Omnidirectional Wheeled Mobile Robot Research Articles

Using a Robot for Indoor Navigation and Door Opening Control Based on Image Processing

This study used real-time image processing to realize obstacle avoidance and indoor navigation with an omnidirectional wheeled mobile robot (WMR). The distance between an obstacle and the WMR was obtained using a depth camera. Real-time images were used to control the robot’s movements. The WMR can extract obstacle distance data from a depth map and apply fuzzy theory to avoid obstacles in indoor environments. A fuzzy control system was integrated into the control scheme. After detecting a doorknob, the robot could track the target and open the door. We used the speeded up robust features matching algorithm to recognize the WMR’s movement direction. The proposed control scheme ensures that the WMR can avoid obstacles, move to a designated location, and open a door. Like humans, the robot performs the described task only using visual sensors.

Nonlinear Model Predictive Control-based Collision Avoidance for Mobile Robot

This work proposes an efficient and safe single-layer Nonlinear Model Predictive Control (NMPC) system based on LiDAR to solve the problem of autonomous navigation in cluttered environments with previously unidentified static and dynamic obstacles of any shape. Initially, LiDAR sensor data is collected. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, is used to cluster the (Lidar) points that belong to each obstacle together. Moreover, a Minimum Euclidean Distance (MED) between the robot and each obstacle with the aid of a safety margin is utilized to implement safety-critical obstacle avoidance rather than existing methods in the literature that depend on enclosing the obstacles with a circle or minimum bounding ellipse. After that, to impose avoidance constraints with feasibility guarantees and without compromising stability, an NMPC for set-point stabilization is taken into consideration with a design strategy based on terminal inequality and equality constraints. Consequently, numerous obstacles can be avoided at the same time efficiently and rapidly through unstructured environments with narrow corridors. Finally, a case study with an omnidirectional wheeled mobile robot (OWMR) is presented to assess the proposed NMPC formulation for set-point stabilization. Furthermore, the efficacy of the proposed system is tested by experiments in simulated scenarios using a robot simulator named CoppeliaSim in combination with MATLAB which utilizes the CasADi Toolbox, and Statistics and Machine Learning Toolbox. Two simulation scenarios are considered to show the performance of the proposed framework. The first scenario considers only static obstacles while the second scenario is more challenging and contains static and dynamic obstacles. In both scenarios, the OWMR successfully reached the target pose (1.5m, 1.5m, 0°) with a small deviation. Four performance indices are utilized to evaluate the set-point stabilization performance of the proposed control framework including the steady-state error in the posture vector which is less than 0.02 meters for position and 0.012 for orientation, and the integral of norm squared actual control inputs which is 19.96 and 21.74 for the first and second scenarios respectively. The proposed control framework shows a positive performance in a narrow-cluttered environment with unknown obstacles.

Kinematic Analysis and Odometry-Based Navigation of an Omnidirectional Wheeled Mobile Robot on Uneven Surfaces

Kinematic Analysis and Odometry-Based Navigation of an Omnidirectional Wheeled Mobile Robot on Uneven Surfaces

PSO-based optimized neural network PID control approach for a four wheeled omnidirectional mobile robot

AbstractThe demand for automation using mobile robots has been increased dramatically in the last decade. Nowadays, mobile robots are used for various applications that are not attainable to humans. Omnidirectional mobile robots are one particular type of these mobile robots, which has been the center of attention for their maneuverability and ability to track complex trajectories with ease, unlike their differential type counterparts. However, one of the disadvantages of these robots is their complex dynamical model, which poses several challenges to their control approach. In this work, the modeling of a four-wheeled omnidirectional mobile robot is developed. Moreover, an intelligent Proportional Integral Derivative (PID) neural network control methodology is developed for trajectory tracking tasks, and Particle Swarm Optimization (PSO) algorithm is utilized to find optimized controller's weights. The simulation study is conducted using Simulink and Matlab package, and the results confirmed the accuracy of the proposed intelligent control method to perform trajectory tracking tasks.

