Search Results (60)

This study presents the design and development of a structurally optimized mobile robotic system with a machine learning-based energy-efficient path planning framework. Topology optimization (TO) and finite element analysis (FEA) were applied to reduce structural weight while maintaining mechanical integrity. The optimized components were manufactured using Fused Deposition Modeling (FDM) with ABS (Acrylonitrile Butadiene Styrene) material. A custom power analysis tool was developed to compare energy consumption between the optimized and initial designs. Real-world current consumption data were collected under various terrain conditions, including inclined surfaces, vibration-inducing obstacles, gravel, and direction-altering barriers. Based on this dataset, a path planning model was developed using machine learning algorithms, capable of simultaneously optimizing both energy efficiency and path length to reach a predefined target. Unlike prior works that focus separately on structural optimization or learning-based navigation, this study integrates both domains within a single real-world robotic platform. Performance evaluations demonstrated superior results compared to traditional planning methods, which typically optimize distance or energy independently and lack real-time consumption feedback. The proposed framework reduces total energy consumption by 5.8%, cuts prototyping time by 56%, and extends mission duration by ~20%, highlighting the benefits of jointly applying TO and ML for sustainable and energy-aware robotic design. This integrated approach addresses a critical gap in the literature by demonstrating that mechanical light-weighting and intelligent path planning can be co-optimized in a deployable robotic system using empirical energy data. Full article

Recent advancements have been made in unmanned freight systems at ports, effectively improving port freight efficiency and being widely promoted and popularized in the field of cargo transportation in major ports around the world. The path planning and motion control of port transfer vehicles are the key technology for automatic transportation of vehicles. How to integrate cutting-edge unmanned driving control technology into port unmanned freight transportation and improve the level of port automation is currently an important issue. This article introduces the three-layer control operation architecture of unmanned freight systems in ports, as well as the challenges of applying path planning and motion control technology for unmanned freight vehicles in port environments. It focuses on the mainstream algorithms of path planning and motion control technology, introduces their principles, provides a summary of their current development situation, and elaborates on the improvement and integration achievements of current researchers on algorithms. The algorithms are reviewed and contrasted, highlighting their respective strengths and weaknesses. Finally, this article looks ahead to the development trend of unmanned cargo transportation in ports and provides reference for the automation and intelligent upgrading of unmanned cargo transportation in ports in the future. Full article

With the modernization of agricultural production, single-arm machine systems in agriculture are unable to meet the needs of future agricultural development. In order to further improve agricultural operation efficiency, the collaborative operation of multi-robotic arms has become a hot topic in current research. This paper focuses on the task allocation problem in the collaborative operation of agricultural multi-robotic arms and summarizes the main algorithms currently used, including the genetic algorithm, particle swarm algorithm, etc., in terms of the aspects of work area division and task planning order. On this basis, further analysis is conducted on the path planning problem of agricultural multi-robotic arms. This paper summarizes the key technologies used in current research, including heuristic algorithms, fast search rapidly exploring random trees, reinforcement learning algorithms, etc., and focuses on reviewing the present applications of cutting-edge reinforcement learning algorithms in agricultural robotic arms. In summary, the agricultural multi-robot arms system can help with agricultural mechanization and intelligent production. Full article

At present, there is a lack of effective and usable machinery in the harvesting of aquatic vegetables. The harvesting of most aquatic vegetables such as Brasenia schreberi relies entirely on manual labor, resulting in a high labor demand and labor shortages, which restricts the industrial development of aquatic vegetables. To address this problem, an intelligent harvesting system for the aquatic vegetable Brasenia schreberi was developed in response to the challenging working conditions associated with harvesting it. The system is composed of a catamaran mobile platform, a picking device, and a harvesting manipulator control system. The mobile platform, driven by two paddle wheels, is equipped with a protective device to prevent vegetable stem entanglement, making it suitable for shallow pond aquatic vegetable environments. The self-designed picking device rapidly harvests vegetables through lateral clamping and cutting. The harvesting manipulator control system incorporates harvesting posture perception based on the YOLO-GS recognition algorithm and combines it with an improved RRT algorithm for robotic arm path planning. The experimental results indicate that the intelligent harvesting system is suitable for aquatic vegetable harvesting and the improved RRT algorithm surpasses the traditional one in terms of the planning time and path length. The vision-based positioning error was 4.80 mm, meeting harvesting accuracy requirements. In actual harvest experiments, the system showed an average success rate of 90.0%, with an average picking time of 5.229 s per leaf, thus proving its feasibility and effectiveness. Full article

