Search Results (275)

Robotics optimization is essential for improving the performance, efficiency, and reliability of robotic systems, especially when dealing with complex engineering problems involving multiple conflicting objectives. Algorithm-specific parameter-free metaheuristic algorithms have gained attention in such applications because they eliminate the need for problem-specific parameter tuning. However, their performance can be further enhanced by improving convergence and maintaining solution diversity in multi-objective optimization. This paper proposes three multi-objective variants—archive, opposition, and self-adaptive multi-population (SAMP)—for the algorithm-specific parameter-free BxR algorithms such as Best–Mean–Random (BMR), Best–Worst–Random (BWR), and Best–Mean–Worst–Random (BMWR). The proposed variants are evaluated on five robotic optimization problems spanning two to six objectives, including Autonomous Underwater Vehicle shape optimization, power line inspection robot design, inverse kinematics of a 4-DOF manipulator, wall-building robot trajectory planning, and optimization of a reconfigurable parallel cutting and grinding mechanism. Their performance is compared with several established multi-objective algorithms using metrics such as GD, IGD, SPC, and HV, supported by rigorous statistical testing involving Friedman tests, Conover post hoc analysis with Holm correction, and Vargha–Delaney A₁₂ effect sizes over 30 independent runs. The results show that archive variants achieve the best IGD rank in four of the five case studies and the best HV rank in three of them, with the five-objective trajectory planning problem being the sole exception where SAMP and base BxR variants show improved IGD performance. The base BxR algorithms prove to be strong competitors, consistently outperforming established parameter-dependent methods on IGD across all five problems. The opposition variants do not provide consistent improvement; however, they also do not cause catastrophic degradation, suggesting that refined opposition strategies warrant further investigation. The study demonstrates the effectiveness of the proposed algorithms as practical optimization tools for complex robotic optimization problems. Full article

In unmanned aerial vehicle (UAV)-based power line inspection, multi-scale defects and complex backgrounds challenge the balance between detection accuracy, speed, and model lightweighting, limiting automated grid inspection. This paper proposes a Multi-Scale Mamba Framework (MS-Mamba) for efficient and accurate defect perception. A drone inspection dataset containing 5137 images from 14 defect categories was constructed and divided into training and validation sets with an 8:2 split. To address the large scale variation among defects, the categories are decoupled into macroscopic, mesoscopic, and microscopic groups according to physical attributes and visual scales. As the core perception engine, a lightweight state-space mechanism is designed to balance accuracy and deployability. A spatial resolution-aware hierarchical reconstruction strategy and a dynamic feature selection mechanism are integrated to enhance feature extraction, reduce background redundancy, and improve small-target representation. Compared with the YOLOv5s baseline, MS-Mamba achieves an mAP@0.5 of 0.749, corresponding to a 15.6 percentage-point improvement, while reducing parameters by 0.13 M and computational cost by 1.7 GFLOPs. Ablation studies and visual analyses further confirm fewer missed and false detections in complex backgrounds. The developed end-to-end inspection system was validated through closed-loop engineering tests, demonstrating strong potential for industrial deployment. Full article

We present a hybrid algorithm for 3D obstacle-avoidance path planning of a six-axis robotic arm in cable inspection environments. It improves on traditional RRT, which suffers from blind sampling and low efficiency, and APF, which tends to become stuck in local optima and has unstable potential fields. For the bidirectional RRT, we introduce target-biased sampling and a dynamic step-size expansion strategy driven by target attraction to enhance sampling directionality. For the APF, we optimize the potential field function by incorporating shape and size factors, use simulated annealing to overcome local optima, and apply Gaussian filtering to smooth the potential field. A triangular inequality pruning strategy with a target chain is then used to optimize the initial path, combined with cubic B-spline curves for path smoothing, and we design a simplified collision detection method to reduce computational cost. Simulation experiments are carried out in 2D and 3D spaces, as well as in a robotic arm setup that mimics cable inspection. Compared with basic RRT, bidirectional RRT, and the RRT-APF fusion algorithm, our method achieves significant improvements in average iteration count, planning time, path length, and number of generated nodes. The resulting trajectories are shorter and smoother, effectively boosting the efficiency and quality of 3D obstacle-avoidance path planning for six-axis robotic arms, and offering a practical solution for engineering scenarios such as power line inspection. Full article