Application of 3D point cloud map and image identification to mobile robot navigation

This paper presents application of the combination of mapping, image identification, and indoor navigation for the omnidirectional wheeled mobile robot (WMR). The mapping part uses the point clouds converted from the depth map of the LiDAR camera to construct the indoor 3D point cloud map. Combining this map with the You Only Look Once ver.4 (YOLOv4) network, the door number and the doorknob can be displayed on the map by image identification. The navigation part applies 3D point cloud map, image, and depth map to an omnidirectional WMR so that it can know the current position and plan the path to the designated location. The proposed control scheme can make the robot perform indoor navigation and reach designated location.

Balance Control of a Configurable Inverted Pendulum on an Omni-Directional Wheeled Mobile Robot

This paper considers the balance control problems of a configurable inverted pendulum with an omni-directional wheeled mobile robot. The system consists of two parts. One is an inverted pendulum, and another one is an omni-directional wheeled mobile robot. The system can be configured as a rotary inverted pendulum or a spherical inverted pendulum. The objective is to control the omni-directional wheeled mobile robot to provide translational force on the plane to balance the spherical inverted pendulum and to provide the moment to balance the rotary inverted pendulum. Detailed dynamic models of these two systems are derived for the control strategy design and simulation studies. Stabilizing controllers based on the second-order sliding mode control are designed for both systems. The closed-loop stability is proved based on the passivity properties. The proposed control schemes can guarantee semi-globally asymptotical stability over the upper-half plane. In addition, the conventional sliding mode controllers proposed in our previous work and Linear-Quadratic Regulator (LQR) controllers based on the linearized system models about its upright equilibrium point are also used for performance comparison. The effectiveness of the control strategies is investigated and verified using simulation and experimental studies. In the simulation studies, different sources of uncertainty and disturbance are investigated. It is shown that the second-order sliding mode control outperforms the conventional sliding mode control and LQR control without any uncertainty and disturbance. For robustness to the matched disturbance, the simulation results show that the second-order sliding mode controller has a less significant steady-state oscillation in the pendulum’s angular displacement than other controllers. The simulation results also show that only the second-order sliding mode controller can stabilize the system with a significant initial deviation from the pendulum’s upright position. Finally, the experimental results demonstrate that second-order sliding mode control outperforms conventional sliding mode control and LQR control.

Quasi‐min‐max MPC for visual servoing stabilization of omnidirectional wheeled mobile robots

AbstractThis paper proposes a new visual servoing quasi‐min‐max MPC algorithm for stabilization control of an omnidirectional wheeled mobile robot subject to physical and visual constraints. The visual servoing dynamics of the robot are modeled as the state‐dependent linear error system with nonlinear control inputs of rotation and deflection velocities of wheels. The state‐dependent linear error system is covered as linear parameters‐varying models which is used to design the visual servoing quasi‐min‐max MPC controller. The actual control inputs of the robot are then computed by the solution of an inverse algebraic equation of the MPC actions. The recursive feasibility and stability of the new visual servoing MPC are ensured by some LMIs conditions. The performance and practicability of the visual servoing MPC are verified by some simulation and experiment results.

Motion optimization of an omnidirectional mobile robot with MY wheel based on contact mechanics

PurposeThe mechanical properties between wheel and ground will affect the motion performance of wheeled omnidirectional mobile robot (OMR). MY3 wheel is an omnidirectional wheel. This paper aims to analyze the contact mechanical characteristics between MY3 wheel and ground to improve the motion accuracy of an omnidirectional mobile platform with MY3 wheel (MY3-OMR).Design/methodology/approachThis method takes MY3 wheel as the research objective. The normal and tangential contact mechanics model and rolling contact mechanics model of MY3 wheel are established by analyzing the structure of MY3 wheel, and thereby, the slip ratio of MY3 wheel in the process of motion is calculated. The kinematics model of MY3-OMR is optimized by taking the slip ratio as the optimization parameter that aims to improve motion accuracy of MY3-OMR.FindingsThe correctness of the mechanical analysis and the feasibility of the method are verified by the MY3-OMR prototype. Let MY3-OMR move along the set circular trajectory and square trajectory, and the error between the motion trajectory before and after optimization and the standard trajectory is obtained. It illustrates that the error in the square trajectory is reduced by 1.5%, and the circular trajectory error is reduced by 2%; therefore, the method is effective.Originality/valueA method based on contact mechanics is proposed and verified. Through the establishment of wheel-ground contact mechanics model to optimize MY3-OMR kinematics model, and thereby, the motion accuracy of MY3-OMR is improved, which lays a foundation for MY3-OMR engineering application.

