Search Results (141)

Underground mining environments are highly hazardous, often prone to gas explosions, cave-ins, and fires that may trap miners during emergencies. The accurate, real-time localization of miners is vital for effective self-escape and rescue operations. Although the Mine Improvement and New Emergency Response (MINER) Act of 2006 mandates communication and tracking systems, most current solutions rely on low-power devices and line-of-sight methods that are ineffective in GPS-denied, dynamic subsurface conditions. Delay-Tolerant Networking (DTN) has emerged as a promising alternative by supporting message relay through intermittent links. In this work, we propose a deep learning framework that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to predict miner locations using simulated DTN-based movement data. The model was trained on a simulated dataset of 1,048,575 miner movement entries, predicting miner locations across 26 pillar classes. It achieved an 89% accuracy, an 89% recall, and an 83% F1-score, demonstrating strong performance for real-time underground miner localization. These results demonstrate the model’s potential for the real-time localization of trapped miners in GPS-denied environments, supporting enhanced self-escape and rescue operations. Future work will focus on validating the model with real-world data and deploying it for operational use in mines. Full article

Vision-based navigation is a common solution for the critical challenge of GPS-denied Unmanned Aerial Vehicle (UAV) operation, but a research gap remains in the autonomous discovery of robust landmarks from aerial survey imagery needed for such systems. In this work, we propose a framework to fill this gap by identifying visually distinctive urban buildings from aerial survey imagery and curating them into a landmark database for GPS-free UAV localization. The proposed framework constructs semantically rich embeddings using intermediate layers from a pre-trained YOLOv11n-seg segmentation network. This novel technique requires no additional training. An unsupervised landmark selection strategy, based on the Isolation Forest algorithm, then identifies objects with statistically unique embeddings. Experimental validation on the VPAIR aerial-to-aerial benchmark shows that the proposed max-pooled embeddings, assembled from selected layers, significantly improve retrieval performance. The top-1 retrieval accuracy for landmarks more than doubled compared to typical buildings (0.53 vs. 0.31), and a Recall@5 of 0.70 is achieved for landmarks. Overall, this study demonstrates that unsupervised outlier selection in a carefully constructed embedding space yields a highly discriminative, computation-friendly set of landmarks suitable for real-time, robust UAV navigation. Full article

This study introduces an innovative refinement to EKF-based monocular SLAM by incorporating attitude, altitude, and range-to-base data to enhance system observability and minimize drift. In particular, by utilizing a single range measurement relative to a fixed reference point, the method enables unmanned aerial vehicles (UAVs) to mitigate error accumulation, preserve map consistency, and operate reliably in environments without GPS. This integration facilitates sustained autonomous navigation with estimation error remaining bounded over extended trajectories. Theoretical validation is provided through a nonlinear observability analysis, highlighting the general benefits of integrating range data into the SLAM framework. The system’s performance is evaluated through both virtual experiments and real-world flight data. The real-data experiments confirm the practical relevance of the approach and its ability to improve estimation accuracy in realistic scenarios. Full article

The gradual integration of Manned–Unmanned Teaming (MUM-T) is gaining increasing significance. An intriguing feature is the ability to do relative estimation solely through the use of the INS/GPS system. However, in certain environments, such as GNSS-denied areas, this method may lack the necessary accuracy and reliability to successfully execute autonomous formation flight. In order to achieve autonomous formation flight, we are conducting an initial investigation into the development of a relative estimator and control laws for MUM-T. Our proposal involves the use of a quaternion-based relative state estimator to combine GPS and INS sensor data from each UAV with vision pose estimation of the remote carrier obtained from the fighter. The technique has been validated through simulated findings, which paved the way for the experiments explained in the paper. Full article

