Search Results (421)

The increasing heterogeneity of wheeled mobile robot (WMR) architectures, including differential-drive, Ackermann, omnidirectional, and reconfigurable platforms, poses a major challenge for defining a unified, scalable kinematic representation. Most existing formulations are tailored to specific mechanical layouts, limiting analytical coherence, cross-platform interoperability, and the systematic reuse of modeling, odometry, and motion-related algorithms. This work introduces a generalized kinematic modeling framework that provides a mathematically consistent formulation applicable to arbitrary WMR configurations. Wheel–ground velocity relationships and non-holonomic constraints are expressed through a concise vector formulation that maps wheel motions to chassis velocities, ensuring consistency with established models while remaining independent of the underlying mechanical structure. A parameterized wheel descriptor encodes all relevant geometric and kinematic properties, enabling the modular assembly of complete robot models by aggregating wheel-level relations. The framework is evaluated through numerical simulations on four structurally distinct platforms: differential-drive, Ackermann, three-wheel omnidirectional (3,0), and 4WD. Results show that the proposed formulation accurately reproduces the expected kinematic behavior across these fundamentally different architectures and provides a coherent and consistent representation of their motion. The unified representation further provides a common kinematic backbone that is directly compatible with odometry, motion-control, and simulation pipelines, facilitating the systematic retargeting of algorithms across heterogeneous robot platforms without architecture-specific reformulation. Additional simulation studies under realistic physics-based conditions show that the proposed formulation preserves coherent kinematic behavior during complex trajectory execution and supports the explicit incorporation of geometric imperfections, such as wheel mounting misalignments, when such parameters are available. By consolidating traditionally separate derivations into a single coherent formulation, this work establishes a rigorous, scalable, and architecture-agnostic foundation for unified kinematic modeling of wheeled mobile robots, with particular relevance for modular, reconfigurable, and cross-architecture robotic systems. Full article

The use of robots in various industries has increased significantly in recent years, with mobile robots playing a central role in automation. Their applications range from service robotics and automated material handling to bomb disposal and planetary exploration. A rapidly growing area of mobile robotics involves coordinated groups of autonomous robots, commonly referred to as swarms. However, only a limited number of studies have addressed systems in which ropes or wires physically connect robots. Connecting multiple autonomous robotic vehicles with a tensioned wire can form a movable fence, enabling coordinated motion as a single dynamic entity. This paper presents a real-time control approach for the off-road motion of physically connected skid-steering robotic vehicles. A numerical-simulation-driven artificial neural network is employed as a surrogate model to estimate wheel–ground load distribution online, enabling stable steering control and accurate trajectory tracking on rough terrain while accounting for wire-induced coupling effects. Full article

This work presents the development of a differential-drive wheeled mobile robot educational prototype, manufactured using 3D additive techniques. The robot is powered by an embedded ARM-based computing system and uses open-source software. To validate the prototype, a trajectory-tracking task was successfully implemented. The aim of this contribution is to provide an easily replicable prototype for teaching automatic control and related engineering topics in academic settings. Full article

Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation. Full article

In recent years, while progress has been made in barrier-free design, the complete elimination of physical barriers such as uneven road surfaces and stairs remains difficult, and wheelchair passengers continue to face significant mobility constraints. This study aims to verify the effectiveness of a transformable quadruped wheelchair that can switch between two modes of movement: walking and wheeled travel. Specifically, reinforcement learning using Proximal Policy Optimization (PPO) was used to acquire walking strategies for uneven terrain and wheeled travel strategies for flat terrain. NVIDIA Isaac Sim was used for simulation. To evaluate the stability of both modes, we performed a frequency analysis of the passenger’s acceleration data. As a result, we observed periodic vibrations around 2 Hz in the vertical direction in walking mode, while in wheeled mode, we confirmed extremely small vibrations and stable running. Furthermore, we distilled these two strategies into a single mode-conditional strategy and conducted long-distance running experiments involving mode transformation. The results demonstrated that by adaptively switching between walking and wheeled modes depending on the terrain, mobility efficiency was significantly improved compared to continuous operation in a single mode. This study demonstrates the effectiveness of an approach that involves learning multiple specialized strategies and switching between them as needed to efficiently traverse diverse environments using a transformable robot. Full article

