Search Results (33)

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 article presents a 2D pose estimation method for an omnidirectional mobile robot with Mecanum wheels, using an extended Kalman filter (EKF) formulated on the Lie group $\mathrm{SO}\left(2\right)$. The purpose is estimate the robot’s position and orientation by fusing angular velocity measurements from the wheel encoders with data from an IMU. Employing Lie algebra, the EKF provides a consistent and compact representation of rotational motion, improving prediction and update steps. The filter was implemented in ROS 1 and validated in simulation using Gazebo, with a reference trajectory and real measurements used for evaluation. The system delivers higher pose estimation precision, validating the effectiveness in rotational maneuvers. Full article

Path navigation for mobile robots critically determines the operational efficiency of warehouse logistics systems. However, the current QR (Quick Response) code path navigation for warehouses suffers from low operational efficiency and poor dynamic adaptability in complex dynamic environments. This paper introduces a deep reinforcement learning and hybrid-algorithm SLAM (Simultaneous Localization and Mapping) path navigation method for Mecanum-wheeled robots, validated with an emphasis on dynamic adaptability and real-time performance. Based on the Gazebo warehouse simulation environment, the TD3 (Twin Deep Deterministic Policy Gradient) path planning method was established for offline training. Then, the Astar-Time Elastic Band (TEB) hybrid path planning algorithm was used to conduct experimental verification in static and dynamic real-world scenarios. Finally, experiments show that the TD3-based path planning for mobile robots makes effective decisions during offline training in the simulation environment, while Astar-TEB accurately completes path planning and navigates around both static and dynamic obstacles in real-world scenarios. Therefore, this verifies the feasibility and effectiveness of the proposed SLAM path navigation for Mecanum-wheeled mobile robots on a miniature warehouse platform. Full article

The developed omnidirectional mobile manipulator is an educational omnidirectional mobile manipulator that utilizes the Raspberry Pi Pico W and is programmed in Python. It is designed to enhance STEM education by providing an interactive environment for studying robotics, sensor integration, and programming techniques. The robot is built on an off-the-shelf chassis equipped with Mecanum wheels and a robotic arm actuated by servo motors. As part of this project, the control electronics were designed and implemented to enable seamless operation. While the platform allows students to program the robot as part of the STEM curriculum, our base software solution, developed in Python, provides control of both the mobile base and the robotic arm via a web interface accessible through the robot’s Wi-Fi hotspot. Full article

This study proposes a novel nonlinear adaptive fuzzy hybrid sliding mode (AFHSM) control strategy for the precise trajectory tracking of autonomous mobile robots (AMRs) equipped with four Mecanum wheels. The control design addresses the inherent complexities of such platforms, which include strong system nonlinearities, significant parametric uncertainties, torque saturation effects, and external disturbances that can adversely affect dynamic performance. Unlike conventional approaches that rely on model linearization or dimension reduction, the proposed AFHSM control retains the full nonlinear characteristics of the system to ensure accurate and robust control. The controller is systematically derived from the trajectory-tracking error dynamics between the AMR and the desired trajectory (DT). It integrates higher-order sliding mode (SM) control, fuzzy logic inference, and adaptive learning mechanisms to enable real-time compensation for model uncertainties and external perturbations. In addition, a saturation handling mechanism is incorporated to ensure that the control signals remain within feasible limits, thereby preserving actuator integrity and improving practical applicability. The stability of the closed-loop nonlinear system is rigorously established through the Lyapunov theory, guaranteeing the asymptotic convergence of tracking errors. Comprehensive simulation studies conducted under severe conditions with up to 60 percent model uncertainty confirm the superior performance of the proposed method compared to classical SM control. The AFHSM control consistently achieves lower trajectory and heading errors while generating smoother control signals with reduced torque demand. This improvement enhances tracking precision, suppresses chattering, and significantly increases energy efficiency. These results validate the effectiveness of the AFHSM control approach as a robust and energy-aware control solution for AMRs operating in highly uncertain and dynamically changing environments. Full article