Future planetary exploration missions will rely heavily on efficient human–robot interaction to ensure astronaut safety and maximize scientific return. In this context, digital twins offer a promising tool for planning, simulating, and optimizing extravehicular activities. This study presents the development and evaluation of a digital twin for the AMADEE-24 analog Mars mission, organized by the Austrian Space Forum and conducted in Armenia in March 2024. Alternative local positioning methods were evaluated to enhance the system’s utility in Global Navigation Satellite System (GNSS)-denied environments. The digital twin integrates telemetry from the Aouda space suit simulators, inertial measurement unit motion capture (IMU-MoCap), and sensor data from the Intuitive Rover Operation and Collecting Samples (iROCS) rover. All nine experiment runs were reconstructed successfully by the developed digital twin. A comparative analysis of localization methods found that Simultaneous Localization and Mapping (SLAM)-based rover positioning and IMU-MoCap localization of the astronaut matched Global Positioning System (GPS) performance. Adaptive Cluster Detection showed significantly higher deviations compared to the previous GNSS alternatives. However, the IMU-MoCap method was limited by discontinuous segment-wise measurements, which required intermittent GPS recalibration. Despite these limitations, the results highlight the potential of alternative localization techniques for digital twin integration. Full article

In this paper, we investigate the target-approaching control problem for a discrete-time first-order vehicle system where the target area is modeled as a static circular region. In the absence of absolute bearing or position information, we propose a simple local controller that relies solely on range measurements to the target obtained at two consecutive sampling instants. Specifically, if the measured distance decreases between two successive samples, the vehicle maintains a constant velocity; otherwise, it rotates its velocity vector by an angle of $\pi /2$ in the clockwise direction. This control strategy guarantees convergence to the target region, ensuring that the vehicle’s velocity direction remains unchanged in the best-case scenario and is adjusted at most three times in the worst case. The effectiveness of the proposed method is theoretically established and further validated through outdoor experiments with a mobile vehicle. Full article

Quadruped robots have shown significant potential in disaster relief applications, where they have to navigate complex terrains for search and rescue or reconnaissance operations. However, their deployment is hindered by limited adaptability in highly uncertain environments, especially when relying solely on vision-based sensors like cameras or LiDAR, which are susceptible to occlusions, poor lighting, and environmental interference. To address these limitations, this paper proposes a novel sensor-enhanced hierarchical switching model predictive control (MPC) framework that integrates proprioceptive sensing with a bi-level hybrid dynamic model. Unlike existing methods that either rely on handcrafted controllers or deep learning-based control pipelines, our approach introduces three core innovations: (1) a situation-aware, bi-level hybrid dynamic modeling strategy that hierarchically combines single-body rigid dynamics with distributed multi-body dynamics for modeling agility and scalability; (2) a three-layer hybrid control framework, including a terrain-aware switching MPC layer, a distributed torque controller, and a fast PD control loop for enhanced robustness during contact transitions; and (3) a multi-IMU-based proprioceptive feedback mechanism for terrain classification and adaptive gait control under sensor-occluded or GPS-denied environments. Together, these components form a unified and computationally efficient control scheme that addresses practical challenges such as limited onboard processing, unstructured terrain, and environmental uncertainty. A series of experimental results demonstrate that the proposed method outperforms existing vision- and learning-based controllers in terms of stability, adaptability, and control efficiency during high-speed locomotion over irregular terrain. Full article

In order to address the problem of error accumulation in long-duration autonomous navigation using Strapdown Inertial Navigation Systems (SINS), this paper proposes an error prediction and correction method based on Deep Neural Networks (DNN). A 12-dimensional feature vector is constructed using angular increments, velocity increments, and real-time attitude and velocity states from the inertial navigation system, while a 9-dimensional response vector is composed of attitude, velocity, and position errors. The proposed DNN adopts a feedforward architecture with two hidden layers containing 10 and 5 neurons, respectively, using ReLU activation functions and trained with the Levenberg–Marquardt algorithm. The model is trained and validated on a comprehensive dataset comprising 5 × 10³ seconds of real vehicle motion data collected at 100 Hz sampling frequency, totaling 5 × 10⁵ sample points with a 7:3 train-test split. Experimental results demonstrate that the DNN effectively captures the nonlinear propagation characteristics of inertial errors and significantly outperforms traditional SINS and LSTM-based methods across all dimensions. Compared to pure SINS calculations, the proposed method achieves substantial error reductions: yaw angle errors decrease from 2.42 × 10⁻² to 1.10 × 10⁻⁴ radians, eastward velocity errors reduce from 455 to 4.71 m/s, northward velocity errors decrease from 26.8 to 4.16 m/s, latitude errors reduce from 3.83 × 10⁻³ to 7.45 × 10⁻⁴ radians, and longitude errors reduce dramatically from 3.82 × 10⁻² to 1.5 × 10⁻⁴ radians. The method also demonstrates superior performance over LSTM-based approaches, with yaw errors being an order of magnitude smaller and having significantly better trajectory tracking accuracy. The proposed method exhibits strong robustness even in the absence of external signals, showing high potential for engineering applications in complex or GPS-denied environments. Full article

