Search Results (408)

The detection of coils in reservoir areas is part of the environmental perception technology of unmanned cranes. In order to improve the perception ability of unmanned cranes to include environmental information in reservoir areas, a method of automatic detection of coils based on two-dimensional LiDAR dynamic scanning is proposed, which realizes the detection of the position and attitude of coils in reservoir areas. This algorithm realizes map reconstruction of 3D point cloud by fusing LiDAR point cloud data and the motion position information of intelligent cranes. Additionally, a processing method based on histogram statistical analysis and 3D normal curvature estimation is proposed to solve the problem of over-segmentation and under-segmentation in 3D point cloud segmentation. Finally, for segmented point cloud clusters, coil models are fitted by the RANSAC method to identify their position and attitude. The accuracy, recall, and F1 score of the detection model are all higher than 0.91, indicating that the model has a good recognition effect. Full article

Thermoregulatory fanning behavior in honeybees is a vital indicator of colony health and environmental response. This study presents a novel dataset of 18,000 annotated video frames containing 57,597 instances capturing fanning behavior at the hive entrance across diverse conditions. Three state-of-the-art single-shot object detection models (YOLOv8, YOLO11, YOLO12) are evaluated using standard RGB input and two motion-enhanced encodings: Temporally Stacked Grayscale (TSG) and Temporally Encoded Motion (TEM). Results show that models incorporating temporal information via TSG and TEM significantly outperform RGB-only input, achieving up to 85% mAP@50 with real-time inference capability on high-performance GPUs. Deployment tests on the Jetson AGX Orin platform demonstrate feasibility for edge computing, though with accuracy–speed trade-offs in smaller models. This work advances real-time, non-invasive monitoring of hive health, with implications for precision apiculture and automated behavioral analysis. Full article

Trajectory prediction under multimodal information is critical for autonomous driving, necessitating the integration of dynamic vehicle states and static high-definition (HD) maps to model complex agent–scene interactions effectively. However, existing methods often employ static scene encodings and unstructured latent spaces, limiting their ability to capture evolving spatial contexts and produce diverse yet contextually coherent predictions. To tackle these challenges, we propose MS-SLV, a novel generative framework that introduces (1) a time-aware scene encoder that aligns HD map features with vehicle motion to capture evolving scene semantics and (2) a structured latent model that explicitly disentangles agent-specific intent and scene-level constraints. Additionally, we introduce an auxiliary lane prediction task to provide targeted supervision for scene understanding and improve latent variable learning. Our approach jointly predicts future trajectories and lane sequences, enabling more interpretable and scene-consistent forecasts. Extensive evaluations on the nuScenes dataset demonstrate the effectiveness of MS-SLV, achieving a 12.37% reduction in average displacement error and a 7.67% reduction in final displacement error over state-of-the-art methods. Moreover, MS-SLV significantly improves multi-modal prediction, reducing the top-5 Miss Rate (${\mathrm{MR}}_5$) and top-10 Miss Rate (${\mathrm{MR}}_{10}$) by 26% and 33%, respectively, and lowering the Off-Road Rate (ORR) by 3%, as compared with the strongest baseline in our evaluation. Full article

Traffic congestion has increased significantly in today’s rapidly urbanizing world, influencing people’s daily lives. Traffic signal control systems (TSCSs) play an important role in alleviating congestion by optimizing traffic light timings and improving road efficiency. Yet traditional TSCSs neglected pedestrians, cyclists, and other non-monitored road users, degrading traffic signal optimization (TSO). Therefore, this framework proposes a multi-object-based traffic flow analysis and intensity estimation model for efficient TSO using Upper Confidence Bound Multi-agent Reinforcement Learning Cubic Spline Fuzzy Logic (UCB-MRL-CSFL). Initially, the real-time traffic videos undergo frame conversion and redundant frame removal, followed by preprocessing. Then, the lanes are detected; further, the objects are detected using Temporal Context You Only Look Once (TC-YOLO). Now, the object counting in each lane is carried out using the Cumulative Vehicle Motion Kalman Filter (CVMKF), followed by queue detection using Vehicle Density Mapping (VDM). Next, the traffic flow is analyzed by Feature Variant Optical Flow (FVOF), followed by traffic intensity estimation. Now, based on the siren flashlight colors, emergency vehicles are separated. Lastly, UCB-MRL-CSFL optimizes the Traffic Signals (TSs) based on the separated emergency vehicle, pedestrian information, and traffic intensity. Therefore, the proposed framework outperforms the other conventional methodologies for TSO by considering pedestrians, cyclists, and so on, with higher computational efficiency (94.45%). Full article

