Search Results (51)

Autonomous omnidirectional vehicles that combine redundant holonomic kinematics, ROS 2/micro-ROS implementation, and simulation-to-real validation remain limited in the literature. This paper presents an eight-wheel Mecanum autonomous mobile robot for campus navigation in environments shared with pedestrians. The work formulates forward and inverse kinematics for the redundant eight-wheel topology and implements a distributed architecture in which ROS 2 handles high-level navigation and micro-ROS connects ESP32-based wheel interfaces. The platform integrates LiDAR, stereo vision, inertial, encoder, and ultrasonic sensing within a closed-loop navigation stack. Validation was conducted through Gazebo simulation and physical experiments using an out-and-back navigation protocol. In the physical platform, 91 of 100 missions were completed without safety interruptions, with pose-accuracy success rates of 96% for outbound legs and 81% for return legs under $e_p<1.5\mathrm{m}$ and $|e_{\theta }|<{15}^{\circ }$. Median errors at the intermediate waypoint were $0.64\mathrm{m}$, $0.191\mathrm{m}$, and ${17}^{\circ }$, while final-pose medians after return were $1.016\mathrm{m}$, $0.573\mathrm{m}$, and ${28.5}^{\circ }$. These results provide a quantitative baseline for campus-scale redundant Mecanum navigation and identify heading recovery as the main limitation. Full article

Rear-view human tracking and re-identification remain critical challenges for robotic visual sensing in unmanned vehicles, particularly under adverse weather conditions and severe occlusion. Conventional deep learning models often suffer from feature contamination and trajectory drift under dynamic illumination. To overcome these bottlenecks, we propose a lightweight tracking framework driven by spatiotemporal prediction and multimodal feature fusion. Specifically, an ego-motion-aware Kalman prediction mechanism maintains temporal continuity during complete occlusions. Upon target reappearance, a multi-factor descriptor—fusing color histograms with geometric constraints—is employed within a dynamic Mahalanobis search region. This is coupled with a specular-reflection-penalized adaptive learning rate (η_k) that actively freezes template updates during severe environmental degradation conditions. Evaluated on a custom Mecanum-wheeled robot, the proposed method achieves a peak precision of 94.2% and a tracking success rate of 93.4%. Extensive experiments in extreme rainy night scenarios demonstrate a 35% reduction in average tracking error, maintaining a Center Location Error (CLE) below 11 pixels. Furthermore, the system achieves a rapid target re-identification response of 72.83 ms during occlusion phases. Ultimately, this framework delivers a highly robust and real-time solution for autonomous navigation in complex dynamic environments. Full article

This paper presents a comparative modeling and experimental validation study for a modular four-wheel omnidirectional mobile robot, focusing on two locomotion architectures implemented on the same platform: four omni wheels (90° rollers) and four Mecanum wheels (45° rollers). Both configurations were evaluated under identical benchmark conditions on a 1 m × 1 m square path (4 m total path length), using the same nominal 12 V supply and the same test duration, in order to ensure a fair and reproducible cross-architecture comparison. A MATLAB/Simulink–Simscape dynamic model was developed for both architectures, while experimental validation was performed using Hall-effect current sensors integrated into the drive modules. Based on the measured and simulated motor currents, a 12 V-based electrical input-power estimate was evaluated at both motor and robot level. For the considered benchmark, the four-Mecanum configuration exhibited a lower measured input-power estimate than the four-omni configuration (17.88 W vs. 25.75 W), corresponding to an approximate reduction of 30.6% under the adopted assumptions. At robot level, the deviation between simulated and measured total input-power estimate was 3.70% for the four-omni architecture and 21.42% for the four-Mecanum architecture, indicating higher predictive agreement for the omni-wheel model in its present form. The comparative analysis also suggests that wheel–ground interaction and roller geometry influence not only the measured current demand but also the level of agreement between simulation and experiment. Although the present study is limited to a single standardized benchmark and nominal-voltage conditions, it provides a controlled basis for comparing the two locomotion solutions and for identifying directions for further model refinement. The findings should therefore be interpreted as benchmark-specific comparative results offering practical guidance for locomotion architecture selection and for future refinement of friction-aware omnidirectional robot models. Full article

To enhance the navigation efficiency and obstacle avoidance capability of omnidirectional mobile robots in unstructured and complex environments, this paper conducts research on intelligent path planning and management for X-type Mecanum-wheeled mobile robots with the improved Proximal Policy Optimization–Gated Recurrent Unit (PPO-GRU) model on the basis of robot kinematics modeling and deep reinforcement learning. First, by performing kinematic modeling of the X-type Mecanum-wheeled chassis and designing a high-dimensional state space along with a multi-factor composite reward function, the agent training environment for the robot–environment interaction control is established, laying the environmental foundation for in-depth research on path planning. Second, based on the construction of a Proximal Policy Optimization (PPO) path planning model, the PPO model is integrated with Gated Recurrent Units (GRUs) to form an improved PPO-GRU path planning model, thereby achieving an end-to-end path planning strategy. Finally, using a self-developed kinematic simulation platform for the X-type Mecanum-wheeled robot, the rationality and robustness of the proposed path planning model are investigated through ablation experiments, comparative experiments, dynamic environment tests, and tests considering key real-world phenomena. The research results indicate that the improved PPO-GRU path planning model increases the path planning success rate to 96%, reduces the average number of collisions by 82.7%, and achieves an average linear velocity reaching 84.5% of the maximum speed set in the environment. While attaining high-precision and robust planning management for autonomous navigation paths, it significantly improves the response speed of the agent’s autonomous navigation path planning. Full article

