Search Results (17)

Unmanned underwater vehicles (UUVs) play a pivotal role in marine applications such as resource exploration, maritime search and rescue. However, communication signal loss remains a critical bottleneck, constraining UUV autonomous operation and mission reliability across four dimensions: navigation, coordination, monitoring, and planning. To address these challenges in communication-denied environments, this paper proposes a UUV digital twin system utilizing motion prediction technology, such as virtual mapping, prediction, and autonomous decision support. Based on a four-layer architecture—comprising the Physical Entity Layer, Virtual Entity Layer, Twin Data & Connectivity Layer, and Services Layer, the system achieves full-state mapping and real-time visualization. Specifically, a hybrid prediction model integrating Transformer and Convolutional Neural Networks (CNN) architectures is developed to extract multi-scale features for resistance prediction, which serves as the critical basis for UUV motion state forecasting. Experimental validation confirms the system’s capability for real-time resistance tracking and high-precision prediction, providing a robust foundation for autonomous navigation control and energy management. These results advance the development of specialized UUV digital twin systems and establish a robust foundation for their engineering applications. Full article

The rapid development of intelligent transportation systems and autonomous driving technologies has made visual perception a key component in ensuring safety and improving efficiency in complex traffic environments. As a core task in visual perception, object detection directly affects the reliability of downstream modules such as path planning and decision control. However, adverse weather conditions (e.g., fog, rain, and snow) significantly degrade image quality—causing texture blurring, reduced contrast, and increased noise—which in turn weakens the robustness of traditional detection models and raises potential traffic safety risks. To address this challenge, this paper proposes an enhanced object detection framework, ECL-YOLOv11 (Edge-enhanced, Context-guided, and Lightweight YOLOv11), designed to improve detection accuracy and real-time performance under adverse weather conditions, thereby providing a reliable solution for in-vehicle perception systems. The ECL-YOLOv11 architecture integrates three key modules: (1) a Convolutional Edge-enhancement (CE) module that fuses edge features extracted by Sobel operators with convolutional features to explicitly retain boundary and contour information, thereby alleviating feature degradation and improving localization accuracy under low-visibility conditions; (2) a Context-guided Multi-scale Fusion Network (AENet) that enhances perception of small and distant objects through multi-scale feature integration and context modeling, improving semantic consistency and detection stability in complex scenes; and (3) a Lightweight Shared Convolutional Detection Head (LDHead) that adopts shared convolutions and GroupNorm normalization to optimize computational efficiency, reduce inference latency, and satisfy the real-time requirements of on-board systems. Experimental results show that ECL-YOLOv11 achieves mAP@50 and mAP@50–95 values of 62.7% and 40.5%, respectively, representing improvements of 1.3% and 0.8% over the baseline YOLOv11, while the Precision reaches 73.1%. The model achieves a balanced trade-off between accuracy and inference speed, operating at 237.8 FPS on standard hardware. Ablation studies confirm the independent effectiveness of each proposed module in feature enhancement, multi-scale fusion, and lightweight detection, while their integration further improves overall performance. Qualitative visualizations demonstrate that ECL-YOLOv11 maintains high-confidence detections across varying motion states and adverse weather conditions, avoiding category confusion and missed detections. These results indicate that the proposed framework provides a reliable and adaptable foundation for all-weather perception in autonomous driving systems, ensuring both operational safety and real-time responsiveness. Full article

The primary challenge that robots face in disaster rescue is to precisely and efficiently construct disaster maps and achieve autonomous navigation. This paper proposes a method for air–ground collaborative map construction. It utilizes the flight capability of an unmanned aerial vehicle (UAV) to achieve rapid three-dimensional space coverage and complex terrain crossing for rapid and efficient map construction. Meanwhile, it utilizes the stable operation capability of an unmanned ground vehicle (UGV) and the ground detail survey capability to achieve precise map construction. The maps constructed by the two are accurately integrated to obtain precise disaster environment maps. Among them, the map construction and positioning technology is based on the FAST LiDAR–inertial odometry 2 (FAST-LIO2) framework, enabling the robot to achieve precise positioning even in complex environments, thereby obtaining more accurate point cloud maps. Before conducting map fusion, the point cloud is preprocessed first to reduce the density of the point cloud and also minimize the interference of noise and outliers. Subsequently, the coarse and fine registrations of the point clouds are carried out in sequence. The coarse registration is used to reduce the initial pose difference of the two point clouds, which is conducive to the subsequent rapid and efficient fine registration. The coarse registration uses the improved sample consensus initial alignment (SAC-IA) algorithm, which significantly reduces the registration time compared with the traditional SAC-IA algorithm. The precise registration uses the voxelized generalized iterative closest point (VGICP) algorithm. It has a faster registration speed compared with the generalized iterative closest point (GICP) algorithm while ensuring accuracy. In reinforcement learning navigation, we adopted the deep deterministic policy gradient (DDPG) path planning algorithm. Compared with the deep Q-network (DQN) algorithm and the A* algorithm, the DDPG algorithm is more conducive to the robot choosing a better route in a complex and unknown environment, and at the same time, the motion trajectory is smoother. This paper adopts Gazebo simulation. Compared with physical robot operation, it provides a safe, controllable, and cost-effective environment, supports efficient large-scale experiments and algorithm debugging, and also supports flexible sensor simulation and automated verification, thereby optimizing the overall testing process. Full article

