Search Results (343)

The seamless integration of real-time operational technology (OT) with big data architectures remains a critical bottleneck in developing robust robotic Digital Twins. Furthermore, evaluating stochastic motion planners strictly within pristine simulations obscures vital real-world challenges such as sensor noise, communication latency, and mechanical stress. This study presents a high-throughput, real-time Hardware-in-the-Loop (HIL) pipeline integrating ROS 2, Apache Kafka, and Functional Mock-up Units (FMUs). Using a UR10e manipulator in a constrained industrial environment, we conducted extensive physical benchmarking of 11 Open Motion Planning Library (OMPL) algorithms across 10 repetitions, generating a comprehensive dataset of 785,192 samples. The proposed IT/OT architecture achieved deterministic millisecond-level synchronization, bounding end-to-end communication latency between 0.09 and 15.51 ms. Physical executions revealed a macroscopic “topological divergence” between simulation and reality, with spatial deviations peaking at 457.65 mm due to real-world point-cloud noise. While algorithms like EST and KPIECE demonstrated optimal geometric efficiency (e.g., a mean path length of 14.57 m) and hardware-friendly dynamics, traditional planners like RRT generated severe inertial spikes of up to 100 N, demonstrating substantial unsuitability for continuous industrial deployment. The primary contribution is a methodologically novel, rigorously validated big data pipeline and the release of an open-source, 50 Hz multimodal dataset (spatial, temporal, and dynamic forces), bridging the sim-to-real gap and providing a foundational benchmark for future data-driven robotic applications. Full article

Facility agricultural robots operating in greenhouse environments often encounter narrow passages, dense obstacle distributions, and frequent path-direction changes, which increase the difficulty of achieving efficient and smooth autonomous navigation. Conventional A* algorithms usually suffer from redundant node expansion, dense turning-point distributions, and insufficient path continuity under such constrained conditions. To address these issues, this study proposes an improved A*-based path-planning framework that integrates adaptive heuristic weighting, dynamic corner correction, and Bézier-curve-based path smoothing. Rather than introducing an entirely new planning paradigm, the proposed method coordinates several existing optimization strategies within a unified framework to improve search efficiency, path regularity, and path continuity for facility agricultural scenarios. The adaptive heuristic weighting strategy dynamically adjusts the contribution of the heuristic term according to the relative distance between the current node and the target node, thereby improving global search guidance while reducing unnecessary exploration. Dynamic corner correction is introduced to suppress zigzag path structures and reduce redundant turning nodes in obstacle-dense regions, while Bézier-curve-based smoothing is employed to improve path continuity and compatibility with the kinematic characteristics of agricultural mobile robots. Simulation experiments were conducted on grid maps and greenhouse-like environments with different obstacle distributions, and comparative evaluations were performed against Dijkstra, RRT, and conventional A* algorithms. Under representative simulation scenarios, the proposed framework reduced the number of turning points by up to 53.7% and decreased computation time by approximately 19.4% compared with the conventional A* algorithm, based on the average results of repeated trials under identical conditions. In addition, physical platform experiments on a ROS2-based agricultural robot demonstrated that the planned trajectories maintained relatively stable navigation performance and smoother directional transitions in constrained greenhouse-like environments. The results indicate that the proposed framework achieves a more balanced trade-off between computational efficiency, path compactness, and path smoothness than the benchmark methods considered in this study. Nevertheless, the current validation remains limited to structured or semi-structured greenhouse environments under static obstacle conditions. Future work will focus on improving adaptability to dynamic agricultural scenarios and integrating the framework with real-time perception and motion-control systems for practical greenhouse deployment. Full article

