Mecanum-wheeled Mobile Robot Research Articles

Experimental control approach of a mecanum-wheeled mobile robot for slippage error and energy consumption reduction on different surfaces

Experimental control approach of a mecanum-wheeled mobile robot for slippage error and energy consumption reduction on different surfaces

Control of Mecanum Wheel Mobile Robot Based on Reinforcement Learning

Control of Mecanum Wheel Mobile Robot Based on Reinforcement Learning

Sliding mode observer-based model predictive tracking control for Mecanum-wheeled mobile robot

This paper proposes a novel adaptive variable power sliding mode observer-based model predictive control (AVPSMO-MPC) method for the trajectory tracking of a Mecanum-wheeled mobile robot (MWMR) with external disturbances and model uncertainties. First, in the absence of disturbances and uncertainties, a model predictive controller that considers various physical constraints is designed based on the nominal dynamics model of the MWMR, which can transform the tracking problem into a constrained quadratic programming (QP) problem to solve the optimal control inputs online. Subsequently, to improve the anti-jamming ability of the MWMR, an AVPSMO is designed as a feedforward compensation controller to suppress the effects of external disturbances and model uncertainties during the actual motion of the MWMR, and the stability of the AVPSMO is proved via Lyapunov theory. The proposed AVPSMO-MPC method can achieve precise tracking control while ensuring that the constraints of MWMR are not violated in the presence of disturbances and uncertainties. Finally, comparative simulation cases are presented to demonstrate the effectiveness and robustness of the proposed method.

Reinforcement Learning-Based Tracking Control for a Three Mecanum Wheeled Mobile Robot.

This brief investigates the robust optimal tracking control for a three Mecanum wheeled mobile robot (MWMR) with the external disturbance by the aid of online actor-critic synchronous learning algorithm. The Euler-Lagrange motion equation of MWMR subject to slipping is established by analyzing the structural characteristics of Mecanum wheels. Concatenating the tracking error with the desired trajectory, the tracking control problem is converted into a time-invariant optimal control problem of an augmented system. Then, an approximate optimal tracking controller is obtained by applying online actor-critic synchronous learning algorithm. With the help of Lyapunov-based analysis, the ultimately bounded tracking can be guaranteed. Finally, simulation results show the effectiveness of synchronous learning algorithm and approximate optimal tracking controller.

Mecanum wheel mobile platform for rapid prototyping of the autonomous control algorithms

This article describes the author’s design of the Mecanum wheel mobile robots (MWR), which can be used as a test platform for experimental research into the development and implementation of autonomous systems. The paper contains a literature review of the subject matter and describes the mechanical actuator subsystem and electronic and information subsystem of the mobile robot solution. The robot was navigated using a minicomputer equipped with a Jetson Nano chip. The robot’s navigation system was implemented using the ROS environment through high-level software. Software modularity is desirable in circuits intended for research and rapid prototyping. This allows for easier modification and maintenance. The microcontroller has been implemented with a DC drive control system and incremental encoder support to estimate robot position using odometry. The project utilizes the Jetson Nano for processing data from distance sensors and monitoring the robot’s performance. The mobile platform is equipped with a RGB-D camera (Microsoft Kinect 360), the robot can use this to perform 3D mapping of the environment. A LiDAR sensor is used to collect data from a two-dimensional space. Laboratory tests were carried out to test the validity of the solutions adopted in the design of the Mecanum-wheeled mobile robot’s navigation system configuration.

Kinematic Modeling and Stable Control Law Designing for Four Mecanum Wheeled Mobile Robot Platform Based on Lyapunov Stability Criterion

Transportation in warehouses and production workshops is a matter of urgency today. Most warehouses arrange routes for circulation along the shelves, transportation vehicles will move on this road to perform the task of exporting or importing goods. Routes will be arranged to move in one direction because vehicles do not have enough space to turn around in cramped warehouses. This causes many difficulties in planning the trajectory for transportation vehicles, especially self-propelled vehicles. In order to have an appropriate transportation plan, it is necessary to solve many problems, including: reasonable transport equipment, sufficient number of devices, optimal route layout, algorithm of operation center for Positioning and Navigation of transportation equipment,... This study proposes a method for transportation using an omnidirectional automated guided vehicle (AGV). The AGV's omnidirectional mobility is supported by the mecanum wheels. This study consists of two parts, the first part focuses on kinematic modeling for mecanum wheels and extends to robot’s platform using four mecanum wheels. Part two proposes a diagram to calculate the errors of the robot compared to a reference tracking line, design a control law based on the Lyapunov stability criterion. The stability of the control law is verified and confirmed by simulation on MATLAB environment.