Apple harvesting faces increasing challenges owing to rising labor costs and the limited seasonal workforce availability, highlighting the need for robotic harvesting solutions in precision agriculture. This study presents a 6-DOF robotic arm system designed for harvesting in dwarf hedge-planted orchards, featuring a lightweight perception module, a task-adaptive motion planner, and an adaptive soft gripper. A lightweight approach was introduced by integrating the Faster module within the C2f module of the You Only Look Once (YOLO) v8n architecture to optimize the real-time apple detection efficiency. For motion planning, a Dynamic Temperature Simplified Transition Adaptive Cost Bidirectional Transition-Based Rapidly Exploring Random Tree (DSA-BiTRRT) algorithm was developed, demonstrating significant improvements in the path planning performance. The adaptive soft gripper was evaluated for its detachment and load-bearing capacities. Field experiments revealed that the direct-pull method at 150 mN·m torque outperformed the rotation-pull method at both 100 mN·m and 150 mN·m. A custom control system integrating all components was validated in partially controlled orchards, where obstacle clearance and thinning were conducted to ensure operation safety. Tests conducted on 80 apples showed a 52.5% detachment success rate and a 47.5% overall harvesting success rate, with average detachment and full-cycle times of 7.7 s and 15.3 s per apple, respectively. These results highlight the system’s potential for advancing robotic fruit harvesting and contribute to the ongoing development of autonomous agricultural technologies. Full article

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a large payload in underwater scenarios. More precisely, by using the advantages of multi-UUV formation cooperation, based on rigidity graph theory and backstepping technology, the distance between each UUV, as well as the UUV and the transport payload, is controlled to form a three-dimensional rigid structure so that the load remains balanced and stable, to coordinate the transport of objects within the feasible area of the workspace. Moreover, a neural network (NN) is utilized to maintain system stability despite unknown nonlinearities and disturbances in the system dynamics. In addition, based on the interfered fluid flow algorithm, a collision-free motion trajectory was planned for formation systems. The control scheme also performs real-time formation reconfiguration according to the size and position of obstacles in space, thereby enhancing the flexibility of cooperative handling. The uniform ultimate boundedness of the formation distance errors is comprehensively demonstrated by utilizing the Lyapunov stability theory. Finally, the simulation results show that the UUVs can quickly form and maintain the desired formation, transport the payload along the planned trajectory to shuttle in multi-obstacle environments, verify the feasibility of the method proposed in this paper, and achieve the purpose of the collaborative transportation of large underwater payload by multiple UUVs and their targeted delivery. Full article

The automation of adhesive application in footwear manufacturing is challenging due to complex surface geometries and model variability. This study presents an integrated 3D vision-based robotic system for adhesive spraying on lasted uppers. A triangulation-based scanning setup reconstructs each upper into a high-resolution point cloud, enabling customized spraying path planning. A six-axis robotic arm executes the path using an adaptive transformation matrix that aligns with surface normals. UV fluorescent dye and inspection are used to verify adhesive coverage. Experimental results confirm high repeatability and precision, with most deviations within the industry-accepted ±1 mm range. While localized glue-deficient areas were observed around high-curvature regions such as the toe cap, these remain limited and serve as a basis for further system enhancement. The system significantly reduces labor dependency and material waste, as observed through the replacement of four manual operators and the elimination of adhesive over-application in the tested production line. It has been successfully installed and validated on a production line in Hanoi, Vietnam, meeting real-world industrial requirements. This research contributes to advancing intelligent footwear manufacturing by integrating 3D vision, robotic motion control, and automation technologies. Full article