Accurate detection and reliable grasping of small bolts are essential for intelligent manufacturing and automated assembly. However, this remains a challenge due to the small size, slender geometry, and metallic reflective surfaces of bolts. In this paper, we propose a vision-guided robotic bolt handling framework that integrates lightweight object detection, optimization-oriented grasp execution, and collision-aware trajectory planning. The lightweight YOLOv8n-BoltLite detector, improved with E-C2f, LCA, SA-PAN, and WD-IoU loss, enhances localization accuracy and feature representation for small and slender bolts. A robotic grasping framework is designed to transform detection results into executable robotic actions through 3D pose estimation, mid-shank grasp point generation, and optimization-oriented execution formulation. Additionally, a five-segment trajectory planning strategy ensures safe and efficient robot motion. Experimental results show that YOLOv8n-BoltLite achieves a five-run average mAP of 99.64 ± 0.05% with 198 FPS, and 3.02 M parameters. On an additional challenging external test set involving illumination variation, clutter, partial occlusion, reflection, and clustered bolts, the proposed detector achieves 94.62 ± 0.18%, outperforming recent lightweight detectors under the same training protocol. Robotic experiments involving 1000 controlled grasping trials and 300 multi-target grasping attempts demonstrate a controlled-condition success rate of 97.0% and improved target-selection reliability in multi-bolt scenes. These results suggest that the proposed framework offers a practical and efficient solution for automated bolt handling in intelligent manufacturing environments. Full article

This paper focuses on the development of a mobile robot-based security surveillance and target-tracking application that combines image-processing algorithms with the Navigation Stack in the robot operating system (ROS). The proposed approach integrates a 3D camera with the MobileNet-SSD object detection model to estimate the target’s three-dimensional spatial coordinates in real time. These coordinates are continuously transmitted to the ROS Navigation Stack as dynamic goal points, enabling the robot to perform path planning and target-following while maintaining a predefined safety distance and avoiding obstacles. The proposed solution has been validated on a real differentially driven wheeled mobile robot. Experimental results demonstrate smooth and stable robot motion, accurate maintenance of the desired following distance, and reliable static obstacle avoidance while continuously tracking the target. These outcomes confirm the effectiveness and robustness of the integrated system for vision-based navigation tasks in indoor environments. Full article

Autonomous navigation on unknown uneven terrain remains a critical challenge for unmanned ground vehicle (UGV) deployed in unstructured environments such as disaster relief, wilderness exploration, and off-road logistics. Existing motion planning methods for such environments suffer from three key limitations: under-utilization of the solution space due to discretized terrain assessment, difficulty in transforming complex terrain safety constraints into optimization-compatible forms, and the inherent trade-off between environmental modeling accuracy and real-time performance. This paper presents a hierarchical motion planning framework that enables safe and fast navigation of UGV on unknown uneven terrain. We first construct a traversability map based on terrain slope, roughness, and sparsity extracted from ground point cloud clusters. Non-traversable points are then transformed via spherical inversion and inverse mapping to generate terrain safety corridors composed of a series of convex polygons. The geometric containment relationship between the vehicle’s convex hull and the corridor is reformulated as continuously differentiable Control Barrier Function (CBF) constraints to ensure driving safety. The front-end employs a kinodynamic Hybrid A* algorithm with a traversability-aware node pruning strategy, while the back-end trajectory optimization embeds the CBF constraints as hard constraints within the optimization loop to guarantee forward invariance of the safety set under the linearized dynamics. The proposed framework achieves full-shape collision avoidance without sacrificing the solution space, while maintaining real-time performance for autonomous navigation on complex terrain. Full article

This paper studies an inspection path-planning task for cross-domain platforms, in which a Reeds–Shepp unmanned surface vehicle (USV) must plan the shortest curvature-constrained path from the starting configuration (e.g., the USV depot) to the intermediate inspection point and then to the terminal configuration (e.g., the technical facility). The problem emerges from cooperative inspection applications and can be formulated as a three-point Reeds–Shepp problem (3PRSP), where the heading at the intermediate point needs to be optimized. To the best of our knowledge, neither closed-form nor optimization-based solutions exist for this problem. We propose an approximation-and-optimization-based approach that approximates the trigonometric constraints arising from the Reeds–Shepp forward–backward motion kinematics using piecewise-linear formulations, enabling the resulting model to be effectively solved by mathematical programming solvers. Extensive simulations and comparisons with the three-point Dubins approach and related methods demonstrate the accuracy and computational efficiency of the proposed method. Full article