Modeling and Trajectory Planning Optimization for the Symmetrical Multiwheeled Omnidirectional Mobile Robot

Trajectory optimization is the series of actions that are taken into consideration in order to produce the best path such that it improves the overall performances of physical properties or reduces the consumption of the resources where the restriction system remains maintained. In this paper, first, a compact mathematical model for a symmetrical annular-shaped omnidirectional wheeled mobile robot (SAOWMR) is derived and verified. This general mathematical model provides an opportunity to conduct research, experiments, and comparisons on these omnidirectional mobile robots that have two, three, four, six, or even more omnidirectional wheels without the need to switch models or derive a new model. Then, a new computationally efficient method is proposed to achieve improvements in the trajectory planning optimization (TPO) for a SAOWMR. Moreover, the proposed method has been tested in collision-free navigation by incorporation of the path constraints. Numerical tests and simulations are presented aiming to ensure the efficiency and effectiveness of the proposed method.

Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot.

The ball-catching system examined in this research, which was composed of an omni-directional wheeled mobile robot and an image processing system that included a dynamic stereo vision camera and a static camera, was used to capture a thrown ball. The thrown ball was tracked by the dynamic stereo vision camera, and the omni-directional wheeled mobile robot was navigated through the static camera. A Kalman filter with deep learning was used to decrease the visual measurement noises and to estimate the ball’s position and velocity. The ball’s future trajectory and landing point was predicted by estimating its position and velocity. Feedback linearization was used to linearize the omni-directional wheeled mobile robot model and was then combined with a proportional-integral-derivative (PID) controller. The visual tracking algorithm was initially simulated numerically, and then the performance of the designed system was verified experimentally. We verified that the designed system was able to precisely catch a thrown ball.

A New Algorithm for Calibration of an Omni-Directional Wheeled Mobile Robot Based on Effective Kinematic Parameters Estimation

Calibration is a strategy to compensate for the systematic errors of a mobile robot hence, to increase the accuracy of the robot localization. So far, various methodologies have been proposed for the calibration of wheeled robots, but the majority have focused on non-holonomic mobile robots, e.g., differential type, and holonomic ones have been less studied from this point of view. This paper presents an innovative approach for the calibration of a holonomic robot by introducing “Effective Kinematic Parameters” (EKPs). To estimate the EKPs of a holonomic mobile robot, some tests are proposed, and the variables of the inverse Jacobian equations are modified by defining and minimizing an appropriate cost function. In the following, a virtual holonomic robot is modeled with some unknown intentional errors in its parameters, which cause some error in its path tracking. Then, the methodology is applied to estimate its EKPs, and the simulation is fulfilled again. The results show incredible improvement in path tracking. The methodology is applied to a 3-wheeled Omni-directional mobile robot, and some experiments are performed in a laboratory environment to experimentally evaluate the approach. The outcomes approved the algorithm and showed great enhancement in the calibration of the robot compared to the previous researches. The methodology can be employed to calibrate other robotic systems as well.