To address the challenges of insufficient small target recognition, difficulty in edge information extraction, and high computational overhead in insulator defect detection, this paper proposes a lightweight detection method based on the YOLO-MLSL model for transmission line insulator defect detection. First, a C2f-LRFCA module is introduced, effectively enhancing feature interaction through a long-range convolutional attention mechanism, thereby improving the perception of fine-grained defects. Second, an MEUM multi-scale feature enhancement module is designed to achieve more efficient contextual information fusion during upsampling, improving the detection performance for multi-scale targets. Third, the ShapeIoU loss function is employed to improve the bounding box regression accuracy in complex backgrounds, and LAMP pruning technology significantly reduces the model’s computational and storage overhead. Experimental results show that the improved algorithm achieves an mAP@0.5 of 85.4%, a 4.1% improvement compared to the original YOLOv8n, while maintaining a low parameter count and computational complexity, demonstrating both high accuracy and efficiency. This research provides a valuable reference for the design and application of lightweight target detection models in the intelligent inspection of power equipment. Full article

Tungsten carbide and cobalt cutting tools require low surface roughness to improve cutting performance by reducing the wear from machining friction. While this is achieved by conventional manufacturing processes (pressing and sintering, grinding), with additive manufacturing processes it is more difficult (layer height, printing strategy). Since less costly and more sustainable solutions (without lubricants) are being studied as alternatives to conventional processes, a complementary technology (laser ablation) is suggested for the additive manufacturing of green WC-10Co. In this study, material extrusion (MEX) was used to produce green WC-10Co 3D objects, followed by laser ablation (50 W ytterbium fiber laser, 800–1100 nm wavelength) on their surface. Different laser strategies and parameters (power, speed, frequency, distance between lines, number of passages) were tested to find the most suitable. Most combinations were excluded by initial visual inspection, while the best ones were measured with a contact and non-contact profilometer. Further analysis was made on the composition and microstructure (with techniques such as Raman spectroscopy, scanning electron microscope, x-ray diffraction, and hardness indentation) to study what the interaction with the laser changed on the surface. Results show that with a combination of 50 W laser power, 1000 mm/s laser speed, 2000 kHz laser frequency, 0.1 mm distance between lines and three laser passages, it was possible to achieve a surface roughness of 0.6 µm (Sa) for the sintered WC-10Co, produced by MEX. No η-phase and graphite were detected, as well as microporosity and fissures. Full article

To address the challenges of strong coupling between resource allocation and collaborative scheduling in vehicle–UAV cooperative inspections of power distribution lines, as well as the difficulty in balancing efficiency and stability, this paper proposes a path-scheduling feedback-based evolutionary cooperative optimization method. First, an integrated modeling framework for resource allocation and execution scheduling is constructed, incorporating vehicle path decisions and drone task scheduling into a unified optimization space. Next, a feedback-driven two-layer multi-objective evolutionary collaborative optimization algorithm (FB-MOC²) is introduced. The outer layer performs evolutionary search for adaptive resource allocation, while the inner layer solves path planning and collaborative scheduling, with dynamic resource adjustments achieved through execution-layer feedback, forming a data-driven adaptive optimization process. Subsequently, sensitivity analysis is conducted on resource deployment mechanisms, revealing phased evolutionary patterns between resource scale and system performance, and identifying the effective operational range for resource allocation. Finally, the algorithm’s robustness is validated under multiple failure scenarios. Simulation results demonstrate that the proposed method reduces total operation time from 412 min to 315 min, improves battery utilization to 78.5%, and maintains recovery costs within 1.65 times the baseline even under high drone failure rates, while ensuring full inspection coverage. This approach provides an effective bio-inspired and data-driven solution for adaptive resource allocation and robust scheduling in intelligent power distribution line inspections. Full article