Long-distance navigation requires global positioning methods to have complete autonomy, particularly when the Global Positioning System is unavailable. Considering that bionic polarized light-based global positioning technology exhibits good autonomy, this study develops an error model for autonomous global positioning based on the polarized skylight and a true north measurement instrument, using an approach of partial derivatives. The proposed model can rapidly and accurately provide the global error distribution of a bionic positioning method under varying angular measurement errors at different times. In addition, the conditions under which the proposed error model remains valid are investigated. The results indicate that the investigation can be simplified to verify whether the denominators of four partial derivatives of an implicit function system are simultaneously non-zero. The accuracy of the proposed error model is verified through numerical simulations. The results indicate that when the deviations of the two independent variables are up to 0.0001°, the positioning error mostly remains less than 14 m. In contrast, fewer geographical locations have positioning errors approaching positive infinity. By analyzing the global error distribution, one can effectively design and optimize the parameters of the autonomous global positioning system, enhancing its reliability and stability. Full article

SmallUnmanned Aerial Systems (sUAS) have seen rapid adoption thanks to advances in endurance, communications, autonomy, and manufacturing costs, yet most testing remains focused on GPS-supported, above-ground operations. This study introduces new test methodologies and presents comprehensive experimental evaluations of collision tolerance, navigation, and trajectory following for commercial sUAS platforms in GPS-denied indoor environments. We also propose numerical and categorical metrics—based on established vehicle collision protocols such as the Modified Acceleration Severity Index (MASI) and Maximum Delta V (MDV)—to quantify collision resilience; for example, the tested platforms achieved an average MASI of 0.1 g, while demonstrating clear separation between the highest- and lowest-performing systems. The experimental results revealed that performance varied significantly with mission complexity, obstacle proximity, and trajectory requirements, identifying platforms best suited for subterranean or crowded indoor applications. By aggregating these metrics, users can select the optimal drone for their specific mission requirements in challenging enclosed spaces. Full article

This paper explores breakthroughs from the perspective of UAV navigation architectures and proposes a UAV autonomous navigation method based on aerial–ground cooperative perception to address the challenge of UAV navigation in GPS-denied and unknown environments. The approach consists of two key components. Firstly, a mobile anchor trilateration and environmental modeling method is developed using a multi-UAV system by integrating the visual sensing capabilities of aerial surveillance UAVs with ultra-wideband technology. It constructs a real-time global 3D environmental model and provides precise positioning information, supporting autonomous planning and target guidance for near-ground UAV navigation. Secondly, based on real-time environmental perception, an improved D* Lite algorithm is employed to plan rapid and collision-free flight trajectories for near-ground navigation. This allows the UAV to autonomously execute collision-free movement from the initial position to the target position in complex environments. The results of real-world flight experiments demonstrate that the system can efficiently construct a global 3D environmental model in real time. It also provides accurate flight trajectories for the near-ground navigation of UAVs while delivering real-time positional updates during flight. The system enables UAVs to autonomously navigate in GPS-denied and unknown environments, and this work verifies the practicality and effectiveness of the proposed air–ground cooperative perception navigation system, as well as the mobile anchor trilateration and environmental modeling method. Full article

Geospatial navigation in GPS-denied environments presents significant challenges, particularly for autonomous vehicles operating in complex, unmapped regions. We explore the Earth’s geomagnetic field, a globally distributed and naturally occurring resource, as a reliable alternative for navigation. Since vehicles can only observe the geomagnetic field along their traversed paths, they must rely on incomplete information to infer the navigation strategy; therefore, we formulate the navigation problem as a partially observed Markov decision process (POMDP). To address this POMDP, we employ proximal policy optimization with long short-term memory (PPO-LSTM), a deep reinforcement learning framework that captures temporal dependencies and mitigates the effects of noise. Using real-world geomagnetic data from the international geomagnetic reference field (IGRF) model, we validate our approach through experiments under noisy conditions. The results demonstrate that PPO-LSTM outperforms baseline algorithms, achieving smoother trajectories and higher heading accuracy. This framework effectively handles the uncertainty and partial observability inherent in geomagnetic navigation, enabling robust policies that adapt to complex gradients and offering a robust solution for geospatial navigation. Full article