Accurate trajectory tracking remains a central challenge in differential-drive mobile robots (DDMRs), particularly when operating under real-world conditions. Model Predictive Control (MPC) provides a powerful framework for this task, but its performance degrades when the robot deviates significantly from the nominal path. To address this limitation, robust recovery mechanisms are required to ensure stable and precise tracking. This work presents an experimental validation of an MPC controller applied to a four-wheel DDMR, whose odometry is corrected by a SLAM algorithm running in ROS 2. The MPC is formulated as a quadratic program with state and input constraints on linear (v) and angular ($\omega$) velocities, using a prediction horizon of $N_p=15$ future states, adjusted to the computational resources of the onboard computer. A novel dynamic look-ahead re-entry strategy is proposed, which activates when the robot exits a predefined lateral error band ($\delta =0.05$ m) and interpolates a smooth reconnection trajectory based on a forward look-ahead point, ensuring gradual convergence and avoiding abrupt re-entry actions. Accuracy was evaluated through lateral and heading errors measured via geometric projection onto the nominal path, ensuring fair comparison. From these errors, RMSE, MAE, $P_{95}$, and in-band percentage were computed as quantitative metrics. The framework was tested on real hardware at 50 Hz through 5 nominal experiments and 3 perturbed experiments. Perturbations consisted of externally imposed velocity commands at specific points along the path, while configuration parameters were systematically varied across trials, including the weight R, smoothing distance $L_{\mathrm{smooth}}$, and activation of the re-entry strategy. In nominal conditions, the best configuration (ID 2) achieved a lateral RMSE of 0.05 m, a heading RMSE of 0.06 rad, and maintained 68.8% of the trajectory within the validation band. Under perturbations, the proposed strategy substantially improved robustness. For instance, in experiment ID 6 the robot sustained a lateral RMSE of 0.12 m and preserved 51.4% in-band, outperforming MPC without re-entry, which suffered from larger deviations and slower recoveries. The results confirm that integrating MPC with the proposed re-entry strategy enhances both accuracy and robustness in DDMR trajectory tracking. By combining predictive control with a spatially grounded recovery mechanism, the approach ensures consistent performance in challenging scenarios, underscoring its relevance for reliable mobile robot navigation in uncertain environments. Full article

The article presents the analytic method for real-time detection of the stationary state of a vehicle based on information retrieved from 6 DoF IMU sensor. Reliable detection of stillness is essential for the application of resetting the inertial sensor’s output bias, called Zero Velocity Update method. It is obvious that the signal from the strapped on inertial sensor differs while the vehicle is stationary or moving. Effort was then made to find a computational method that would automatically discriminate between both states with possibly small impact on the vehicle embedded controller. An algorithmic step-by-step method for building, optimizing, and implementing a diagnostic system that detects the vehicle’s stationary state was developed. The proposed method adopts the “Mahalanobis Distance” quantity widely used in industrial quality assurance systems. The method transforms (fuses) information from multiple diagnostic variables (including linear accelerations and angular velocities) into one scalar variable, expressing the degree of deviation in the robot’s current state from the stationary state. Then, the method was implemented and tested in the dead reckoning navigation system of an autonomous wheeled mobile robot. The method correctly classified nearly 93% of all stationary states of the robot and obtained only less than 0.3% wrong states. Full article