The ability to autonomously generate up-to-date 3D models of robotic workcells is critical for advancing smart manufacturing, yet existing Next-Best-View (NBV) methods often rely on paradigms ill-suited for the fixed-base manipulators found in dynamic industrial environments. To address this gap, this paper proposes a novel NBV method for the complete exploration of a 6-DOF robotic arm’s workspace. Our approach integrates collision-based information gain metric, a potential field technique to generate candidate views from exploration frontiers, and a tunable fitness function to balance information gain with motion cost. The method was rigorously tested in three simulated scenarios and validated on a physical industrial robot. Results demonstrate that our approach successfully maps the majority of the workspace in all setups, with a balanced weighting strategy proving most effective for combining exploration speed and path efficiency, a finding confirmed in the real-world experiment. We conclude that our method provides a practical and robust solution for autonomous workspace mapping, offering a flexible, training-free approach that advances the state-of-the-art for on-demand 3D model generation in industrial robotics. Full article

Insect detection under real-field conditions remains a challenging task due to factors such as lighting variations and the small size of insects that often lack sufficient visual features for reliable identification by deep learning models. These limitations become especially pronounced in lightweight architectures, which, although efficient, struggle to capture fine-grained details under suboptimal conditions, such as variable lighting conditions, shadows, small object size and occlusion. To address this, we introduce the motion module, a lightweight component designed to enhance object detection by integrating motion information directly at the feature map level within the YOLOv8 backbone. Unlike methods that rely on frame differencing and require additional preprocessing steps, our approach operates on raw input and uses only two consecutive frames. Experimental evaluations demonstrate that incorporating the motion module leads to consistent performance improvements across key metrics. For instance, on the YOLOv8n model, the motion module yields gains of up to 5.11% in mAP50 and 7.83% in Recall, with only a small computational overhead. Moreover, under simulated illumination shifts using HSV transformations, our method exhibits robustness to these variations. These results highlight the potential of the motion module as a practical and effective tool for improving insect detection in dynamic and unpredictable field scenarios. Full article

Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations of existing denoising methods—including traditional adaptive filtering that relies on prior noise information, modal decomposition techniques that depend on empirical parameter optimization and are prone to modal aliasing, wavelet threshold functions that struggle to balance signal preservation with smoothness, and the high computational complexity of deep learning approaches—this paper proposes an ISSA-VMD-AWPTD denoising algorithm. This innovative approach integrates an improved sparrow search algorithm (ISSA), variational mode decomposition (VMD), and adaptive wavelet packet threshold denoising (AWPTD). The ISSA is enhanced through cubic chaotic mapping, butterfly optimization, and sine–cosine search strategies, targeting the minimization of the envelope entropy of modal components for adaptive optimization of VMD’s decomposition levels and penalty factors. A correlation coefficient-based selection mechanism is employed to separate target and mixed modes effectively, allowing for the efficient removal of noise components. Additionally, an exponential adaptive threshold function is introduced, combining wavelet packet node energy proportion analysis to achieve efficient signal reconstruction. By leveraging the rapid convergence property of ISSA (completing parameter optimization within five iterations), the computational load of traditional VMD is reduced while maintaining the denoising accuracy. Experimental results demonstrate that for a 200 Hz test signal, the proposed algorithm achieves a signal-to-noise ratio (SNR) of 24.47 dB, an improvement of 18.8% over the VMD method (20.63 dB), and a root-mean-square-error (RMSE) of 0.0023, a reduction of 69.3% compared to the VMD method (0.0075). The processing results for measured human blood flow signals achieve an SNR of 24.11 dB, a RMSE of 0.0023, and a correlation coefficient (R) of 0.92, all outperforming other algorithms, such as VMD and WPTD. This study effectively addresses issues related to parameter sensitivity and incomplete noise separation in traditional methods, providing a high-precision and low-complexity real-time signal processing solution for wearable devices. However, the parameter optimization still needs improvement when dealing with large datasets. Full article

The increasing deployment of unmanned aerial vehicles (UAVs) in complex urban environments necessitates efficient and reliable path planning algorithms. While traditional sampling-based methods such as Rapidly exploring Random Tree Star (RRT*) are widely adopted, their computational inefficiency and suboptimal path quality in intricate 3D spaces remain significant challenges. This study proposes a novel framework (MPN-RRT*) that integrates Motion Planning Networks (MPNet) with RRT* to enhance UAV navigation in 3D urban maps. A key innovation lies in reducing computational complexity through dimensionality reduction, where 3D urban terrains are sliced into 2D maze representations while preserving critical obstacle information. Transfer learning is applied to adapt a pre-trained MPNet model to the simplified maps, enabling intelligent sampling that guides RRT* toward promising regions and reduces redundant exploration. Extensive MATLAB simulations validate the framework’s efficacy across two distinct 3D environments: a sparse 200 × 200 × 200 map and a dense 800 × 800 × 200 map with no-fly zones. Compared to conventional RRT*, the MPN-RRT* achieves a 47.8% reduction in planning time (from 89.58 s to 46.77 s) and a 19.8% shorter path length (from 476.23 m to 381.76 m) in simpler environments, alongside smoother trajectories quantified by a 91.2% reduction in average acceleration (from 14.67 m/s² to 1.29 m/s²). In complex scenarios, the hybrid method maintains superior performance, reducing flight time by 14.2% and path length by 13.9% compared to RRT*. These results demonstrate that the integration of deep learning with sampling-based planning significantly enhances computational efficiency, path optimality, and smoothness, addressing critical limitations in UAV navigation for urban applications. The study underscores the potential of data-driven approaches to augment classical algorithms, providing a scalable solution for real-time autonomous systems operating in high-dimensional dynamic environments. Full article