Home indoor environments are characterized by alternating open spaces and obstacle-cluttered regions, which pose critical challenges to the autonomous navigation of home service robots. Existing hybrid path planning algorithms generally suffer from three core limitations: low global search efficiency, weak global-local planning coordination, and poor dynamic scene adaptability. To tackle these issues, this paper presents a novel hierarchical path planning framework combining an enhanced Jump Point Search (JPS) and a fuzzy-optimized Dynamic Window Approach (DWA). In the global planning layer, an adaptive Octile heuristic JPS based on local obstacle density is designed to reduce redundant node expansion and accelerate global path search, with a bounded suboptimality guarantee. To bridge global and local planning, a look-ahead distance-based dynamic waypoint selection strategy is developed to match the optimal waypoint in real time according to the robot’s motion state and environmental complexity, enabling seamless coordination between global path guidance and local trajectory generation. In the local planning layer, a fuzzy logic controller is introduced to dynamically tune the weights of the DWA trajectory evaluation function, which significantly improves the robot’s dynamic obstacle avoidance capability and motion smoothness. Comparative simulation experiments verify that the proposed method not only outperforms the conventional hybrid path planning algorithm, reducing expanded nodes by 68.09% and global planning time by 52.94%, while improving dynamic obstacle avoidance success rate by 31.43% and overall navigation efficiency by 23.95%, it also achieves better comprehensive navigation performance than the widely adopted PSO-DWA comparison algorithm. The proposed framework shows superior comprehensive performance and is well suited for the indoor autonomous navigation of home service robots. Full article

Accidents and vehicle damage caused by irregular road surfaces, such as potholes and cracks, remain a significant challenge in autonomous driving, particularly in terms of safety and trajectory reliability. Existing approaches often treat perception and control as separate processes, limiting their ability to respond effectively to road-surface anomalies in real time. In the proposed work, a unified framework for road-surface anomaly-aware control that integrates 3D point cloud perception with a Linear Parameter-Varying Model Predictive Controller (LPV-MPC) is presented. The proposed approach utilizes onboard sensors to capture detailed geometric information of the road surface and detect anomalies relevant to vehicle motion. The detected anomalies are represented in a control-oriented form and incorporated into the LPV-MPC framework, enabling adaptive trajectory planning and speed regulation. This integration allows the controller to proactively adjust vehicle behavior in response to surface irregularities, improving both safety and tracking performance. Experimental results demonstrate that the proposed method enhances robustness against road disturbances and improves trajectory tracking compared to conventional control approaches without anomaly awareness. These results highlight the effectiveness of tightly coupling perception and control for reliable autonomous driving in real-world conditions. Full article

Land subsidence is an important challenge faced by coastal cities under rapid urban development. This study focuses on the Haikou–Laocheng area and conducts time-series monitoring of land subsidence using PS-InSAR and SBAS-InSAR based on 42 Sentinel-1 SAR scenes acquired from April 2023 to April 2025, thereby deriving the spatial distribution of cumulative subsidence rates and the evolution patterns of multi-temporal cumulative subsidence. Because only ascending-orbit Sentinel-1 data were used, the reported deformation values are vertical-projected estimates converted from line-of-sight (LOS) displacement under the assumption that horizontal motion is negligible. The reliability of the monitoring results is evaluated through cross-validation between the two methods, assessing their inter-method consistency. The results indicate that the study area is dominated by slight subsidence, with vertical-projected subsidence rates mainly ranging from −6 to 3.7 mm/y, while a few uplift points are locally observed, forming an overall “stable with localized anomalies” deformation pattern. PS-InSAR and SBAS-InSAR show good consistency in overall trends, and both identify a pronounced subsidence bowl in the southwestern part of the study area, where the peak vertical-projected subsidence rates reach −25.1 mm/y and −35.1 mm/y, respectively, with outward banded attenuation. The results suggest that land subsidence in the study area is influenced by both natural factors and human activities. Specifically, rainfall shows a non-synchronous, stage-wise modulation relationship with subsidence evolution, and most high-subsidence zones are distributed in impervious surfaces such as built-up land and transportation corridors, or in low-elevation areas such as farmland. In terms of geological factors, thick, highly compressible soft soils are the primary geological control on the continued development of subsidence. These findings can provide scientific references for the prevention and control of abnormal subsidence and for urban planning and development in the Haikou–Laocheng area. The strengthened discussion clarifies the research gap, planning significance, and limitations of applying dual time-series InSAR in a data-scarce tropical coastal soft-soil setting. Full article