Trajectory planning is essential for robotic polishing tasks, as the effectiveness of this planning directly influences the quality of the work and the energy efficiency of the operation. This study introduces an innovative trajectory planning method for robotic polishing tasks, focusing on the development and application of quintic B-spline interpolation. Recognizing the critical impact of trajectory planning on the quality and energy efficiency of robotic operations, we analyze the structure and parameters of the ABB-IRB120 robot within a laboratory setting. Using the Denavit–Hartenberg parameter method, a kinematic model is established, and the robot’s motion equations are derived through matrix transformation. We then propose a novel approach by implementing both fifth-degree polynomial and quintic B-spline interpolation algorithms for planning the robot’s spatial spiral arc trajectory, which is a key contribution of this work. The effectiveness of these methodologies is validated through simulation in MATLAB’s robotics toolbox. Our findings demonstrate that the quintic B-spline interpolation not only significantly improves task precision but also optimizes energy consumption, making it a superior method for trajectory planning in robotic grinding applications. By integrating advanced interpolation techniques, this study provides substantial technological and environmental benefits, offering a groundbreaking reference for enhancing the precision and efficiency of robotic control systems. Full article

With the rapid advancement of autonomous driving technology, the recognition of vehicle lane-changing can provide effective environmental parameters for vehicle motion planning, decision-making and control, and has become a key task for intelligent vehicles. In this paper, an improved method for vehicle taillight detection and intent recognition based on YOLOv8 (You Only Look Once version 8) is proposed. Firstly, the CARAFE (Context-Aware Reassembly Operator) module is introduced to address fine perception issues of small targets, enhancing taillight detection accuracy. Secondly, the TriAtt (Triplet Attention Mechanism) module is employed to improve the model’s focus on key features, particularly in the identification of positive samples, thereby increasing model robustness. Finally, by optimizing the EfficientP2Head (a small object auxiliary head based on depth-wise separable convolutions) module, the detection capability for small targets is further strengthened while maintaining the model’s practicality and lightweight characteristics. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 93.27%, a recall rate of 79.86%, and a mean average precision (mAP) of 85.48%, which shows that the proposed method could effectively achieve taillight detection. Full article

With the advancement of computer vision and sensor technologies, many multi-camera systems are being developed for the control, planning, and other functionalities of unmanned systems or robots. The calibration of multi-camera systems determines the accuracy of their operation. However, calibration of multi-camera systems without overlapping parts is inaccurate. Furthermore, the potential of feature matching points and their spatial extent in calculating the extrinsic parameters of multi-camera systems has not yet been fully realized. To this end, we propose a multi-camera calibration algorithm to solve the problem of the high-precision calibration of multi-camera systems without overlapping parts. The calibration of multi-camera systems is simplified to the problem of solving the transformation relationship of extrinsic parameters using a map constructed by multiple cameras. Firstly, the calibration environment map is constructed by running the SLAM algorithm separately for each camera in the multi-camera system in closed-loop motion. Secondly, uniformly distributed matching points are selected among the similar feature points between the maps. Then, these matching points are used to solve the transformation relationship between the multi-camera external parameters. Finally, the reprojection error is minimized to optimize the extrinsic parameter transformation relationship. We conduct comprehensive experiments in multiple scenarios and provide results of the extrinsic parameters for multiple cameras. The results demonstrate that the proposed method accurately calibrates the extrinsic parameters for multiple cameras, even under conditions where the main camera and auxiliary cameras rotate 180°. Full article

The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology. Full article

The working environment of construction machinery is harsh, and some operations are highly repetitive. The realization of intelligent construction machinery helps to improve economic efficiency and promote industrial development. Construction machinery is different from ordinary passenger vehicles. Aiming at the fact that the existing environmental perception data set cannot be directly applied to construction machinery, this paper establishes the corresponding data set in combination with the specific working conditions of construction machinery and carries out training based on the PointPillars network to realize the environmental perception function applicable to the working conditions of construction machinery. Most construction machinery runs on unstructured roads, and the existing passenger vehicle path planning algorithm is not applicable to construction machinery. Based on this, this paper uses a hybrid A* algorithm to achieve path planning that meets the kinematics of construction machinery and realizes real-time obstacle detection and avoidance. At the same time, this paper combines environmental perception with a path planning algorithm to provide a method of autonomous path finding and obstacle avoidance for construction machinery. Based on the improved pure pursuit algorithm, the high-precision motion control and established trajectory tracking of construction machinery are realized, which lays a certain foundation for the follow-up research and development of related intelligent technologies of construction machinery. Full article

