ABSTRACT Path planning for construction robots in complex construction environments suffers from insufficient adaptability, poor path quality, and high computational cost. This paper proposes a hierarchical path planning method based on improved‐bidirectional‐rapidly‐exploring random tree‐artificial potential field. The proposed method integrates the following key techniques: (1) a semantically enhanced unified bounding volume hierarchy collision detection mechanism for identifying multiple categories of construction obstacle; (2) goal‐biased sampling and dynamic step size to enhance global search efficiency, combined with a modified potential field to improve obstacle avoidance; turning‐angle constraints and B‐spline smoothing are applied to optimize trajectory feasibility; (3) a weakly coupled mechanism model of the mobile vehicle and the robotic arm to reduce computational complexity. Digital twin and large language model are introduced to further enhance the engineering practicality. Ablation experiments are conducted in multiple simulation scenarios based on a scaled cable‐truss structure, demonstrating the superiority of the method in effectiveness and efficiency.
ABSTRACT This study develops a binocular stereo vision system to enhance the operational safety of agricultural unmanned aerial vehicles (UAVs) by detecting and localizing thin cables—one of the most prevalent and hazardous obstacles in farmland environments. The system integrates cost‐effective hardware (dual OV4689 CMOS sensors, 36°/8 mm lenses, 120 mm baseline) with a process‐enhanced SemiGlobal Block Matching algorithm, specifically tailored to address the core challenge of low‐contrast cable detection in complex agricultural settings. The algorithmic innovation combines Hue, Saturation, and Lightness color space segmentation with adaptive dynamic threshold processing, effectively resolving cable feature extraction under variable illumination conditions and intense background interference from crops or terrain. Experimental results demonstrate maximum detection distances of 14.547 and 16.846 m for 20 and 40 mm diameter cables, respectively, with an average ranging error of 0.055 m (0.58% error rate). UAV Flight tests confirm reliable obstacle avoidance within a 2–5 m/s velocity range, achieving an average braking distance of 11.828 m—sufficient to ensure safe maneuvering given agricultural UAVs' inherent dynamic constraints of high moment of inertia and low thrust‐to‐weight ratio. Compared with conventional approaches, the proposed system exhibits superior chromatic separation between cables and backgrounds and enhanced dynamic noise suppression, while maintaining low hardware cost. This renders it a highly cost‐efficient and deployable safety solution for agricultural scenarios with dense cable distributions. The research outcomes provide robust technical support for autonomous obstacle avoidance of agricultural UAVs and offer significant practical value for improving operational safety in precision agriculture.
ABSTRACT Rice cultivation is a cornerstone of global food security, yet effective weed management remains a critical challenge. Autonomous weeding robots offer a sustainable alternative, but their efficacy hinges on precise navigation, particularly in the complex visual environments of paddy fields. This study introduces a novel instance segmentation‐based navigation line extraction method tailored for paddy‐field robots. Unlike conventional machine vision techniques, which struggle with noise, water reflections, and irregular crop rows, our approach leverages a lightweight instance segmentation model to accurately identify and differentiate rice seedling rows. The model, optimized for deployment on resource‐constrained edge devices like the Jetson Nano, achieves high precision with minimal angular and pixel deviations (0.54° and 6.40 pixels, respectively). It also demonstrates real‐time inference speeds of 16.13 FPS, making it suitable for practical applications. Furthermore, our method eliminates the need for high‐precision data annotation, reducing labeling complexity and enhancing generalizability. Comparative experiments with existing methods, such as YOLOv8‐seg, ESNet, VGG‐UNet, Mobile‐UNet, and Mobile‐DeeplabV3+, demonstrate that our method achieves superior performance in both accuracy and computational efficiency. The proposed navigation line extraction method not only meets the demands of agricultural robots in paddy fields but also lays a foundation for future advancements in automated field navigation, potentially integrating GNSS/INS systems for fully autonomous operations. This research represents a significant step toward sustainable and efficient agricultural practices, addressing both environmental and economic challenges in rice cultivation.
ABSTRACT This paper presents an active disturbance rejection dynamic positioning (DP) control scheme of overactuated autonomous underwater vehicles (AUVs) for inspection missions. The proposed control scheme consists of dynamic control and control allocation. First, the DP process of the slender‐shaped AUV is decomposed into three sequential stages: yaw regulation, path following and position keeping. The tracking error calculation and control allocation are distinctively designed based on the motion performance of the AUV in each stage. Second, the adaptive line‐of‐sight (ALOS) guidance and extended state observer (ESO) are constructed to estimate and compensate for external disturbances to meet the high‐accuracy positioning requirements. A stability analysis is provided to establish the local UUB property of the closed‐loop system. Third, the thruster dead‐zone effect is compensated by shifting the control commands, and smooth control allocation between rudder and thrusters is achieved using a smooth transition function. Finally, the effectiveness and robustness of the proposed DP control scheme are verified through comparative lake trials. Compared to the conventional PID controller, the proposed method achieves statistically significant tracking error reductions () and substantially reduces equivalent control effort during steady‐state station‐keeping by suppressing actuator chattering.
ABSTRACT Maintaining traffic restrictions has become more difficult because growing number of vehicles on the road, particularly in locations with high populations. Efficient traffic monitoring and road safety management have become critical challenges due to the rapid increase in vehicular density in urban environments. This paper proposes a novel IoT‐aided robotic framework, termed MAGCNN‐RTMS‐IoT‐AR, for real‐time traffic sign detection and recognition using the German Traffic Sign Recognition Benchmark (GTSRB) dataset. Initially, the input images are pre‐processed using an Innovation Saturated Koopman Kalman Filter (ISKKF) to enhance data quality and robustness against noise. Subsequently, a Multi‐component Attention Graph Convolutional Neural Network (MAGCNN) is employed to effectively capture spatial and contextual dependencies for accurate classification of traffic signs. To further improve model performance, a Gradient Jellyfish Search Algorithm (GJSA) is integrated for optimal parameter tuning. The proposed approach is evaluated using multiple performance metrics, including accuracy, precision, recall, F 1‐score, error rate, loss, and mean square error (MSE). Experimental results demonstrate that the proposed method; significantly outperforms existing models such as RTMS‐IoT‐LeNet‐5, EATS‐CR‐CNN, and ETSR‐DS‐VGG‐16, achieving notable improvements in 19.36%, 24.82%, and 31.36%, higher accuracy; 21.62%, 24.54%, and 30.54% higher precision. The integration of IoT, robotics, and optimized deep learning makes the proposed system highly suitable for real‐time intelligent transportation applications.