With the widespread adoption of the Internet of Things (IoT), UAV–vehicle collaborative inspection systems are crucial for large-scale, IoT-enabled monitoring. Empowered by the IoT, these systems optimize resource allocation and boost the efficiency of IoT-based applications. Nevertheless, variable vehicle and UAV speeds due to wind and precipitation complicate path planning and task scheduling in the IoT-integrated setup. To solve this, this study offers an adaptive solution for dynamic, complex-weather scenarios within the IoT framework. A dynamic task-processing model was developed first, using real-time IoT sensor data for better decisions. Then, the KGTSA optimization algorithm was designed. It combines K-means clustering, HGA, and TS, considering UAV and vehicle speed variations in complex weather and making full use of IoT-device data. K-means generates an initial solution, HGA refines it, and TS fine-tunes UAV routes and task assignments. The simulation results show that KGTSA significantly cuts data collection time while maintaining flexibility. It efficiently manages speed and path uncertainties in complex weather, optimizing task efficiency without weather forecasts. Compared to traditional algorithms, KGTSA shortens data collection time and adapts better to dynamic IoT environments for real-world efficiency. Full article

In modern manufacturing, achieving high-speed laser cutting requires advanced robotic trajectory planning for smoothness and collision avoidance. Poorly optimized motion can cause frequent velocity changes, leading to mechanical vibrations that shorten machine service life. This study presents an innovative trajectory optimization approach for laser cutting machines equipped with a redundant standoff axis. A B-spline-based analytical model formulates rotational axes trajectories as quadratic programming problems to minimize jerk (the rate of acceleration change) under machining accuracy and kinematic constraints. Additionally, an M path, represented by the wrist center’s trajectory, refines translational axes by adjusting the standoff axis through a similar optimization model, thereby reducing mechanical stress. Collision avoidance is ensured through a concurrent iterative optimization process, considering the feasible domains of representative 3D geometric tool orientations. Simulation experiments on a complex B-pillar workpiece demonstrate the framework’s effectiveness, clearly indicating significant reductions in jerk and improved trajectory smoothness for both rotational and translational axes compared with conventional methods and a prior approach. This work advances high-speed machining capabilities by offering a novel, robust solution that leverages redundant structures to further improve trajectory smoothness and reliability in demanding industrial applications. Full article

Logging operations comprise a repeated and tedious job in forestry operations because forestry forwarders must keep completing round-trip transportation on forest roads from tree-cutting sites to forest roads where their truck can be accessed. In this study, an autonomous driving system for tracked forwarders was developed using GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System). The mechanical control system of the forwarder was replaced with an electronic control system, and path-planning and -tracking algorithms were implemented. The electronic control system, operated by servo motors to operate the driving levers, exhibited a response that was 150 milliseconds faster in lever control compared to manual operation. To generate an autonomous driving path, a skilled operator drove the forwarder along a forest road, and the recorded path was post-processed using the Novatel Inertial Explorer 8.70 GNSS + INS software to minimize GNSS errors. The autonomous forwarder followed the generated path using the pure pursuit algorithm. Autonomous driving tests conducted along this path achieved a root mean square error (RMSE) within 0.4 m (range: 0.389–0.393). Driving errors were primarily attributed to GNSS positional inaccuracies, especially in environments with dense canopies and landslide prevention structures located higher than the GNSS antenna, obstructing satellite signals. These findings underscore the importance and feasibility of autonomous forwarders in diverse forest environments, providing a critical foundation for advancing autonomous forestry machinery. The proposed technologies are expected to significantly contribute to enhancing the productivity of forestry operations. Full article

Wind energy’s crucial role in global sustainability necessitates efficient wind turbine maintenance, traditionally hindered by labor-intensive, risky manual inspections. UAV-based inspections offer improvements yet often lack adaptability to dynamic conditions like blade pitch and wind. To overcome these limitations and enhance inspection efficacy, we introduce the Dynamic Trajectory Adaptation Method (DyTAM), a novel approach for automated wind turbine inspections using UAVs. Within the proposed DyTAM, real-time image segmentation identifies key turbine components—blades, tower, and nacelle—from the initial viewpoint. Subsequently, the system dynamically computes blade pitch angles, classifying them into acute, vertical, and horizontal tilts. Based on this classification, DyTAM employs specialized, parameterized trajectory models—spiral, helical, and offset-line paths—tailored for each component and blade orientation. DyTAM allows for cutting total inspection time by 78% over manual approaches, decreasing path length by 17%, and boosting blade coverage by 6%. Field trials at a commercial site under challenging wind conditions show that deviations from planned trajectories are lowered by 68%. By integrating advanced path models (spiral, helical, and offset-line) with robust optical sensing, the DyTAM-based system streamlines the inspection process and ensures high-quality data capture. The dynamic adaptation is achieved through a closed-loop control system where real-time visual data from the UAV’s camera is continuously processed to update the flight trajectory on the fly, ensuring optimal inspection angles and distances are maintained regardless of blade position or external disturbances. The proposed method is scalable and can be extended to multi-UAV scenarios, laying a foundation for future efforts in real-time, large-scale wind infrastructure monitoring. Full article

