Search Results (268)

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

This paper focuses on a class of 5-degrees-of-freedom (5-DOF) aerial platforms, particularly the Passively Articulated Dual UAVs (PADUAVs). These platforms have the potential to achieve omnidirectional motion, as their joints are free from position constraints. However, PADUAVs encounter singularity issues in certain configurations. To address this challenge, we propose a novel singularity-avoidance control strategy. The approach begins with an analysis of the flat outputs of the 5-DOF aerial system. Based on this analysis, we design a careful allocation strategy that maps position control to attitude control via the flat outputs. A variable intermediate attitude is introduced to ensure that this allocation remains singularity-free across all configurations of the 5-DOF aerial vehicle. The stability of the proposed controller is rigorously proven. We then apply the proposed control method to the PADUAV platform, providing detailed modeling, analysis, and dynamic decoupling of the system. Due to the presence of additional sub-vehicle dynamics in the PADUAV, an auxiliary attitude allocation module is also developed. The proposed position and attitude control allocation strategies enable the controller to maintain singularity-free stability across all configurations. Finally, we implement a 5-DOF tracking control strategy specifically tailored for the PADUAV. Numerical simulations validate the effectiveness of the proposed approach, demonstrating its robustness and reliability in aerial manipulation tasks. Full article

Enhancing quadrotor control to improve both precision and responsiveness is essential for expanding their deployment in complex and dynamic environments. These aerial vehicles are widely used in applications, such as aerial mapping, delivery, disaster response, and defense, where maintaining stability and accuracy is critical, especially under external disturbances like wind. This paper makes three key contributions. First, it develops a nonlinear mathematical model of a quadrotor and designs a backstepping controller for trajectory tracking. Second, the controller’s parameters are optimized using three nature-inspired algorithms: Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and the Flower Pollination Algorithm (FPA), enabling performance comparisons in terms of their tracking precision and control effort. Third, the robustness of the best-performing optimized controller is evaluated by applying wind disturbances at the simulation level, modeled as external forces acting along the x-axis and summed with the control input. The simulation results highlight the comparative efficiency of the optimization methods and demonstrate the robustness of the selected controller in maintaining stability and accuracy under wind-induced perturbations. Full article

A mobile manipulation robot combines the navigation capability of unmanned ground vehicles and manipulation advantage of robotic arms. However, the development of a mobile manipulation robot is challenging due to the integration requirement of numerous heterogeneous subsystems. In this paper, we propose a multifunctional mobile manipulation robot by integrating perception, mapping, navigation, object detection, and grasping functions into a seamless workflow to conduct search-and-fetch tasks. To realize navigation and collision avoidance in complex environments, a new hierarchical motion planning strategy is proposed by fusing global and local planners. Control Lyapunov Function (CLF) and Control Barrier Function (CBF) are employed to realize path tracking and to guarantee safety during navigation. The convolutional neural network and the gripper’s kinematic constraints are adopted to construct a learning-optimization hybrid grasping algorithm to generate precise grasping poses. The efficiency of the developed mobile manipulation robot is demonstrated by performing indoor fetching experiments, showcasing its promising capabilities in real-world applications. Full article

This paper proposes a general workflow for collecting and analyzing open data from Bicycle Sharing Systems (BSSs) that was developed using data from the Valenbisi system, operated in Valencia by the French company JCDecaux; however, the stages of the proposed workflow are service-independent and can be applied broadly. Cycling has become an increasingly popular mode of transportation, leading to the emergence of BSSs in modern cities. Parallel to this, Smart City solutions have been implemented using Internet of Things (IoT) technologies, such as embedded sensors and GPS-based communication systems, which have become essential to everyday life. When public transportation services or bicycle sharing systems are used, real-time information about the services is provided to customers, including vehicle tracking based on GPS technology and the availability of bikes via sensors installed at bike rental stations. The bike stations were examined from two different perspectives: first, their daily usage, and second, the types of facilities located in their surroundings. Based on these two approaches, the overlap between the clustering results was analyzed—specifically, the similarity in how stations could be grouped and the correlation between their usage and locations. To enhance the raw data retrieved from the service provider’s official API, the stations were annotated based on OpenStreetMap and Overpass API data. Data visualization was created using Tableau from Salesforce. Based on the results, an agreement of 62% was found between the results of the two different clustering approaches. Full article