Traditional dexterous hands can readily grasp objects but face limitations in dexterous manipulation due to complex control systems and high actuation demands. This paper presents a novel dexterous hand designed to address these challenges. The hand consists of four fingers, each equipped with two mecanum wheels at the fingertips to allow for the omnidirectional manipulation of objects. Continuous rotation of the mecanum wheels enables unbounded motion of grasped objects without the need for finger gaiting. Object pose adjustment is achieved by controlling the rotation of mecanum wheels, thus significantly reducing operational complexity and enhancing manipulative agility. Furthermore, to address the control difficulty of multi-finger coordinated motion, a four-finger coupled mechanism is implemented, resulting in a dexterous hand with three degrees of freedom. Kinematic models of omnidirectional manipulation are established for typical geometric objects, including a flat plate, a cuboid, a sphere, and a cylinder. Simulations confirm the correctness of the kinematic models. Experimental results show that the hand can achieve omnidirectional manipulation of objects. Finally, the extended functionality of the dexterous hand is briefly presented, which allows it to be reconfigured into an omnidirectional mobile robot. 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 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

Robotic walking assistance devices support the rehabilitation of patients with neurological impairments. However, most commercialized systems rely on treadmill-based walking, which may not reflect real-world environments. This study aimed to evaluate the usability of a newly developed over-ground walking assistance robot (OWAR-MW) based on mecanum wheels compared with a commercial system (Andago) from the perspectives of physical therapists and patients with stroke. Nine physical therapists and nine stroke patients participated. Each participant walked 100 m using both the OWAR-MW and Andago systems. Subsequently, a satisfaction survey was conducted across three categories—safety, operability and functionality, and convenience—using a questionnaire adapted from the standard usability testing guidelines for walking assistive devices. Additionally, in-depth interviews were conducted to explore user experience and improvement needs. In both participant groups, the OWAR-MW showed a tendency for lower satisfaction scores than Andago across all categories. Stroke patients reported significantly lower scores in all three categories (safety: 4.90 vs. 4.04, operability and functionality: 4.83 vs. 4.33, convenience: 4.87 vs. 4.49, p < 0.05), whereas therapists noted a significant difference only in safety (4.02 vs. 3.37, p < 0.05). Key issues identified included a lack of handles, delay in actuator response, low motion detection sensitivity, non-intuitive controls, and discomfort caused by the harness, particularly the thigh straps. OWAR-MW demonstrated usability limitations in its current prototype form. Technical improvements in user interface, control accuracy, and harness design are necessary before clinical application. This study provides valuable feedback for the future development of user-centered rehabilitation robotics. 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 paper investigates the dynamic modeling and robust control of a Mecanum-wheeled vehicle (MWV) under load disturbances and measurement noise. The system is modeled as a cascaded state-space representation, where the motor transfer function (PWM input → torque output) and the vehicle transfer function (torque input → vehicle speed output) are combined. The PWM-induced motor delay is linearized, and the complete dynamic model is derived using Lagrangian mechanics, addressing the limitations of conventional models that are incomplete and unable to decouple control signals from disturbance signals. For the developed model, a robust stability controller is designed by integrating Internal Model Control (IMC) with a Disturbance Observer (DOB), enhancing real-time disturbance rejection. Open-loop experiments validate the model’s accuracy, showing a Dynamic Time Warping (DTW) error of 0.2662 m, significantly lower than the 0.3198 m observed in traditional models. In closed-loop simulations, under load disturbances ($T_L=0.1$ to $T_L=-0.7$) and Gaussian noise (power: 0.0001–0.00005), the proposed IMC + DOB controller achieves 97.6% faster stabilization than IMC and 98.3% faster than PID, demonstrating superior convergence speed, robustness, and disturbance rejection. This study provides a novel control strategy that effectively handles non-square system dynamics while mitigating external disturbances in real time. The proposed framework enhances trajectory tracking accuracy and stability, with potential applications in autonomous robotics and vehicular systems. Full article

Wheelchairs play a crucial role in society by providing mobility and autonomy to individuals with physical disabilities, essential for their social inclusion. However, conventional wheelchairs often face significant limitations in narrow spaces and uneven terrains. The development of omnidirectional wheelchairs with suspension systems, as addressed in this work, is essential to tackle these challenges and offer greater independence to individuals with disabilities. These innovations can enhance quality of life by enabling access to previously inaccessible places and facilitating mobility in areas where, for example, sidewalks are deteriorated or nonexistent. The wheelchair was designed considering the challenges that conventional models face in terms of maneuverability and mobility in uneven terrains with small obstacles. The design process is briefly described, with a special focus on system requirements, conceptual design, hardware architecture, and the overall proposed design, along with the proposed control strategy. An analysis of the Mecanum-wheeled locomotion system when one of the wheels encounters an obstacle is also presented. It was concluded that the proposed design met the initial requirements, and that the suspension system allowed the wheelchair to navigate uneven terrains without experiencing significant changes in pitch or roll angles while keeping all four wheels in contact with the ground. Full article

The Mecanum-wheeled robot has four special wheels. It can control four wheels independently and has seven turning axes. The robot can translate in all directions and travel in curves without changing its direction by means of the control commands for turning ratio, speed, and direction of travel. However, no model has been proposed that can accurately simulate the output of the actual machine for the three types of inputs, even when the characteristics of the motor and motor driver are unknown. In this study, we synthesized and simplified transfer functions and estimated the undetermined coefficients that minimize the sum of squared errors to construct a model of the robot that can output the position and posture equivalent to those of the actual robot for the input commands for turning ratio, speed, and the direction of travel. We modeled a Mecanum-wheeled robot using the proposed modeling method and parameter determination method and compared the outputs of the real robot to the step and ramp inputs. The results showed that the errors between the two outputs were very small and accurate enough to simulate AI learning, such as reinforcement learning, using the model of the robot. 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