Advancements in infrared imaging technology have introduced a novel perspective for inspecting power transmission lines. Nevertheless, the inherent low contrast and indistinct edges of infrared images present significant challenges, rendering the direct application of traditional semantic segmentation algorithms unsatisfactory. To mitigate this problem, we propose a multi-neighborhood densely connected network architecture. This framework incorporates two pivotal modules: the Multi-Head Squeeze-and-Excitation (MHSE) module and the Multi-Neighborhood Feature Fusion (MNFF) module. The MHSE enhances local feature representations by capturing nuanced feature interactions, thereby alleviating the issue of imbalanced global feature weight distribution. The MNFF aggregates feature data from multiple adjacent nodes at each node’s input, which not only facilitates the integration of multi-scale target features but also leverages neighborhood information to precisely localize and amplify features within specific regions. Furthermore, we have built the first Infrared Dataset of Power Transmission Line Suspension Clamp (CLAMPTISS) to substantiate our approach. Empirical evidence demonstrates that our proposed network surpasses state-of-the-art networks across three key metrics: the mean Intersection over Union (mIoU) and localization accuracy (Pd) have increased by 8.3% and 13.3%, respectively, while the false alarm rate (Fa) has decreased by 38.2%. Full article

Quadrotor motion planning in cluttered environments presents significant challenges in achieving both computational efficiency and trajectory smoothness, particularly in low-altitude economy and intelligent energy system applications where autonomous aerial vehicles perform infrastructure inspection and power line monitoring. Many existing methods either rely on sampling-based algorithms that suffer from long computation times and suboptimal paths, or employ trajectory representations that produce high-order derivative discontinuities unsuitable for agile flight. In this work, we propose an efficient hierarchical motion planning framework that integrates a JPS–Bresenham-based path search with safe flight corridor construction and Bézier curve optimization. Our approach addresses trajectory generation through a two-stage process: a front-end path search that efficiently identifies collision-free paths with reduced waypoints, followed by a back-end optimization that leverages convex safe corridors with overlapping regions to expand the solution space. Through comprehensive benchmark experiments across six different map scenarios, we demonstrate that our method outperforms RRT* and PRM in both path quality and computational efficiency. Monte Carlo experiments across varying map sizes and obstacle densities confirm robustness and scalability advantages. Comparative studies with state-of-the-art planners demonstrate superior success rates and cost efficiency while maintaining strict kinodynamic feasibility. The Bézier-based optimization reduces snap integral by up to 55% compared to ordinary polynomial approaches, demonstrating its superiority for fast quadrotor trajectory planning in complex environments. Full article

Medium voltage induction motors (MVIM) are a key component of numerous industries, such as water treatment plants, sewage discharge stations, and chilled water systems. The starting process for these MV motors is critical as it is associated with a major impact on both motor lifetime and power grid quality. In this article, a proposed modified and comprehensive starting scheme of MV three-phase induction motors driving pumps for water stations is introduced. Firstly, the starting performance and its impact on power grid quality will be discussed when all motors are normally started with direct on line connection (DOL), which is already the normal established status. A modified starting scheme based on an optimized coordination of motor starting methods in addition to variable voltage variable frequency drive (VVVFD) drive and control implementation will be discussed. A transition between the starting of variant MV induction motors as well as the starting event coordination principle will be discussed to improve the power quality relative to the obligatory time shift required for the operation. The coordination is based on an algorithm implementation which is achieved using different optimization concepts based on artificial intelligence techniques, properly conducting the transition time in addition to the power delivered by the inverter unit rather than determining the number of DOL and VVVF-implemented motors. A comparison between using the optimized VVVFD soft-starting and the proposed modified scheme is performed, focusing on the power quality improvement rather than optimizing the cost function. The modified scheme is simulated using ETAP power station for brief analysis and study of load flow rather than the complete inspection and power quality assessment. Full article

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

Insulators are essential components in high-voltage transmission lines and require regular inspection to ensure reliable power delivery. Traditional manual inspection methods are inefficient and labor intensive, highlighting the need for intelligent and automated solutions. In this study, we propose a missing insulator detection method that integrates Unmanned Aerial Vehicle (UAV) imaging with deep learning techniques. Firstly, an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) is employed to detect and localize insulators in aerial images. Secondly, the localized insulators are segmented using an improved U-Net to reduce background interference. A bounding box regression approach is adopted to obtain the minimum enclosing rectangles, and the insulators are aligned vertically. Adaptive thresholding is then applied to extract binary images of the insulators. These binary images are further transformed into defect curves, from which missing insulators are identified based on curve distribution. To address the limited availability of labeled samples, a transfer learning-based strategy is adopted to improve model generalization. A dataset of glass insulators was collected using a DJI M300 UAV equipped with an H20T camera along a 330 kV overhead transmission line. On the collected UAV insulator dataset, the proposed method achieved an AP@0.5 of 99.85% and an average IoU of 88.56% for insulator string detection, while the improved U-Net achieved an mIoU of 89.73% for insulator string segmentation. Outdoor flight experiments further verified performance under varying backgrounds and illumination conditions in our UAV inspection scenarios. Full article