Velocity Control of an Omnidirectional Wheeled Mobile Robot Using Computed Voltage Control with Visual Feedback: Experimental Results

In this paper, we propose a novel computed voltage based control law for an omnidirectional wheeled mobile robot equipped with four mecanum wheels. First, the dynamical model for the mechanical part is originally introduced by means of the Euler-Lagrange formulation. The motion constraints are added to the dynamic model using the Lagrange multipliers. Then, the dynamic model is completed by incorporating the dynamics of the actuators. Therefore, the dynamical model input signals are the armature voltage of the motors. The control law requires only the feedback of position and velocity of the whole robot, unlike most controllers in the literature that also need wheel speed feedback. The position and velocity are obtained by means of a multicam vision system, so measurements from motor encoders are not needed. A high-order sliding modes differentiator is included to estimate linear and angular velocities in a finite time. A stability proof is presented by means of the direct Lyapunov method, and furthermore, an analysis about parametric uncertainties in the mechanical parameters is introduced. The experimental results validate the theoretical proposal and show the good performance of the approach.

Real-Time Acceleration-Continuous Path-Constrained Trajectory Planning With Built-In Tradeoff Between Cruise and Time-Optimal Motions

In this article, a novel real-time acceleration-continuous path-constrained trajectory planning algorithm is proposed with an appealing built-in tradeoff mechanism between the cruise motion and time-optimal motion. Different from existing approaches, the proposed approach smoothens time-optimal trajectories with bang-bang input structures to generate acceleration-continuous trajectories while preserving the completeness property. More importantly, a novel built-in tradeoff mechanism is proposed and embedded into the trajectory planning framework so that the proportion of the cruise motion and time-optimal motion can be flexibly adjusted by changing a user-specified functional parameter. Thus, the user can easily apply the trajectory planning algorithm for various tasks with different requirements on motion efficiency and cruise proportion. Moreover, it is shown that feasible trajectories are computed more quickly than optimal trajectories. Rigorous mathematical analysis and proofs are presented for those aforementioned theoretical results. Comparative simulations and experimental results on an omnidirectional wheeled mobile robot demonstrate that flexible tunings between the cruise and time-optimal motions can be achieved in a higher computational efficiency manner by the proposed algorithm. Note to Practitioners —This article is motivated by the time-optimal and smooth motion planning problem for mobile robots along given paths. Existing approaches generally use the piecewise polynomial interpolations to smoothen and adjust feasible trajectories. This article proposes a novel path-constrained trajectory planning approach, which preserves properties of completeness and a high-efficient tradeoff mechanism between the optimal and cruise motions when achieving a globally optimal and acceleration-continuous trajectory. Comparative experimental results with other methods show the effectiveness of the proposed approach. In future research, we will attempt to integrate the proposed approach with typical path planning methods to achieve a complete and high-efficient motion planning framework.

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and convergence speeds than backpropagation training method. Moreover, the propose adaptive PID approach includes a back-calculation anti-windup scheme to deal with windup effects, which is a common problem in PID controllers. The performance of the proposed approach is shown by presenting both simulation and experimental tests, giving results that are comparable to similar and more complex implementations. Tests are performed for a four wheeled omnidirectional mobile robot. Tests show the superiority of the proposed adaptive PID controller over the conventional PID and other adaptive neural PID approaches. Experimental tests are performed on a KUKA® Youbot® omnidirectional platform.

Indoor Navigation System of Omni-Directional Mobile Robot Based on Static Obstacles Avoidance

This paper introduces and establishes an autonomous indoor navigation guidance system for the Omni-directional mobile robot along with a static obstacles avoidance. The mobile robot is fitted with a Kinect camera as the key sensor to operate in an indoor environment. The proposed navigation guidance system has three main functions; target searching for actions, avoidance of collision, and position. The target searching for actions uses fuzzy Proportional-Derivative (PD) controller to adjust the robot-heading angle to the target point. The collision avoidance consists of a controller that is designed to provide the robot with the proper heading angle to avoid collision with obstacles. Concerning the confinement, two strategies for position are implemented. The primary strategy uses encoders and compass sensor as a feedback signal, while the second strategy uses Kinect sensor in a combination with odometry to distinguish landmarks spots and to right robot position as required. An in-house built three wheeled Omni-directional mobile robot platform is designed to validate the proposed navigation guidance system. The results demonstrate the robot platform ability to navigate to its target autonomously with a minimal error, and to avoid collision with static obstacles.

