Omnidirectional Mobile Robot Research Articles

Robust Output Regulation for a Constrained Omnidirectional Mobile Robot

This paper aims to design a robust output control to regulate the output for an uncertain omnidirectional mobile robot with input saturation and state constraints, in the presence of external disturbances. The proposed robust control approach is composed of a linear control part, that is designed based on the attractive ellipsoid method and a barrier Lyapunov function approach, taking into account the input and state constraints, and a nonlinear control part that can compensate the effect of matched disturbances. The synthesis of the robust control is given in terms of linear matrix inequalities. Simulation results show the workability of the proposed robust control approach.

Optimal PID Control of a Brushed DC Motor with an Embedded Low-Cost Magnetic Quadrature Encoder for Improved Step Overshoot and Undershoot Responses in a Mobile Robot Application.

The development of a proportional–integral–derivative (PID) control system is a simple, practical, highly effective method used to control the angular rotational velocity of electric motors. This paper describes the optimization of the PID control of a brushed DC motor (BDCM) with an embedded low-cost magnetic quadrature encoder. This paper demonstrates empirically that the feedback provided by low-cost magnetic encoders produces some inaccuracies and control artifacts that are not usually considered in simulations, proposing a practical optimization approach in order to improve the step overshoot and undershoot controller response. This optimization approach is responsible for the motion performances of a human-sized omnidirectional mobile robot using three motorized omnidirectional wheels.

Swarm control for large-scale omnidirectional mobile robots within incremental behavior

In this paper, the swarm control for large-scale omnidirectional mobile robots (OMRs) within incremental behavior is proposed to imitate the confluence behavior of animals during migration. In previous work, the number of OMRs in the swarm system was small and immutable. As such, the system lacked flexibility for swarm systems in practical applications. To solve these problems, we make several innovations. Firstly, OMRs within incremental behavior are proposed. Based on this, the incremental system of OMRs within incremental behavior is designed when the original swarm system needs assistance to form an incremental swarm system, which allows the incremental behavior happens among different swarm systems and the formation of each incremental system unchanged. Notably, incremental updating method based on second-order communication topology is proposed to update the adjacency matrix and the state matrix instead of creating a new swarm system. Then, to solve the pressure caused by the increasing number of OMRs in incremental swarm systems on calculating and storage, the incremental swarm system of large-scale OMRs based on second-order communication topology is introduced to rank the system and weaken the strong coupling relationship. In this case, the swarm control for a large-scale incremental swarm system is proposed through the backstepping method. The Lyapunov function is designed to prove the stability of the proposed controller. The simulation results verify the effectiveness of the proposed controller.

Conditional Disturbance Negation Based Control for an Omnidirectional Mobile Robot: An Energy Perspective

Disturbances widely exist in all control systems. Disturbance observers are commonly employed to estimate the disturbances which are often fully compensated in the control signal. However, one rarely recognized fact is that disturbances may be beneficial to the control performances and thus can be effectively exploited. In this letter, from an energy balance point, a conditional disturbance negation (CDN) control scheme is designed for an omnidirectional mobile robot, yielding a physically interpretable design. By energy analysis, the forces imposed on the robot are classified into workless force, internal damping force, and lumped disturbance force, wherein the later two are effectively exploited. A judgment function is designed to determine whether the disturbance is beneficial to the control performance, allowing the controller to selectively compensate the estimated disturbances. Convergence of the proposed control system is analyzed. Finally, both simulations and experimental results show that the proposed control scheme can achieve better performance against conventional full disturbance compensation control scheme, if the disturbances contain beneficial parts.

A T-S Fuzzy Quaternion-Value Neural Network-Based Data-Driven Generalized Predictive Control Scheme for Mecanum Mobile Robot

Four-mecanum-wheeled omnidirectional mobile robots (FMOMR) are widely used in many practical scenarios because of their high mobility and flexibility. However, the performance of trajectory tracking would be degenerated largely due to various reasons. To deal with this issue, this paper proposes a data-driven algorithm by using the T-S fuzzy quaternion-value neural network (TSFQVNN). TSFQVNN is tailored to obtain the controlled autoregressive integral moving average (CARIMA) model, and then the generalized predictive controller (GPC) is designed based on the CARIMA model. In this way, the spatial relationship between the three-dimensional pose coordinates can be preserved and training times can be reduced. Furthermore, the convergence of the proposed algorithm is verified by the Stone–Weierstrass theorem, and the convergence conditions of the algorithm are discussed. Finally, the proposed control scheme is applied to the three-dimensional (3D) trajectory tracking problem on the arc surface, and the simulation results prove the necessity and feasibility of the algorithm.