This paper presents a software application developed in Python (v3.9) for obstacle avoidance trajectory planning in the RoboDK (v6.0) virtual environment. The proposed method automatically scans the virtual station, identifies obstacles, discretizes the workspace into a three-dimensional free-space-graph (FSG) and searches for candidate routes between start and finish points. Each route is then verified at the robot level by inverse kinematics and collision control, and the validated solutions can be transformed into preview curves, intermediate points and motion programs executable by the robot. The study includes an initial test scenario performed with the ABB IRB 6650-125/3.2 robot and randomly generated obstacles, followed by a series of benchmark tests performed in different virtual scenarios and with four different robot models. In the comparative tests, the proposed method was evaluated together with a rapidly exploring random tree (RRT) reference planner and the native probabilistic roadmap (PRM) planner, embedded RoboDK. The final scenario included a robotic cell with realistic objects. The results show that the application can identify valid executable routes and, in some cases, several alternative variants for the same pair of target points. Overall, the benchmark suggests that the analyzed methods have different strengths and should be viewed as complementary solutions. In the tested scenarios, RRT was the method with the lowest computational times, while the proposed method offered the possibility of generating several alternative routes. At the current stage, the application can thus be used as an offline programming tool but also as a research and analysis tool for planning robotic trajectories in the presence of static obstacles. Full article

The conservation and restoration of architectural heritage face dual challenges from natural erosion and human interference, necessitating the adoption of efficient and non-contact digital technologies to achieve sustainable preservation. Virtual reality (VR) technology, with its advantages of immersion, interactivity, and visualization, provides a novel technological pathway for digital documentation, conservation decision-making, and public presentation of architectural heritage. Taking the Fuliang Red Pagoda in Jingdezhen, Jiangxi Province, as the research object, this study constructs a high-precision digital reconstruction and VR interactive application workflow based on the integration of terrestrial laser scanning and close-range photogrammetry. Through point cloud denoising, Iterative Closest Point (ICP) registration, and Poisson surface reconstruction algorithms, a refined three-dimensional model of the pagoda is achieved, and an immersive VR system is developed with functions including component information query, virtual restoration scheme switching, and interactive exploration. The results demonstrate that this technical workflow not only enables non-contact digital archiving of the Fuliang Red Pagoda but also provides a visual decision-support tool for conservation interventions. Under full-scene operation, the system achieves an average rendering frame rate of 92 FPS and maintains motion-to-photon latency below 20 ms, ensuring good real-time performance and interaction stability. The findings indicate that VR-based digital technologies can enhance the scientific rigor of conservation planning and promote public engagement while adhering to the principles of authenticity and minimum intervention. This study provides a replicable technical pathway and practical reference for high-precision digital reconstruction and sustainable conservation of historic buildings. Full article

Safety-oriented UAV motion planning relies on distance-to-obstacle fields and their gradients, yet onboard mapping is typically limited to bounded local distance updates. Consequently, optimization may stall outside the updated band due to missing gradients, while enlarging the update range substantially increases computational cost. Our key insight is that motion-planning locality implies only a small subset of obstacles governs local trajectory refinement. We term this subset points of interest (POIs). Motivated by this observation, we develop a locality-aware sequential motion planning framework with a POI-driven feedback mechanism that continuously identifies and augments these trajectory-relevant obstacles during search and optimization. The mechanism tightly couples mapping, search, and optimization and enables safe trajectory refinement without requiring global distance updates. The framework adopts a heuristic mapping strategy that combines a long-term occupancy grid with bounded incremental distance updates and a POI-based short-term k-d tree, enabling efficient nearest-neighbor queries and gradient proxies beyond the update band. The search process generates a dynamically feasible initial trajectory in the long-term map while collecting POIs, which are then used to construct the short-term component. The trajectory is subsequently refined through iterative optimization loops, where newly exposed closest obstacles are incorporated into the POI set and the short-term map is updated until convergence. Safety is enforced through conservative collision checking against the inflated long-term occupancy map. Simulations in building and forest environments show that 99.7% of trials converge within two refinements in sparse scenes and none exceed four overall. Compared with FastPlanner and EgoPlanner, the proposed method achieves consistently larger obstacle clearances. Onboard experiments further validate its practicality under real sensing and computational constraints. Full article