Design of controllers for omnidirectional robot based on the ystem identification technique for trajectory tracking

Omnidirectional wheeled mobile robots are gaining a lot of importance owing to its widespread applications in healthcare, industries and various other service sectors. Most of these applications demand the mobile base to track the desired trajectory with the desired speed, optimal time and energy consumption. This work focusses on the trajectory tracking of the four-wheeled mobile robot with omnidirectional wheels. Most of the existing works make use of the models developed using first principles but in this work, system modeling is carried out by the system identification technique using the data obtained from the fabricated mobile base. Trajectory tracking is then carried out using PID and Linear Quadratic Tracking (LQT) based controllers. The simulation results are compared for tracking errors and energy consumption and the results obtained in real time are presented.

Energy Aware Mission Planning for WMRs on Uneven Terrains

Field robotics applications typically require platforms to be deployed for extended periods of time, traversing large distances over often uneven terrains. To best leverage these platforms, it is important to know the energy costs of their operation, to maximise range and operating time. This is particularly the case for electrically powered robots, which share a single power source for actuation, sensing, and computation. In this paper we build upon our prior work—derivation of a physics-based power model for an omnidirectional wheeled mobile robot— to develop an energy-aware rough terrain motion planner, which enables efficient missions to be defined and carried out whilst considering the impact of the terrain slope and slippage characteristics on the energy cost of motion. We develop an effective pipeline for the generation of such missions on-the-fly, and compare the performance of the proposed energy-aware approach against a 3D Euclidean distance metric, experimentally verifying the increased energy efficiency of the resulting plans.

Tree-Search-Based Any-Time Time-Optimal Path-Constrained Trajectory Planning With Inadmissible Island Constraints

Time-optimal path-constrained trajectory planning is of significant importance for enhancing the work efficiency of robotic systems. In particular, when inadmissible island constraints are considered, existing approaches are typically offline. In order to solve the problem online, this paper proposes a heuristic tree-search-based any-time time-optimal trajectory planning algorithm to achieve incremental computation of feasible trajectories. In a limited planning period, the proposed algorithm iteratively generates feasible trajectories, as optimal as possible, until the period terminates. This any-time performance of the proposed algorithm ensures that feasible trajectories can be obtained in real time and even time-optimal trajectories can be obtained if the planning period is long enough for searching. Experimental results on active-casters-based omnidirectional wheeled mobile robots demonstrate the validity of the proposed algorithm.

Fuzzy Behavior-based Control of Three Wheeled Omnidirectional Mobile Robot

In this paper, a fuzzy behavior-based approach for a three wheeled omnidirectional mobile robot (TWOMR) navigation has been proposed. The robot has to track either static or dynamic target while avoiding either static or dynamic obstacles along its path. A simple controller design is adopted, and to do so, two fuzzy behaviors “Track the Target” and “Avoid Obstacles and Wall Following” are considered based on reduced rule bases (six and five rules respectively). This strategy employs a system of five ultrasonic sensors which provide the necessary information about obstacles in the environment. Simulation platform was designed to demonstrate the effectiveness of the proposed approach.

Image Based Visual Servoing for Omnidirectional Wheeled Mobile Robots in Voltage Mode

This paper presents a new image based visual servoing (IBVS) control scheme for omnidirectional wheeled mobile robots with four swedish wheels. The contribution is the proposal of a scheme that consider the overall dynamic of the system; this means, we put together mechanical and electrical dynamics. The actuators are direct current (DC) motors, which imply that the system input signals are armature voltage applied to DC motors. In our control scheme the PD control law and eye-to-hand camera configuration are used to compute the armature voltages and to measure system states, respectively. Stability proof is performed via Lypunov direct method and LaSalle's invariance principle. Simulation and experimental results were performed in order to validate the theoretical proposal and to show the good performance of the posture errors.