This study proposes a nonlinear adaptive optimal control method, the adaptive H₂ control method, applied to the trajectory tracking problem of the wheeled mobile robot (WMR) with four-wheel mecanum wheels. From the perspective of solving mathematical problems, finding an analytical adaptive control solution that satisfies the adaptive H₂ performance criterion for the trajectory tracking problem of the WMR with four-wheel mecanum wheels is an extremely challenging task due to the high complexity of the dynamic system. To analytically derive the control law and adaptive control law for this trajectory tracking problem, a proportional-derivative (PD) type transformation is employed to formalize the trajectory tracking error dynamics between the WMR and the desired trajectory (DT). Based on an in-depth analysis of the trajectory tracking error dynamics, a closed-form adaptive control law is analytically derived from the highly complex nonlinear dynamic system equations. This control law provides a solution to the trajectory tracking problem of the WMR while satisfying the adaptive H₂ performance criterion. The proposed adaptive nonlinear control method offers a simple control structure and advantages such as improved energy efficiency. Finally, simulations and experimental implementations were conducted to verify the performance of the proposed adaptive H₂ control method and the H₂ control method in tracking the DT. The results demonstrate that, compared to the H₂ control method, the adaptive H₂ control method exhibits superior trajectory tracking performance, particularly in the presence of significant model uncertainties. Full article

This paper presents a mobile robot concept. To understand what has led to the choice of this particular design, one must first understand mobile robots in general: what are the trends, where are they used, etc. Then, some of the necessary elements for robot propulsion and navigation were considered, looking at their principle of operation, as well as their application. A description of the robot structure follows. Thanks to the software, the approximate value of the various parameters can be calculated, and an initial three-dimensional model can be built. First, the idea of the robot will be introduced in the abstract and then be outlined in detail, showing how the various components work. It will conclude by looking at the most important elements of a mobile robot, as well as what makes a robot successful. Full article

This scientific paper presents the development and validation process of a dynamic model in Simulink used for decision-making regarding the locomotion and driving type of autonomous omnidirectional mobile platforms. Unlike traditional approaches relying on differential equations, this study uses Simulink’s block-based diagrams, offering a simpler and efficient development process. Importantly, the dynamic model accounts for friction forces, a critical factor for energy monitoring. The model’s validation is conducted experimentally, ensuring its accuracy and reliability. This paper formulates mathematical models for both conventional and Mecanum wheel configurations, facilitating energy-efficient driving strategies. By decomposing resistive forces into inertial and frictional components using the Jacobian matrix, this study accurately simulates electrical current consumption during robot motion. Through fuzzy decision algorithms utilizing parameters such as energy consumption, travel time, precision, and desired maneuverability, this paper proposes a method for determining the optimal locomotion mode for mobile platforms with Mecanum wheels. Overall, this research brings a new contribution to the field of mobile robotics by providing a comprehensive framework for dynamic modeling and it offers the possibility to drive omnidirectional robots in an energy-efficient manner. Full article

This paper introduces the mechanical design and control system of a mobile robot for logistics transportation in manufacturing workshops. The robot is divided into a moving part and a grasping part. The moving part adopts the mecanum wheel four-wheel-drive chassis, which has omnidirectional moving ability. The mechanical system is based on four mechanical wheels, and a modular suspension mechanism is designed. The grasping part is composed of a depth camera, a cooperative manipulator, and an electric claw. Finally, the two are coordinated and controlled by computer. The controller hardware of the mobile platform is designed, and the functional modules of the mobile platform are designed based on the RT thread embedded system. For the navigation part of the mobile robot, a fuzzy PID deviation correction algorithm is designed and simulated. Using the Hough circular transform algorithm, the visual grasping of the manipulator is realized. Finally, the control mode of the computer-controlled manipulator and the manipulator-controlling mobile platform is adopted to realize the feeding function of the mobile robot, and the experimental verification is carried out. Full article

Omnidirectionality is a feature that allows motion in any direction without orientation maneuvers. Omnidirectional mobile robots are usually based on omni or mecanum wheels. The motion of an omnidirectional mobile robot is defined by a target motion command $M=\left(v,\alpha ,\omega \right)$, where $v$ is the module of the translational velocity; $\alpha$ is the angular orientation of the translational velocity, and $\omega$ is the angular velocity of the mobile robot. The motion is achieved by converting the target motion command into the target angular velocities that must be applied to the active wheels of the robot. This work proposes a simplified phasor-like interpretation of the relationship between the parameters of a specific motion command and the angular velocities of the wheels. The concept of phasor-like notation is validated from the analysis of the kinematics of omnidirectional mobile robots using omni wheels and mecanum wheels. This simplified phasor-like notation fosters unconstrained conceptual design of single-type and hybrid multi-wheeled omnidirectional mobile robots without the distribution or type of wheels being a design constraint. Full article