Various platforms, such as Google Maps, provide information about the services of public transport companies worldwide. Operators publish the planned (static) timetable using the General Transit Feed Specification (GTFS) format, while the GTFS Realtime (GTFS-RT) specification provides live (dynamic) information about the services. In this paper, we present our dataset that was built by retrieving and pre-processing the data sources of the open data platform of BKK Futár, hosted by the Centre for Budapest Transport Company (BKK). The paper contains a well-detailed description of our methods for retrieving and pre-processing the data among statistical features. The dataset covers a one-year period in which the data collection mechanism used for realtime data was continuously improved from collecting only live vehicle positions to covering all the available feeds and increasing the query frequency. We merged the static data with the vehicle positions to filter them, yielding a clean set of tracked trips. As a result, more than 90% of the daily planned trips could be reconstructed from the responses. We provide an interactive web-based visualization for the analysis of the GTFS schedule’s, and the GTFS-RT Vehicle Positions feed’s, geospatial features. The dataset and also our methodology can serve as input for various research studies to investigate the common characteristics of delays and disruptions or predict real departure times based on the current vehicle positions and historical data. Full article

Autonomous race driving has emerged as a challenging domain for reinforcement learning (RL) applications, requiring high-speed control while adhering to strict safety constraints. Existing RL-based racing methods often struggle to balance performance and safety, with limited adaptability in dynamic racing scenarios with multiple opponent vehicles. The high-dimensional state space and strict safety constraints pose significant challenges for efficient learning. To address these challenges, this paper proposes an integrated RL framework that combines three novel components: (1) a state mapping mechanism that dynamically transforms raw track observations into a consistent representation space; (2) an action mapping technique that rigorously enforces physical traction constraints; and (3) a safe exploration guidance method that combines conservative controllers with RL policies, significantly reducing off-track incidents during training. Extensive experiments conducted in our simulation environment with four test tracks demonstrate the effectiveness of our approach. In time trial scenarios, our method improves lap times by 12–26% and increases the training completion rate from 33.1% to 78.7%. In competitive racing, it achieves a 46–51% higher average speed compared to baseline methods. These results validate the framework’s ability to achieve both high performance and safety in autonomous racing tasks. Full article

A tightly coupled hybrid monocular visual SLAM system for unmanned underwater vehicles (UUVs) is introduced in this paper. Specifically, we propose a robust three-step hybrid tracking strategy. The feature-based method initially provides a rough pose estimate, then the direct method refines it, and finally, the refined results are used to reproject map points to improve the number of features tracked and stability. Furthermore, a tightly coupled visual hybrid optimization method is presented to address the inaccuracy of the back-end pose optimization. The selection of features for stable tracking is achieved through the integration of two distinct residuals: geometric reprojection error and photometric error. The efficacy of the proposed system is demonstrated through quantitative and qualitative analyses in both artificial and natural underwater environments, demonstrating excellent stable tracking and accurate localization results. Full article

Due to the complexity of the unstructured environment and the high-level requirement of smoothness when a tracked transportation vehicle is traveling, making the vehicle travel as safely and smoothly as when a skilled operator is maneuvering the vehicle is a critical issue worth studying. To this end, this study proposes a trajectory-planning method for human-like maneuvering. First, several field equipment operators are invited to manipulate the model vehicle for obstacle avoidance driving in an outdoor scene with densely distributed obstacles, and the manipulation data are collected. Then, in terms of the lateral displacement, by comparing the similarity between the data as well as the curvature change degree, the data with better smoothness are screened for processing, and a dataset of human manipulation behaviors is established for the training and testing of the trajectory-planning network. Then, using the dynamic parameters as constraints, a two-stage planning approach utilizes a modified deep network model to map trajectory points at multiple future time steps through the relationship between the spatial environment and the time series. Finally, after the experimental test and analysis with multiple methods, the root-mean-square-error and the mean-average-error indexes between the planned trajectory and the actual trajectory, as well as the trajectory-fitting situation, reveal that this study’s method is capable of planning long-step trajectory points in line with human manipulation habits, and the standard deviation of the angular acceleration and the curvature of the planned trajectory show that the trajectory planned using this study’s method has a satisfactory smoothness. Full article

This paper proposes a dynamic weight model predictive control (DWMPC) strategy for adaptive cruise control (ACC) in pure electric vehicles, aiming to enhance robustness, energy efficiency, and ride comfort under complex traffic conditions. Unlike conventional MPC with static weights, the proposed method integrates a fuzzy inference system that evaluates driving urgency based on real-time spacing and velocity errors. The resulting emergency coefficient is mapped through a nonlinear function to dynamically adjust the velocity tracking weight in the MPC cost function. Additionally, a four-mode coordination mechanism adaptively modifies acceleration and jerk penalties according to risk levels, enabling balanced responses between safety and comfort. A composite performance evaluation index (PEI) is formulated to quantitatively assess energy consumption, ride comfort, spacing accuracy, and emergency responsiveness. Simulation results under WLTC and typical urban driving scenarios demonstrate that DWMPC outperforms fixed-weight MPC and PI controllers, reducing energy consumption by 6.5%, jerk by 42.9%, and response time by 41.8% while improving coordination in speed tracking, inter-vehicle distance regulation, and energy-efficient control. Full article