The paper presents a comprehensive deep learning-based framework for automated visual inspection of overhead power line infrastructure using unmanned aerial vehicles. Traditional manual and helicopter inspections are costly, time-consuming, and hazardous for maintenance personnel. The proposed approach integrates UAV imaging with advanced computer vision models such as YOLOv8, EfficientDet-D2, and Faster R-CNN to automatically detect defects in critical components, including insulators, conductors, and transmission towers. Several open datasets (InsPLAD, TTPLA, MPID) were used for training and validation, ensuring robustness under diverse lighting and environmental conditions. Experimental results demonstrate that YOLOv8 achieved the best performance, reaching 88.5% mAP@0.5 with real-time inference capabilities (over 50 FPS on GPU). The system significantly enhances inspection efficiency, allowing for a threefold increase in coverage capacity and an up to 70% reduction in defect remediation time. The integration of AI-powered visual analytics with maintenance and SCADA systems enables a shift from reactive to predictive maintenance, improving the safety, reliability, and resilience of power transmission infrastructure. Full article

High-precision localization is essential for autonomous navigation and environment perception of unmanned aerial vehicles (UAVs) in complex power inspection scenarios. To overcome the limited accuracy and accumulated drift of conventional GPS-based single-sensor localization, this paper proposes a LiDAR–IMU–GPS-aided navigation method that combines a tightly coupled front-end and a loosely coupled back-end. The front-end employs an improved Lie-group-based UKF-SLAM framework to explicitly handle the nonlinearities of rotational motion, thereby improving the stability of local pose estimation. The back-end integrates GPS absolute constraints, loop closure detection, and point cloud registration via pose graph optimization, which effectively suppresses long-term accumulated drift. The framework achieves accurate and robust localization for UAV power inspection. Experiments on public benchmark datasets and real-world power inspection scenarios demonstrate the effectiveness of the proposed method. On the MH_02_easy sequence, the absolute trajectory error is reduced from 0.521 m to 0.170 m compared with ROVIO, while in a real inspection sequence the cumulative error is reduced by more than 99% after back-end optimization. Moreover, the system maintains stable navigation under GPS-degraded conditions, indicating strong robustness and practical applicability. Full article

Detecting small insulator defects in unmanned aerial vehicle (UAV) imagery remains challenging due to low resolution, complex backgrounds and scale variation, which degrade the performance of existing detectors. This study aims to develop a highly efficient and accurate model for real-time insulator defect inspection on resource-constrained UAV platforms. This paper proposes POLD-YOLO, a novel lightweight object detector based on YOLO11. The key innovations include: (1) A backbone enhanced by a PoolingFormer module and Channel-wise Gated Linear Units (CGLUs) to boost feature extraction efficiency; (2) An Omni-Dimensional Adaptive Downsampling (OD-ADown) module for multi-scale feature extraction with low complexity; (3) A Lightweight Shared Convolutional Detection Head (LSCD-Head) to minimize the number of parameters; (4) A Focaler-MPDIoU loss function to improve bounding box regression. Extensive experiments conducted on a self-built UAV insulator defect dataset show that POLD-YOLO achieves a state-of-the-art mAP@0.5 of 92.4%, outperforming YOLOv5n, YOLOv8n, YOLOv10n, and YOLO11n by 3.6%, 1.6%, 1.4%, and 1.6%, respectively. Notably, it attains this superior accuracy with only 1.55 million parameters and 3.8 GFLOPs. POLD-YOLO establishes a new Pareto front for accuracy-efficiency for onboard defect detection. It demonstrates great potential for automated power line inspection and can be extended to other real-time aerial vision tasks. Full article

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments. Full article