This study applies InSAR time series analysis derived from Sentinel-1 satellite data (ascending and descending orbits) processed with ISCE2 and StaMPS (v.4.1) software to evaluate deformation dynamics in three landfill types near Gijón, Spain. Initially, the data were validated against the European Ground Motion Service (EGMS) dataset using a set of Persistent Scatterers (PS) in an urban control area through two analytical approaches (EGMS protocol and PSDefoPAT(2023)). The results showed high consistency, with 82–85% of points classified as highly reliable. Subsequently, this control group was compared with PS from each landfill type (active sanitary, operational inert, and closed inert). Significant deformation differences were found in each landfill type: the active sanitary landfill exhibited distinct anomalies depending on orbit, with strong residual variance instability detected (p < 0.003) in both. Operational inert landfills showed significant anomalies (p < 0.001) in both orbits with variable stability, while closed inert landfills displayed strong stability (p > 0.7) and variable anomalies. These results confirm the efficacy of InSAR approaches for detecting active landfill zones to aid in locating illegal or unauthorized dumping sites and to direct in situ inspection planning. Full article

At present, commonly used vegetable pot seedling planters can be divided into two categories: one has a complex structure and high manufacturing cost, and the other has a simple structure but poor planting quality. In order to solve this problem, an open-hinge four-bar-mechanism planting manipulator is designed, which has many advantages, such as a simple structure, strong force transfer performance, and the ability to achieve complex trajectory curves. The physical characteristics of pot seedlings are measured; this provides a basis for the structural and dimensional design of the planter and the shape design of the duckbill. According to the analysis of the planting process, the design requirements of the planting mechanism are formulated. The motion path of the mechanism and the motion of each pair are planned and designed; a planetary gear train is used to restrain the rotating pair consisting of connecting rod 1 and connecting rod 2; a cam high pair mechanism is used to restrain the rotating pair consisting of connecting rod 2 and connecting rod 3; and a cam linkage mechanism is used to control the opening and closing action of the duckbill. Finally, a single-degree-of-freedom fully mechanical planting mechanism is designed. The experimental results show that the trajectory of the initial soil entry point of the planting mechanism is consistent with the design requirements and theoretical simulation results. In the transplanting experiment, the rate of qualified planting erectness was 94.79%, among which the rate of excellent planting erectness was 92.45%, and the mechanism has high reliability. The design of this mechanism offers a fully automatic pot seedling planting method, which can provide a reference for research on the full automation of transplanting equipment. Full article

In view of the bottleneck problems existing in the 3D ultrasonic testing of aircraft composite laminated structures—including heavy reliance on manual operation, resulting in low detection efficiency, and the inability of traditional robotic arms to adapt to the testing of complex curved surfaces due to their dependence on predefined fixed trajectories—this paper proposes an automated 3D ultrasonic testing method based on 3D vision guidance for robotic arms. Firstly, the proposed Yolo-Mask model is adopted to realize the visual recognition and segmentation of composite component regions, after which the segmentation results are mapped to the depth map and further converted into the surface point cloud of the material. Secondly, on the basis of point cloud preprocessing and trajectory point extraction, the automatic planning of the robotic arm’s scanning trajectory is achieved, which drives the robotic arm to perform precise motion and to synchronously collect spatial pose and ultrasonic testing data. Finally, 3D reconstruction is completed via a fusion algorithm, and 3D images of the material’s internal structures are generated. Experimental verification shows that the proposed method achieves a Segm-mAP of 97.4%, a detection speed of 11.7 fps, and a 3D imaging error of less than 0.1 mm, thereby realizing fully automated detection throughout the entire process. This research provides an effective solution for the non-destructive testing of aircraft composite structures. Full article