Gait recognition, as a non-contact biometric technology, offers unique advantages in scenarios requiring long-distance identification without active cooperation from subjects. However, existing gait recognition methods predominantly rely on single-modal data, which demonstrates insufficient feature expression capabilities when confronted with complex factors in real-world environments, including viewpoint variations, clothing differences, occlusion problems, and illumination changes. This paper addresses these challenges by introducing a multi-modal gait recognition network based on channel shuffle regulation and spatial-frequency joint learning, which integrates two complementary modalities (silhouette data and heatmap data) to construct a more comprehensive gait representation. The channel shuffle-based feature selective regulation module achieves cross-channel information interaction and feature enhancement through channel grouping and feature shuffling strategies. This module divides input features along the channel dimension into multiple subspaces, which undergo channel-aware and spatial-aware processing to capture dependency relationships across different dimensions. Subsequently, channel shuffling operations facilitate information exchange between different semantic groups, achieving adaptive enhancement and optimization of features with relatively low parameter overhead. The spatial-frequency joint learning module maps spatiotemporal features to the spectral domain through fast Fourier transform, effectively capturing inherent periodic patterns and long-range dependencies in gait sequences. The global receptive field advantage of frequency domain processing enables the model to transcend local spatiotemporal constraints and capture global motion patterns. Concurrently, the spatial domain processing branch balances the contributions of frequency and spatial domain information through an adaptive weighting mechanism, maintaining computational efficiency while enhancing features. Experimental results demonstrate that the proposed GaitCSF model achieves significant performance improvements on mainstream datasets including GREW, Gait3D, and SUSTech1k, breaking through the performance bottlenecks of traditional methods. The implications of this research are significant for improving the performance and robustness of gait recognition systems when implemented in practical application scenarios. Full article

One of the most commonly performed environmental explorations is soil sampling to identify soil properties of agricultural fields, which can inform the farmer about the variable rate treatment of fertilisers in precision agriculture. However, traditional manual methods are slow, costly, and yield low spatial resolution. Deploying multiple robots with proximal sensors can address this challenge by parallelising the sampling process. Yet, multi-robot soil sampling is under-explored in the literature. This paper proposes an auction-based multi-robot task allocation that efficiently coordinates the sampling, coupled with a dynamic sampling strategy informed by Kriging variance from interpolation. This strategy aims to reduce the number of samples needed for accurate mapping by exploring and sampling areas that maximise information gained per sample. The key innovative contributions include (1) a novel Distance Over Variance (DOV) bid calculation for auction-based multi-robot task allocation, which incentivises sampling in high-uncertainty, nearby areas; (2) integration of the DOV bid calculation into the cheapest insertion heuristic for task queuing; and (3) thresholding of newly created tasks at locations with low Kriging variance to drop those unlikely to offer significant information gain. The proposed methods were evaluated through comparative simulated experiments using historical soil compaction data. Evaluation trials demonstrate the suitability of the DOV bid calculation combined with task dropping, resulting in substantial improvements in key performance metrics, including mapping accuracy. While the experiments were conducted in simulation, the system is compatible with ROS and the ‘move_base’ action client to allow real-world deployment. The results from these simulations indicate that the Kriging-variance-informed approach can be applied to the exploration and mapping of other soil properties (e.g., pH, soil organic carbon, etc.) and environmental data. Full article

In multi-vehicle traffic scenarios, achieving accurate environmental perception and motion trajectory tracking through LiDAR–camera fusion is critical for downstream vehicle planning and control tasks. To address the challenges of cross-modal feature interaction in LiDAR–image fusion and the low recognition efficiency/positioning accuracy of traffic participants in dense traffic flows, this study proposes an attention-based 3D object detection network integrating point cloud and image features. The algorithm adaptively fuses LiDAR geometric features and camera semantic features through channel-wise attention weighting, enhancing multi-modal feature representation by dynamically prioritizing informative channels. A center point detection architecture is further employed to regress 3D bounding boxes in bird’s-eye-view space, effectively resolving orientation ambiguities caused by sparse point distributions. Experimental validation on the nuScenes dataset demonstrates the model’s robustness in complex scenarios, achieving a mean Average Precision (mAP) of 64.5% and a 12.2% improvement over baseline methods. Real-vehicle deployment further confirms the fusion module’s effectiveness in enhancing detection stability under dynamic traffic conditions. Full article