Kinematic Modeling and Stable Control Law Designing for Four Mecanum Wheeled Mobile Robot Platform Based on Lyapunov Stability Criterion

Kinematic Modeling and Stable Control Law Designing for Four Mecanum Wheeled Mobile Robot Platform Based on Lyapunov Stability Criterion

A Neural Network Controller Design for the Mecanum Wheel Mobile Robot

Advanced controllers are an excellent choice for the trajectory tracking problem of Wheeled Mobile Robots (WMRs). However, these controllers pose a challenge to the hardware structure of WMRs due to the controller's complex structure and the large number of calculations needed. In that context, designing a controller with a simple structure and a small number of computations but good real-time performance is necessary in order to improve the tracking accuracy for the WMRs without requiring high hardware architecture. In this work, a neural network controller with a simple structure for the trajectory-tracking of a Mecanum-Wheel Mobile robot (MWMR) based on a reference controller is proposed. A two-layer feedforward neural network is designed as a tracking controller for the robot. The neural network is trained with a sample input-output data set so that the error between the neural network output and the reference control signal of the supervisory controller is minimal. The neural network parameters are trained to update over time. The simulation results verified the effectiveness of the neural network controller, whose parameters are tuned adaptively to ensure a fast convergence to the desired Bézier trajectory.

Distributed Model Predictive Control with Particle Swarm Optimizer for Collision-Free Trajectory Tracking of MWMR Formation

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.

Hand Gesture-based Teleoperation Control of a Mecanum-wheeled Mobile Robot

The aim of this study is to design an interface for the teleoperation control of a Mecanum-wheeled Mobile Robot (MMR). To make a cost-effective and non-invasive teleoperation interface approach, a hand gesture recognition strategy via a webcam is adopted. In this study, seven hand gestures are recognized on a desktop PC and then the corresponding velocity commands are sent to the MMR via WiFi. The SqueezeNet architecture is employed for the hand gesture recognition and MMR teleoperation task after various Convolutional Neural Network (CNN) models are compared. The comparison considers some aspects, i.e., accuracy, memory usage, and recognition speed, to find the most feasible method for real-time hand gesture recognition and teleoperation control. Experimental results are presented to show the excellence of the proposed SqueezeNet architecture.

Development of a Manually Operated Mobile Robot That Prints Construction Site Layouts

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.

Trajectory tracking control for mecanum wheel mobile robot by time-varying parameter PID controller

This paper presents the design method a proportional integral derivative (PID) controller with time-varying parameters for trajectory tracking of a mecanum-wheeled robot with a minor error. First, a nonlinear kinematic error model of the robot is established and linearized around the working point. Then a PID controller with time-varying parameters is designed based on this linear model. The coefficients of KP, KI, and KD control parameters are determined by trial and error technique to ensure that the robot moves along the desired trajectory with minimal error. A platform mecanum-wheeled mobile robot has been designed and manufactured to demonstrate the proposed controller. Simulation and experimental results are presented to verify the effective and accuracy of the proposed controller. It shows that can apply this research to control of a four mecanum-wheeled mobile robot in logistics services in practice.

Internal Works Quality Assessment for Wall Evenness using Vision-based Sensor on a Mecanum-Wheeled Mobile Robot

Internal Works Quality Assessment for Wall Evenness using Vision-based Sensor on a Mecanum-Wheeled Mobile Robot

Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot.