A rapidly-exploring random trees approach to combined task and motion planning

Task and motion planning in robotics are typically addressed by separated intertwined methods. Task planners generate abstract high-level actions to be executed, while motion planners provide the associated discrete movements in the configuration space satisfying kinodynamic constraints. However, these two planning processes are strictly dependent, therefore the problem of combining task and motion planning with a uniform approach is very relevant. In this work, we tackle this issue by proposing a RRT-based method that addresses combined task and motion planning. Our approach relies on a combined metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and feasible movements in the configuration space. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in navigation and transportation tasks.

Tracking a Mobile Robot in a UWB-sensor Grid

This work presents the development of a tracking system for a three-wheeled omnidirectional mobile robot. This kind of robot can perform rotation and translation in any pose. The arc-length segment described by the omni-wheels controls the trajectory of the robot. We present a simplified kinematic model in state-space. The observation system is based on an UltraWide Band ranging sensor. Finally made the state estimation using some classic positioning algorithms and compare the results against Finite Impulse Response Filter state estimators.

Adaptive Swarm Control Within Saturated Input Based on Nonlinear Coupling Degree

In this article, an adaptive swarm control within saturated input based on the nonlinear coupling degree is proposed. Swarm control is a forward study in kinematics and dynamics of the swarm system. However, the coupling degree in the previous work can only manifest whether the agents in the swarm being connected or not, ignoring the connection strength. As a result, the nonlinear coupling degree is proposed, which is more suitable for practical engineering than the previous coupling degree. Based on the nonlinear coupling degree, we put forward novel swarm kinematics and dynamics. Besides, the effects of input saturation and nonlinear dynamics should be considered for this novel swarm control based on the nonlinear coupling degree. Therefore, we introduce the backstepping method to design an adaptive swarm controller. With this controller, the input saturation auxiliary system is designed to reduce the effects of input saturation, and a radial basis function neural network (RBF-NN) is introduced to approximate the nonlinear dynamics. To overcome the differential explosion in the backstepping method, a command filter is put forward to reduce the amount of calculation and reduces the difficulty of the controller design. It is proved that the proposed controller ensures stability based on the Lyapunov stability theory. Finally, the simulation results of a multiagent system composed of six omnidirectional mobile robots illustrate the validity of the proposed controller.

Decoupled Fractional Supertwisting Stabilization of Interconnected Mobile Robot Under Harsh Terrain Conditions

The four-wheel omnidirectional mobile robot usually suffers disturbed or unstable lateral motion under harsh terrain conditions (such as uneven or oiled ground). Generally for such a challenging situation, the lumped disturbances and interconnected states render available coupling solutions difficult to achieve demand-satisfied performance. This article proposes a novel decoupled fractional supertwisting sliding mode control (FST-SMC) method by i) constructing an inverse system-based decoupling to form a pseudolinear composition system; ii) presenting an enhanced nominal sliding law for chattering mitigation, and iii) designing an unbiased multilayer fuzzy estimator with gain-learning capacity to compensate for the lumped disturbances actively. Given that the identified disturbances can be directly reflected in the FST-SMC law, this method guarantees an accurate and robust control without causing gain overestimation. Theoretical analysis is offered to verify the asymptotic stability. Under harsh terrain conditions, experimental results validate the effectiveness of the proposed FST-SMC method.

A universal noise-suppressing neural algorithm framework aided with nonconvex activation function for time-varying quadratic programming problems