With the growing deployment of autonomous driving and unmanned systems in road environments, efficiently and accurately performing environmental perception and map construction has become a significant challenge for SLAM systems. In this paper, we propose an innovative SLAM framework comprising a frontend tracking network called SGF-net and a backend filtering mechanism, namely Semantic Gaussian Filter. This framework effectively suppresses dynamic objects by integrating feature point detection and semantic segmentation networks, filtering out Gaussian point clouds that degrade mapping quality, thus enhancing system performance in complex outdoor scenarios. The inference speed of SGF-net has been improved by over 23% compared to non-fused networks. Specifically, we introduce SGF-SLAM (Semantic Gaussian Filter SLAM), a dynamic mapping framework that shields dynamic objects undergoing temporal changes through multi-view geometry and semantic segmentation, ensuring both accuracy and stability in mapping results. Compared with existing methods, our approach can efficiently eliminate pedestrians and vehicles on the street, restoring an unobstructed road environment. Furthermore, we present a map update function, which is aimed at updating areas occluded by dynamic objects by using semantic information. Experiments demonstrate that the proposed method significantly enhances the reliability and adaptability of SLAM systems in road environments. Full article

To address the challenges of low pesticide utilization, insufficient automation, and health risks in orchard plant protection, we developed an autonomous spraying vehicle using ArduPilot firmware and a robot operating system (ROS). The system tackles orchard navigation hurdles, including global navigation satellite system (GNSS) signal obstruction, light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) error accumulation, and lighting-limited visual positioning. A key innovation is the integration of an extended Kalman filter (EKF) to dynamically fuse T265 visual odometry, inertial measurement unit (IMU), and GPS data, overcoming single-sensor limitations and enhancing positioning robustness in complex environments. Additionally, the study optimizes PID controller derivative parameters for tracked chassis, improving acceleration/deceleration control smoothness. The system, composed of Pixhawk 4, Raspberry Pi 4B, Silan S2L LIDAR, T265 visual odometry, and a Quectel EC200A 4G module, enables autonomous path planning, real-time obstacle avoidance, and multi-mission navigation. Indoor/outdoor tests and field experiments in Sun Village Orchard validated its autonomous cruising and obstacle avoidance capabilities under real-world orchard conditions, demonstrating feasibility for intelligent plant protection. 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

Autonomous navigation for micro electric vehicles (micro EVs) operating in semi-structured environments—such as university campuses and industrial parks—requires solutions that are cost-effective, low in complexity, and robust. Traditional autonomous systems often rely on high-definition maps, multi-sensor fusion, or vision-based SLAM, which demand expensive sensors and high computational power. These approaches are often impractical for micro EVs with limited onboard resources. To address this gap, a real-world autonomous navigation system is presented, combining RTK-GNSS and 2D LiDAR with a real-time trajectory scoring algorithm. This configuration enables accurate path following and obstacle avoidance without relying on complex mapping or multi-sensor fusion. This study presents the development and experimental validation of a low-speed autonomous navigation system for a micro electric vehicle based on GNSS-RTK localization and real-time obstacle avoidance. The research achieved the following three primary objectives: (1) the development of a low-level control system for steering, acceleration, and braking; (2) the design of a high-level navigation controller for autonomous path following using GNSS data; and (3) the implementation of real-time obstacle avoidance capabilities. The system employs a scored predicted trajectory algorithm that simultaneously optimizes path-following accuracy and obstacle evasion. A Toyota COMS micro EV was modified for autonomous operation and tested on a closed-loop campus track. Experimental results demonstrated an average lateral deviation of 0.07 m at 10 km/h and 0.12 m at 15 km/h, with heading deviations of approximately 3° and 4°, respectively. Obstacle avoidance tests showed safe maneuvering with a minimum clearance of 1.2 m from obstacles, as configured. The system proved robust against minor GNSS signal degradation, maintaining precise navigation without reliance on complex map building or inertial sensing. The results confirm that GNSS-RTK-based navigation combined with minimal sensing provides an effective and practical solution for autonomous driving in semi-structured environments. Full article