With the rapid development of mobile robotics, wheeled bipedal robots, which combine the terrain adaptability of legged robots with the high mobility of wheeled systems, have attracted increasing research attention. To address the balance control problem during both standing and locomotion while reducing the influence of noise on control performance, this paper proposes a balance control framework based on a Linear Quadratic Regulator integrated with an Extended Kalman Filter (KLQR). Specifically, a baseline LQR controller is designed using the robot’s dynamic model, where the control input is generated in the form of wheel-hub motor torques. To mitigate measurement noise and suppress oscillatory behavior, an Extended Kalman Filter is applied to smooth the LQR torque output, which is then used as the final control command. Filtering experiments demonstrate that, compared with median filtering and other baseline methods, the proposed EKF-based approach significantly reduces high-frequency torque fluctuations. In particular, the peak-to-peak torque variation is reduced by more than 60%, and large-amplitude torque spikes observed in the baseline LQR controller are effectively eliminated, resulting in continuous and smooth torque output. Static balance experiments show that the proposed KLQR algorithm reduces the pitch-angle oscillation amplitude from approximately ±0.03 rad to ±0.01 rad, corresponding to an oscillation reduction of about threefold. The estimated RMS value of the pitch angle is reduced from approximately 0.010 rad to 0.003 rad, indicating improved convergence and steady-state stability. Furthermore, experiments involving constant-speed straight-line locomotion and turning indicate that the KLQR algorithm maintains stable motion with velocity fluctuations limited to within ±0.05 m/s. The lateral displacement deviation during locomotion remains below 0.02 m, and no abrupt acceleration or deceleration is observed throughout the experiments. Overall, the results demonstrate that applying Extended Kalman filtering to smooth the control torque effectively improves the smoothness and stability of LQR-based balance control for wheeled bipedal robots. Full article

Traditional wheeled ground robots offer high energy efficiency and excellent mobility on flat terrain but are constrained by their fixed structures, making it difficult to overcome obstacles or adapt to complex environments. To address these limitations, this paper presents a morphing wheeled land–air robot (MW-LAR) that integrates ground locomotion and quadrotor flight. By incorporating foldable arms and variable-diameter wheels, the MW-LAR can not only switch between ground and flight modes, but also achieve transitions between wheeled and legged locomotion in the ground mode. The foldable arms support seamless aerial-to-ground transitions and in-flight morphing, while the variable-diameter wheels facilitate efficient obstacle traversal on the ground. Benefiting from the design of foldable arms, two complementary landing approaches, namely direct quadrotor landing and ground-mode landing, are implemented to explore different aerial-to-ground transition modes and to improve landing safety and switching efficiency. Experimental results demonstrate that the MW-LAR achieves stable and energy-efficient performance across multiple locomotion modes and complex environments, highlighting its potential for integrated land–air mobility applications. Full article

This paper establishes an integrated framework combining self-induced vibration measurements with deep learning for vibration-based remaining useful life (RUL) prediction of mechanical frame structures in mobile robots. The main innovations comprise (1) a self-induced vibration excitation system that utilizes the robot’s drive wheels to generate controlled mechanical oscillations, using a five-sensor micro-electro-mechanical system (MEMS) accelerometer array to capture non-uniform vibration mode shapes across the robot’s structure, and (2) a processing pipeline for RUL prediction using accelerometer data and early feature fusion in two machine-learning models (long short-term memory (LSTM) and a convolutional neural network (CNN)). Our research methodology includes (i) modal analysis to identify the robot’s natural frequencies, (ii) verification platform evaluation, comparing low-cost MEMS accelerometers against a reference integrated electronic piezoelectric (IEPE) accelerometer, demonstrating industrial-grade measurement quality (coherence > 98%, uncertainty 4.79–7.21%), and (iii) data-driven validation using real data from the mechanical frame, showing that the LSTM model outperforms the CNN with a 2.61× root-mean-square error (RMSE) improvement (R² = 0.99). Our solution demonstrates that early feature fusion provides sufficient information to model degradation and detect faults early at a lower cost, offering a feasible alternative to classical maintenance procedures through combined hardware validation and lightweight software suitable for Industrial Internet-of-Things (IIoT) deployment. Full article

The construction and facilities management sectors are increasingly adopting automation technologies to improve productivity and reduce manual labor. In parallel, decorative and informational floor-marking is widely used in indoor environments such as schools, exhibition halls, and public spaces to support organization, wayfinding, and visual communication. While robotic systems have been developed for floor and layout marking, many existing solutions rely on specialized infrastructure or offer limited flexibility in the range of patterns that can be produced. This paper presents the development of a prototype of a mobile, wheeled robot capable of autonomously executing diverse designs on surfaces such as fields and floors. The robot’s potential applications include use on indoor floors and exhibition halls. It marks the ground using a plotting pen while navigating and avoiding obstacles within its environment. Additionally, the robot can produce a range of drawings, including letters and signage, and its capabilities can be extended to create decorative patterns as well as marks for floor-based games. This robot was constructed entirely from cost-effective, commercially available components. Experimental evaluation demonstrates repeatable motion and drawing performance, with measured standard deviations of approximately 1.6 mm in forward motion and 3 mm in lateral motion during representative grid-based traversal. These results indicate that the proposed approach achieves a level of accuracy and consistency sufficient for decorative floor-marking and similar applications, without reliance on external localization infrastructure. Full article

