system have been carried out. The test results show that the system can provide stabilized attitude information in long durations, and can realize the automatic flight control of UAV.

Unmanned aerial vehicles (UAVs), also known as drones, have gained considerable interest among academics in recent years, which significantly increases the demand for autonomous navigation systems for UAVs. In this study, autonomous navigation is addressed as a two-stage problem. The first stage is the obstacle detection phase, during which the UAVs leverage their onboard sensors to perceive the surroundings and detect obstacles. The second stage is the obstacle avoidance phase, where the UAVs make decisions to continue operations without colliding with anything while approaching the target. This study proposes a self-supervised learning-based depth prediction framework as the criterion for obstacle proximity detection. The framework consists of two subnetworks: the depth and pose estimation modules. The former predicts depth values, while the latter evaluates the object’s translation and rotation matrix. The framework leverages the subnetworks’ outputs to reconstruct the projection relationships of pixels between adjacent frames to optimize the training process. To address the challenge of UAVs operating in large-scale environments, a modified loss function that combines photometric loss and edge-aware smoothness is presented to increase the depth prediction module’s accuracy. This study also presents a deep reinforcement learning-based obstacle avoidance system that takes depth maps as inputs to generate evasive maneuvers. The system works independently from priori maps and models while it learns the collision-free trajectory from interacting with the environment. This study deploys value-based algorithms to learn the nonlinear mapping between the continuous inputs and discrete output motion commands. Furthermore, the proposed system is validated on different simulation scenarios. It is capable of completing the obstacle avoidance task with relatively optimal trajectories in varied environments, demonstrating its superior effectiveness and transferability. Keywords: unmanned aerial vehicles, urban airspace, obstacle detection and avoidance, self-supervised learning, deep reinforcement learning.