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

Development of a Mecanum-Wheeled Mobile Robot for Dynamic- and Static-Obstacle Avoidance Based on Laser Range Sensor

Development of a Mecanum-Wheeled Mobile Robot for Dynamic- and Static-Obstacle Avoidance Based on Laser Range Sensor

Mecanum Wheeled Mobile Robot for Welding Process

Mecanum Wheeled Mobile Robot for Welding Process

Navigation Simulation of a Mecanum Wheel Mobile Robot Based on an Improved A* Algorithm in Unity3D.

Computer simulation is an effective means for the research of robot navigation algorithms. In order to implement real-time, three-dimensional, and visual navigation algorithm simulation, a method of algorithm simulation based on secondary development of Unity3D is proposed. With this method, a virtual robot prototype can be created quickly with the imported 3D robot model, virtual joints, and virtual sensors, and then the navigation simulation can be carried out using the virtual prototype with the algorithm script in the virtual environment. Firstly, the scripts of the virtual revolute joint, virtual LiDAR sensors, and terrain environment are written. Secondly, the A* algorithm is improved for navigation in unknown 3D space. Thirdly, taking the Mecanum wheel mobile robot as an example, the 3D robot model is imported into Unity3D, and the virtual joint, sensor, and navigation algorithm scripts are added to the model. Then, the navigation is simulated in static and dynamic environments using a virtual prototype. Finally, the navigation tests of the physical robot are carried out in the physical environment, and the test trajectory is compared with the simulation trajectory. The simulation and test results validate the algorithm simulation method based on the redevelopment of Unity3d, showing that it is feasible, efficient, and flexible.

A Human-Robot Interaction for a Mecanum Wheeled Mobile Robot with Real-Time 3D Two-Hand Gesture Recognition

Human interaction with mobile robot becomes a popular research area and its applications are widely used in industrial, commercial and military fields. A two-hand gesture recognition method with depth camera is presented for real-time controlling the mecanum wheeled mobile robot. Seven different gestures could be recognized from one hand for mobile robot navigation and three gestures could be recognized from the other hand for controlling the gripper installed on the robot. Under the proposed control scheme, the mobile robot system can be navigated and can be operated at the same time for achieving missions by two different groups of hand gestures. The accuracy of the gesture recognition is about 94%. During mobile robot control experiment, the system works timely, accurately and stably for certain tasks such as directional movement, grasping and cleaning obstacles.

A bio-inspired sighted robot chases like a hoverfly

Here we present a novel bio-inspired visual processing system, which enables a robot to locate and follow a target, using an artificial compound eye called CurvACE. This visual sensor actively scanned the environment at an imposed frequency (50 Hz) with an angular scanning amplitude of and succeeded in locating a textured cylindrical target with hyperacuity, i.e. much finer resolution than the coarse inter-receptor angle of the compound eye. Equipped with this small, lightweight visual scanning sensor, a Mecanum-wheeled mobile robot named ACEbot was able to follow a target at a constant distance by localizing the right and left edges of the target. The localization of the target’s contrasted edges is based on a bio-inspired summation of Gaussian receptive fields in the visual system. By means of its auto-adaptive pixels, ACEbot consistently achieved similar pursuit performances under various lighting conditions with a high level of repeatability. The robotic pursuit pattern mimics finely the behavior of the male fly Syritta Pipens L. while pursuing the female. The high similarity in the trajectories as well as the biomimicry of the visual system provides strong support for the hypothesis that flies do maintain center the target and constant its subtended angle during smooth pursuit. Moreover, we discuss the fact that such simple strategy can also provide a trajectory compatible with motion camouflage.

ICタグ環境を利用した移動ロボットの誘導と制御

We have presented the method to navigate and control a mobile robot under the environment informationized by IC tags so far. A robot can estimate the self-position based on coordinates written in the IC tags. Furthermore, the Dijkstra's algorithm derives a route by using the area number written in the IC tags. We used the omni-wheel mobile robot, but this paper uses the mecanum-wheel mobile robot. The mecanum-wheel robot is easier to go straight and to overcome a step than the omni-wheel robot. So, this paper experiments navigation and control using the mecanum-wheel robot with double antennas in the IC tag environment.