Recent advances in mobile robotics have emphasized the need for systems capable of operating in unstructured environments, combining obstacle negotiation, stability, and adaptability. This study presents the preliminary design and testing of Brush.Q, an articulated ground robot featuring a novel structure distinct from existing wheel-legged robots, equipped with compliant brush-like wheels composed of multiple spokes. The main contribution is the experimental analysis of suspension capability across different wheel geometric profiles, combined with the assessment of obstacle-climbing performance. A simplified prototype was constructed to evaluate the effects of wheel rotation direction, spoke number, and spoke tapering. Results show that reducing the number of spokes improves obstacle-climbing at the expense of suspension, while higher spoke count and compliant geometry enhance suspension and stability. Spoke tapering improves obstacle climbing in the backward-facing configuration but consistently reduces suspension. Overall, these findings highlight the critical role of wheel geometry and the potential for reconfigurable spoked wheels to enhance adaptability and versatility in unstructured terrains. Full article

In this work, an effective control framework is proposed for a two-wheeled mobile robot (TWMR) operating under time-varying disturbances and uncertain system parameters. To enhance robustness against these uncertainties, an integral sliding mode control (ISMC) method is adopted. A mathematical model of the TWMR is obtained in the state form, and an ISMC law is designed. The proposed control law comprises two terms: a nominal term and a discontinuous term. The nominal term is designed based on the linearized model and optimal control to eliminate any steady-state error, while the discontinuous term is designed based on the sliding surface and the reaching law to force the system state onto the sliding surface under changing disturbances and parameter variations. These two terms are combined to constitute the overall control law. The performance and robustness of the proposed method are assessed through simulation under different uncertainty conditions of the TWMR. Full article

This paper presents an intelligent robotic system for in-pipe inspection that integrates a novel mechanical design, deep learning-based defect detection, and high-fidelity simulation for real-time validation. Unlike existing solutions, the proposed system combines a Mecanum wheel-based mobile platform with a modular arm and advanced pan-tilt camera, enabling navigation and inspection of pipes ranging from 100 mm to 500 mm in diameter. A comprehensive dataset of 53,486 images, including 27,000 annotated defect instances across six critical classes, was used to train a YOLOv11-based detection framework. The model achieved high accuracy with a precision of 0.9, recall of 0.8, mAP@0.5 of 0.9, and mAP@0.5:0.95 of 0.6, outperforming previous YOLO versions, SSD, RCNN, and DinoV2 by 26% in mAP. Real-time testing on a Raspberry Pi Camera 3 Wide IR module validated the robust detection under realistic conditions. This work contributes a mechanically adaptable robot, an optimized deep learning inspection framework, and an integrated simulation-to-deployment workflow, providing a scalable and autonomous solution for industrial pipeline inspection. Full article

This paper presents a new approach to the dimensional synthesis of a robotic limb mechanism for a wheel-legged robot. The proposed kinematic structure enables independent control of wheel motions relative to the robot platform, allowing each drive to perform a distinct movement. The selection of the mechanism’s common dimensions simplifies platform levelling to a single-drive actuation. The hybrid limb design, which combines features of driving and walking systems, enhances platform stability on uneven terrain and is suitable for rescue, exploration, and inspection robots. The synthesis method defines the desired trajectory of the wheel centre and applies a genetic algorithm to determine mechanism dimensions that reproduce this motion. The stochastic optimisation process yields multiple feasible solutions, enabling the introduction of additional design criteria for optimal configuration selection. Analytical kinematic relations were developed for workspace and trajectory evaluation, solving both direct and inverse kinematic problems. The results confirm the effectiveness of evolutionary optimisation in synthesising complex kinematic mechanisms. The proposed approach can be adapted to other mobile robot structures. Future work will address dynamic modelling, adaptive control for real-time platform levelling, and comparative studies with other synthesis methods. Full article