With the progress and development of AI technology and industrial automation technology, AI robot dogs are widely used in engineering practice to replace human beings in high-precision and tedious industrial operations. Bionic robots easily produce control errors due to the influence of spatial disturbance factors in the process of pose determination. It is necessary to calibrate robots accurately to improve the positioning control accuracy of bionic robots. Therefore, a robust control algorithm for bionic robots based on binocular vision navigation is proposed. An optical CCD binocular vision dynamic tracking system is used to measure the end position and pose parameters of a bionic robot, and the kinematics model of the controlled object is established. Taking the degree of freedom parameter of the robot’s rotating joint as the control constraint parameter, a hierarchical subdimensional space motion planning model of the robot is established. The binocular vision tracking method is used to realize the adaptive correction of the position and posture of the bionic robot and achieve robust control. The simulation results show that the fitting error of the robot’s end position and pose parameters is low, and the dynamic tracking performance is good when the method is used for the position positioning of control of the bionic robot. Full article

A robotic technology consisting of an industrial robot mounted on an autonomous rover used to tap slash pine trees and collect their oleoresin for processing is introduced, and the technological challenges related to the robotic operations are discussed in detail. Unlike the case of industrial automated manufacturing systems where the relative position between the tool and workpiece can be controlled within a few hundredths of a millimeter accuracy, when used in highly unstructured environments characteristic to forestry or agriculture, the positioning accuracy between the industrial robot and the target on which it operates can be much lower than the accuracy required for the operation of the industrial robot. The paper focuses on presenting the robotic operations necessary for drilling three converging boreholes in the pine tree, spraying the boreholes with chemicals, inserting a plastic tube with pre-attached collection bag in one borehole and inserting two plugs in other two boreholes. The challenges related to performing these robotic operations in conditions of large variations in the actual shape of the pine tree trunk and variations in the relative position between the robot and the pine tree after the autonomous vehicle positions itself in front of the tree are presented. The technical solutions used to address these challenges are also described. The strategies used to programmatically adjust the robot toolpath based on detection of the borehole entry points and on the measurement of the insertion force are presented. Full article

Ultra-Precision Machining (UPM) is a kind of highly accurate processing technology developed to satisfy the manufacturing requirements of high-end cutting-edge products including nuclear energy producers, very large-scale integrated circuits, lasers, and aircraft. The information asymmetry phenomenon widely exists in the design and control of ultra-precision machining. It may lead to inconsistency between the designed performance and operational performance of the UPM equipment on stiffness, thermal stability, and motion accuracy, which result from its design, manufacturing, and control, and determine the form accuracy and surface roughness of machined parts. The performance of the UPM equipment should be improved continuously. It is still challenging to realize the real-time and self-adaptive control, in which building a high-fidelity and computationally efficient digital twin is a valuable solution. Nevertheless, the incorporation of the digital twin technology into the UPM design and control remains vague and sometimes contradictory. Based on a literature search in the Google Scholar database, the critical issues in the UPM design and control, and how to use the digital twin technologies to promote it, are reviewed. Firstly, the digital twins-based UPM design, including bearings module design, spindle-drive module design, stage system module design, servo module design, and clamping module design, are reviewed. Secondly, the digital twins-based UPM control studies, including voxel modeling, process planning, process monitoring, vibration control, and quality prediction, are reviewed. The key enabling technologies and research directions of digital twins-based design and control are discussed to deal with the information asymmetry phenomenon in UPM. Full article

This paper presents the potential of combining ROS (Robot Operating System), its state-of-art software, and EtherCAT technologies to design real-time robot control architecture for human–robot collaboration. For this, the advantages of an ROS framework here are it is easy to integrate sensors for recognizing human commands and the well-developed communication protocols for data transfer between nodes. We propose a shared memory mechanism to improve the communication between non-real-time ROS nodes and real-time robot control tasks in motion kernel, which is implemented in the ARM development board with a real-time operating system. The jerk-limited trajectory generation approach is implemented in the motion kernel to obtain a fine interpolation of ROS MoveIt planned robot path to motor. EtherCAT technologies with precise multi-axis synchronization performance are used to exchange real-time I/O data between motion kernel and servo drive system. The experimental results show the proposed architecture using ROS and EtherCAT in hard real-time environment is feasible for robot control application. With the proposed architecture, a user can efficiently send commands to a robot to complete tasks or read information from the robot to make decisions, which is helpful to reach the purpose of human–robot collaboration in the future. Full article