We propose a method to reconstruct a 3D coordinate system and a vascular map for the operation of magnetic medical robots (MMRs) controlled by a magnetic navigation system (MNS) using two 2D X-ray images and four corners of an MNS. Utilizing the proposed method, we calculated the relative rotation angle of a C-arm considering its rotational precision error. We derived the position information and 3D coordinate system of an MNS workspace in which the magnetic fields are generated and controlled by an MNS. The proposed method can also be utilized to reconstruct vascular maps. Reconstructed vascular maps are in the 3D coordinate system of the C-arm and can be transformed into the 3D coordinate system of an MNS workspace to generate the magnetic flux density with the desired direction and magnitude at the position of the MMR. The proposed method allows us to remotely and precisely control the MMR inserted into the vessel by controlling the external magnetic field. The proposed method was validated through in vitro experiments with an MNS mock-up and a vascular jig. Finally, the proposed method was applied to in vivo experiments where the MMR was inserted into the superficial femoral artery of a mini pig to remotely control the motion of the MMR. This research will enable precise and effective control of MMRs in various medical procedures utilizing an MNS. Full article

Current substation inspection robots mainly use Lidar as a sensor for localization and map building. However, laser SLAM has the problem of localization error in scenes with similar and missing environmental structural features, and environmental maps built by laser SLAM provide more single-road information for inspection robot navigation, which is not conducive to the judgment of the road scene. For this reason, in this paper, 3D Lidar information and visual information are fused to create a SLAM algorithm applicable to substation inspection robots to solve the above laser SLAM localization error problem and improve the algorithm’s localization accuracy. First, in order to recover the scalability of monocular visual localization, the algorithm in this paper utilizes 3D Lidar information and visual information to calculate the true position of image feature points in space. Second, the laser position and visual position are utilized with interpolation to correct the point cloud distortion caused by the motion of the Lidar. Then, a position-adaptive selection algorithm is designed to use visual position instead of laser inter-frame position in some special regions to improve the robustness of the algorithm. Finally, a color laser point cloud map of the substation is constructed to provide more road environment information for the navigation of the inspection robot. The experimental results show that the localization accuracy and map-building effect of the VO-Lidar SLAM algorithm designed in this paper are better than the current laser SLAM algorithm and verify the applicability of the color laser point cloud map constructed by this algorithm in substation environments. Full article

In order to fully harness obstacle information in path planning and improve the coordination between global and local path planning, a novel mobile robot path planning method is proposed. The novelty of the proposed path planning strategy lies in its integration of obstacle gap characteristics into both global and local planning processes. Specifically, this method addresses the issues of low search efficiency, excessive redundant points, and poor path quality in the traditional A* algorithm for global path planning by extracting gap grids in the global grid map and incorporating their influence into the heuristic function, thereby guiding the search more effectively. The generated global path is further optimized at gap points to remove redundant nodes. For local path planning, which employs the Dynamic Window Approach (DWA) and often exhibits weak compatibility with global planning and a lack of smoothness through obstacle gaps, this method calculates feasible steering angles based on the distance between the robot and obstacles as well as gap attributes. Additionally, the geometric relationship between global and local paths is established using the Bernstein equation, generating segmented guidance control points for DWA. Simulation experiments demonstrate that the proposed algorithm significantly enhances path efficiency and obstacle avoidance capability in tight space environments, reducing path length by approximately 4.79% and motion time by approximately 15.22% compared to conventional algorithms. Full article

Video object detection involves identifying and localizing objects within video frames over time. However, challenges such as real-time processing requirements, motion blur, and the need for temporal consistency in video data make this task particularly demanding. This study proposes a novel hybrid model that integrates Neural Ordinary Differential Equations (Neural ODEs) with Convolutional Gated Recurrent Units (ConvGRU) to achieve continuous dynamics in object detection for video data. First, it leverages the continuous dynamics of Neural ODEs to define the hidden state transitions between observation points, enabling the model to naturally align with real-world time-based processes. Second, we present the FPN-Up module, which combines high-level semantic information with low-level spatial details to enhance the exploitation of multi-layer feature representations. Finally, we integrate a CBAM attention module into the detection head, enabling the model to emphasize the most salient input feature regions, thereby elevating detection precision while preserving the existing network structure. Evaluation on the KITTI object detection dataset reveals that our proposed model outperforms a vanilla video object detector by 2.8% in mAP while maintaining real-time processing capabilities. Full article