Mechanical systems that consist of a four-wheeled or two-wheeled robot with Mecanum wheels and a two-wheeled trailer with conventional wheels are considered. The kinematic characteristics of the mechanical systems under consideration of holonomic and non-holonomic constraints are presented and compared. From this, it is shown that the structure of the kinematic constraint equations for mobile systems with a trailer does not apply to Chaplygin’s dynamic equations. If the mechanical system is not Chaplygin’s system, then the dynamic equations cannot be integrated separately from the equations of kinematic constraints. This is the difference between the kinematic constraint equations for the robot-trailer system and the constraint equations for a single robot with Mecanum wheels. Examples of numerical calculations using the equations of kinematic constraints are given. Full article

This paper presents an exciting and meaningful design to make mobile robots capable of adapting to various terrains. We designed a relatively simple and novel composite motion mechanism called the flexible spoked mecanum (FSM) wheel and created a mobile robot, LZ-1, with multiple motion modes based on the FSM wheel. Based on the motion analysis of the FSM wheel, we designed an omnidirectional motion mode for this robot, allowing it to move flexibly in all directions and successfully traverse rugged terrains. In addition, we designed a crawl motion mode for this robot, which can climb stairs effectively. We used a multilayer control method to move the robot according to the designed motion modes. Multiple experiments showed that these two motion modes for the robot are effective on various terrains. Full article

The distributed model predictive control (DMPC) strategy with particle swarm optimization (PSO) is applied to solve the collision-free trajectory tracking problem for the mecanum-wheeled mobile robot (MWMR) formation. Under the leader–follower framework, the predictive model is established considering the kinematics and dynamics of the MWMR with the uncertainties and external disturbances. Based on the information from itself and its neighbors, each MWMR is assigned its own finite-horizon optimal control problem, of which the objective/cost function consists of formation maintenance, trajectory tracking, and collision avoidance terms, and the control inputs of each MWMR are computed synchronously in a distributed manner. PSO serves as the fast and effective optimizer to find feasible solutions to these finite-horizon optimal control problems. Further, the feedback emendation is implemented using a double closed-loop compensator to efficiently inhibit the influence of unknown dynamics in real time. The stability of the proposed distributed formation control approach is strictly analyzed. Numerical simulations confirmed the robustness and effectiveness of the control approach in obstacle environments. Full article

This paper proposes a new trajectory tracking control scheme for the four mecanums wheel omnidirectional mobile robot (FM-OMR). Considering the influence of uncertainty on tracking accuracy, a self-organizing fuzzy neural network approximator (SOT1FNNA) is proposed to estimate the uncertainty. In particular, since the structure of traditional approximation network is preset, it will cause problems such as input constraints and rule redundancy, resulting in low adaptability of the controller. Therefore, a self-organizing algorithm including rule growth and local access is designed according to the tracking control requirements of omnidirectional mobile robots. In addition, a preview strategy (PS) based on Bezier curve trajectory re-planning is proposed to solve the problem of tracking curve instability caused by the lag of tracking starting point. Finally, the simulation verifies the effectiveness of this method in tracking and trajectory starting point optimization. Full article

Chalk lines are used to print layouts in construction sites to indicate the location of attaching or cutting objects; printing depends on the skills of workers and is suitable for small-scale work. Moreover, this type of work requires a precise measurement process, which is time-consuming, to avoid errors. Thus, discrepancies between blueprints and construction site layouts can occur if construction plans and management are not uniformly aligned. To improve the traditional floor-layout-printing technique on construction sites, this study introduces a manually operated mecanum-wheeled mobile robot in the preliminary stage, i.e., before the development of a full-fledged automated system. This manually operated robot helps determine the technologies required for robotic automation. In the development process, layout-printing technology is classified into a marking toolkit, control system that can be manually operated, and mobile driver. To improve layout-printing quality, this study adopted a mecanum-wheeled design to improve mobility. In this study, applied tests are required to consider the site environment for automatically marking floor layout prints. To determine the applicability of the developed technology, this study conducted a field applicability experiment with a pen-type marking module and laser-type toolkit. The experiment confirmed that layout printing based on environmental changes on the construction site can be manually performed using the mobile robot system. To automatically mark floor-layout-printing work, it is necessary to consider the floor characteristics on the construction site. In addition, this experiment shows that the newly applied laser toolkit technology can be applied to layout printing within 12 mm from the floor. To apply this mechanism to a mobile robot that can automate layout printing, it is necessary to technically enhance the optimization of marking quality, e.g., floor separation distance and marking thickness. Full article