Multi-island cavities are common and complex features in structural parts of the aerospace, energy, and power fields. The processing is hindered by low programming efficiency and a strong dependence on the experience of process engineers. In response to these challenges, this paper proposes a highly efficient and robust contour-parallel tool path planning method aimed at improving the rough machining efficiency and quality of multi-island cavities. The method decomposes the complex cavity into multiple sub-regions based on angular geometric features. Subsequently, a closed boundary is formed by connecting the islands with the outer contour using the bridge algorithm. On this base, the method applies rule-based criteria to assess the validity of offset intersections and extracts valid closed loops through point tracing, effectively mitigating both local and global interferences. This approach guarantees the generation of smooth and stable contour-parallel tool paths. The tool path experiments on multiple multi-island cavities demonstrate that the proposed method is capable of automatically generating continuous, interference-free, and residue-free machining paths, thus significantly enhancing machining efficiency and surface quality. Full article

Green asparagus grows in clusters, which can cause overlaps with weeds and immature stems, making it difficult to identify suitable harvest targets and cutting points. Extracting precise stem details in complex spatial arrangements is a challenge. This paper explored the YS3AM (Yolo-SAM-3D-Adaptive-Modeling) method for detecting green asparagus and performing 3D adaptive-section modeling using a depth camera, which could benefit harvesting path planning for selective harvesting robots. Firstly, the model was developed and deployed to extract bounding boxes for individual asparagus stems within clusters. Secondly, the stems inside these bounding boxes were segmented, and binary masks were generated. Thirdly, high-quality depth images were obtained through pixel block completion. Finally, a novel 3D reconstruction method, based on adaptive section modeling and combining the mask and depth data, is proposed. And an evaluation method is introduced to assess modeling accuracy. Experimental validation showed high-performance detection (1095 field images demonstrated, Precision: 98.75%, Recall: 95.46%, F1: 0.97) and robust 3D modeling (103 asparagus stems, average RMSE: length 0.74, depth: 1.105) under varying illumination conditions. The system achieved 22 ms per stem processing speed, enabling real-time operation. The results demonstrated that the 3D model accurately represents the spatial distribution of clustered green asparagus, enabling precise identification of harvest targets and cutting points. This model provided essential spatial pathways for end-effector path planning, thereby fulfilling the operational requirements for efficient green asparagus harvesting robots. Full article

Breast cancer, a prevalent and significant cause of cancer-related mortality in women, often necessitates precise detection through nuclear medicine techniques. Despite the utility of computer-aided navigation in thoracoscopic surgeries like mastectomy, challenges persist in accurately locating and tracking target tissues amidst intricate surgical scenarios. This study introduces a novel system employing a channel R-CNN model to automatically segment target regions in thoracoscopic images and provide precise cutting curve indications for surgeons. By integrating a Detection Network Head and Thorax Network Head, this multi-channel framework outperforms existing single-task models, marking a pioneering effort in cutting curve indication for thoracoscopic procedures. Utilizing a specialized dataset, the model achieves a notable region segmentation mIOU of 79.4% and OPA of 83.2%. In cutting path planning, it attains an mIOU of 68.6% and OPA of 77.5%. The system operates at an average speed of 23.6 frames per second in videos, meeting the real-time response needs of surgical navigation systems. This research underscores the potential of advanced imaging and AI-driven solutions in enhancing precision and efficacy in thoracoscopic surgeries. Full article

It is a great challenge for a safe surgery to localize the cutting tip during laminar grinding. To address this problem, we develop a framework of state estimation based on the CT image–force model. For the proposed framework, the pre-operative CT image and intra-operative milling force signal work as source inputs. In the framework, a bone milling force prediction model is built, and the surgical planned paths can be transformed into the prediction sequences of milling force. The intra-operative milling force signal is segmented by the tumbling window algorithm. Then, the similarity between the prediction sequences and the segmented milling signal is derived by the dynamic time warping (DTW) algorithm. The derived similarity indicates the position of the cutting tip. Finally, to overcome influences of some factors, we used the random sample consensus (RANSAC). The code of the functional simulations has be opened. Full article