The introduction of autonomous vehicles in underground mine trackless transportation systems can significantly reduce safety risks for personnel in production operations and improve transportation efficiency. Current autonomous mining vehicle technology is characterized by complex algorithms and high deployment costs, which limit its widespread application in underground mines. This paper proposes a light-band-guided autonomous driving method for trackless mining vehicles, where a continuous, digitally controllable light band is installed at the tunnel ceiling to provide uninterrupted vehicle guidance. The light band is controlled by an independent hardware system and uses different colors to indicate vehicle movement status, enabling vehicles to navigate simply by following the designated light trajectory. We designed the necessary hardware and software systems and built a physical model for validation. The system enabled multiple vehicles to be guided simultaneously within the same area to perform diverse transportation tasks according to operational requirements. The model vehicles maintained a safe distance from tunnel walls. In GPS-denied environments, positioning was achieved using dead reckoning and periodic location updates at designated points based on the known light-band trajectory. The proposed method demonstrates high potential for practical applications. Full article

Due to traditional recovery systems lacking visual perception, it is difficult to monitor UAVs’ real-time status in communication-constrained or GPS-denied environments. This leads to insufficient ability in decision-making and parameter adjustment and increase uncertainty and risk of recovery. Visual inspection technology can make up for the limitations of GPS and communication and improve the autonomy and adaptability of the system. However, the existing RT-DETR algorithm is limited by single-path feature extraction, a simplified fusion mechanism, and high-frequency information loss, which makes it difficult to balance detection accuracy and computational efficiency. Therefore, this paper proposes a lightweight visual detection model based on transformer architecture to further optimize computational efficiency. Firstly, aiming at the performance bottleneck of existing models, the Parallel Backbone is proposed, which captures local features and global semantic information by sharing the initial feature extraction module and the double-branch structure, respectively, and uses the progressive fusion mechanism to realize the adaptive integration of multiscale features so as to balance the accuracy and lightness of target detection. Secondly, an adaptive multiscale feature pyramid network (AMFPN) is designed, which effectively integrates different scales of information through multi-level feature fusion and information transmission mechanism, alleviates the problem of information loss in small-target detection, and improves the detection accuracy in complex backgrounds. Finally, a wavelet frequency–domain-optimized reverse feature fusion mechanism (WT-FORM) is proposed. By using the wavelet transform to decompose the shallow features into multi-frequency bands and combining the weighted calculation and feature compensation strategy, the computational complexity is reduced, and the representation ability of the global context is further enhanced. The experimental results show that the improved model reduces the parameter size and computational load by 43.2% and 58% while maintaining detection accuracy comparable to the original RT-DETR in three datasets. Even in complex environments with low light, occlusion, or small targets, it can provide more accurate detection results. Full article

This paper presents a complete system for autonomous navigation in GPS-denied environments using a minimal sensor suite that operates onboard a robotic vehicle. Our system utilizes a single camera and, given a target destination without prior knowledge of the environment, replans in real time to generate a collision-free trajectory that avoids static and dynamic obstacles. To achieve this, we introduce, for the first time, a local Euclidean Signed Distance Field (ESDF) map with variable size and resolution, which scales as a function of the vehicle’s velocity. The map is updated at a high rate, requiring minimal computational power. Additionally, a short-term vicinity-based memory is maintained for previously observed areas to facilitate smooth trajectory generation, addressing the limited field-of-view provided by the RGB-D camera. System validation is carried out by deploying our algorithm on a differential drive vehicle in both simulation and real-world experiments involving static and dynamic obstacles. We benchmark our robotic system against state-of-the-art autonomous navigation frameworks, successfully navigating to designated target locations while avoiding obstacles in both static and dynamic scenarios, all without introducing additional computational overhead. Our approach consistently achieves the target goals even in complex settings where current state-of-the-art methods may fall short. Full article