Poplar is an important tree species for timber supply and ecological protection in northern China. Cultivating and selecting high-quality varieties and germplasm resources suitable for cultivation are key factors in enhancing the quality and productivity of poplar plantations in the arid and semi-arid northern regions with shorter growing seasons. This study conducted a field cultivation experiment on 10 progeny clones from the direct cross (D × M) of imported Populus deltoides ‘DD-109’ with Populus maximowiczii and 7 progeny clones from the reciprocal cross (M × D) using one-year-old rooted cuttings planted at a 4 m × 8 m spacing. Based on 17 years of annual growth observations, the study systematically compared growth characteristics, age of quantitative maturity, path relationships between traits, and early selection efficiency in the hybrid offspring. The results indicated that the D × M population had superior diameter at breast height (DBH), tree height (H), and volume (V) compared to the M × D population, while the height-to-diameter ratio (HDR) was lower. The growth rate of the 17 clones peaked from 10 to 14 years, with annual volume growth increments (PAIs) higher than mean annual volume increments (MAIs) during the early growth stages; the quantitative maturity age ranged between 12 and 16 years. The D × M population generally reached quantitative maturity earlier than the M × D population, with the fastest clone maturing in 12 years. Four clones (DM-9-17, DM-9-18, DM-9-14, and MD-61) showed values for V, DBH, H, and HDR above the hybrid group average. Path analysis demonstrated that DBH had the most significant direct and indirect effects on V, suggesting it as the best predictor for V. Using DBH as a reference, correlation and early selection efficiency analysis showed a strong relationship between growth characteristics at planting years 4–5 and later-stage performance, indicating this as the optimal period for early selection. These findings contribute to evaluating the production potential of P. deltoides ‘DD-109’ and P. maximowiczii germplasm in northern China and provide valuable guidance for selecting poplar clones suitable for local cultivation, accelerating breeding processes, and informing management planning for poplar plantations. Full article

As power system equipment gradually ages, the automated disassembly of transformers has become a critical area of research to enhance both efficiency and safety. This paper presents a transformer disassembly system designed for power systems, leveraging multimodal perception and collaborative processing. By integrating 2D images and 3D point cloud data captured by RGB-D cameras, the system enables the precise recognition and efficient disassembly of transformer covers and internal components through multimodal data fusion, deep learning models, and control technologies. The system employs an enhanced YOLOv8 model for positioning and identifying screw-fastened covers while also utilizing the STDC network for segmentation and cutting path planning of welded covers. In addition, the system captures 3D point cloud data of the transformer’s interior using multi-view RGB-D cameras and performs multimodal semantic segmentation and object detection via the ODIN model, facilitating the high-precision identification and cutting of complex components such as windings, studs, and silicon steel sheets. Experimental results show that the system achieves a recognition accuracy of 99% for both cover and internal component disassembly, with a disassembly success rate of 98%, demonstrating its high adaptability and safety in complex industrial environments. Full article

In robotic-assisted laminectomy decompression, stable and precise vertebral plate cutting remains challenging due to manual dependency and the absence of adaptive skill-learning mechanisms. This paper presents an advanced robotic vertebral plate-cutting system that leverages patient-specific anatomical variations and replicates the surgeon’s cutting technique through a trajectory parameter prediction model. A spatial mapping relationship between artificial and patient vertebrae is first established, enabling the robot to mimic surgeon-defined trajectories with high accuracy. The robotic system’s trajectory planning begins with acquiring point cloud data of the vertebral plate, which undergoes preprocessing, Non-Uniform Rational B-Splines (NURBS) fitting, and parametric discretization. Using the processed data, a spatial mapping method translates the surgeon’s cutting path to the robotic coordinate system, with simulation validating the trajectory’s adherence to surgical requirements. To further enhance the accuracy and stability of trajectory planning, a Backpropagation(BP) neural network is implemented, providing predictive modeling for trajectory parameters. The analysis and training of the neural network confirm its effectiveness in capturing complex cutting trajectories. Finally, experimental validation, involving an artificial vertebral body model and cutting trials on patient vertebrae, demonstrates the proposed method’s capability to deliver enhanced cutting precision and stability. This skill-learning-based, personalized trajectory planning approach offers significant potential for improving the safety and quality of orthopedic robotic surgeries. Full article