Time-varying quadratic programming problems (TVQPPs) subject to equality and inequality constraints with different measurement noises often arise in the fields of scientific and economic research. Noises are always ubiquitous and unavoidable in practical problems. However, most existing methods usually assume that the computing process is free of measurement noise or the denoising has been monitored before the calculation. The zeroing neural network (ZNN), a significant neurodynamic approach, has presented potent abilities to compute a great variety of time-varying zeroing problems with odd monotonically increasing activation functions, but the existing results on ZNN cannot deal with the inequality constraints in the TVQPPs. In this article, a nonconvex function activated implicit ZNN (NIZNN) model and a noise-suppressing implicit ZNN (NNSIZNN) model, which is also known as the ZNN-based model, are proposed by the inspiration of the conventional ZNN model from a control-based framework. It guarantees an effective solution for TVQPPs in the presence of different noises. The ZNN-based model allows nonconvex sets for projection operation in activation functions and incorporates noise-tolerant techniques for handling different noises arising in TVQPPs. Besides, theoretical analyses show that the ZNN-based model globally converges to the time-varying optimization solution of TVQPPs in the presence of noises. In addition, an illustrative example is provided to substantiate the efficiency and robustness of the ZNN-based model for online solving TVQPPs with inherent tolerance to noises. Moreover, the ZNN-based model is applied to the economic model, which provides and investigates their computational efficiency and superiority. Finally, an application example to repetitive motion generation of four-wheel omnidirectional mobile robot is simulated to substantiate the feasibility and superiority of the developed NNSIZNN model for online solving TVQPPs with nonconvex constraints and measurement noise.

PID Control Schematic Design for Omni-directional wheel mobile robot Cilacap State of Polytechnic

Omni-directional mobile robot (OMR) is a robot that can move in all directions with an additional wheel around the core wheel. This research presents a PID controller method at the OMR plant with the specific purpose of getting to the specified point. OMR uses three wheels with an angle difference of 120 degrees. The application of this method using MATLAB assistance from knowing the kinematics to the performance results from the application of the control method. The stages of this research are robot design, component selection, electrical design of the robot, determination of forward kinematic and reverse kinematic and determination of PID controller for positioning. Testing is done by determining the position and determining the point to be determined. Simulation time testing is when the state is at 3,6,9,12 and 15 seconds. The results of the simulation robot can follow the specified coordinates.

Wheel Arrangement of Four Omni Wheel Mobile Robot for Compactness

Compact omnidirectional mobile robots are required to automate transportation of raw materials, products, etc. within an industrial plant. This paper focuses on omni wheel robots with low vibration and wheel arrangements that contribute to compactness. Due to its wheels’ configuration, our proposed compact robot may have different sensitivity to noise (controller) and different performance (errors) when following a predetermined path, compared to conventional ones. Using a simple DC motor, a robot with the proposed arrangement and a conventional robot run along a predetermined path. A linear–quadratic regulator that is processed lightly is used to control the robots for practicality. As a result, the robot’s trajectory in the proposed arrangement showed a distortion different from that of the conventional type. The distortion of the trajectory was attributed to the inability of the DC motor to rotate stably at low speed. The different distortions exhibited suggest that the wheel arrangement changes the effect of imperfect control on the robot’s motion. In addition, the proposed arrangement showed the possibility of being suitable for a transport robot because the wheels are placed in the four corners of the robot, facing forward, backward, left, and right.

An Improved Residual-Based Detection Method for Stealthy Anomalies on Mobile Robots

With the expansion of the cyber-physical system (CPS) application area, its importance has become more and more prominent. As one of the typical applications of CPS, the anomaly detections of mobile robots have attracted the attention of all parties. As part of the CPS, mobile robots face the problem that conventional residual-based detection methods cannot identify stealthy anomalies. The conventional residual-based detection methods mainly use the residual signal calculated from the control signal and measure output for detection, which is widely used in fault diagnosis. Still, it is difficult to be useful in deceptive stealthy anomalies purposefully imposed on mobile robots, which are designed to evade the conventional detections by tampering with measure output. Furthermore, they can control the system to deviate from the expected operations, causing degradation of control performance or even damage without being detected. Based on this, by analyzing the system model of CPS and the stealthy conditions of anomalies, the improved residual-based detection method is proposed in this paper. Moreover, three stealthy anomalies purposefully imposed on an omnidirectional mobile robot (OMR) are detected by using the conventional residual-based methods and the improved residual-based method. Finally, the experimental results show that the method proposed can effectively detect the stealthy anomalies purposefully imposed on the OMR.

Active Disturbance Rejection Control of Euler-Lagrange Systems Exploiting Internal Damping.

Active disturbance rejection control (ADRC) is an efficient control technique to accommodate both internal uncertainties and external disturbances. In the typical ADRC framework, however, the design philosophy is to "force" the system dynamics into a double-integral form by an extended state observer (ESO) and then the controller is designed. Especially, the systems' physical structure has been neglected in such a design paradigm. In this article, a new ADRC framework is proposed by incorporating at a fundamental level the physical structure of the Euler-Lagrange (EL) systems. In particular, the differential feedback gain can be selected considerably small or even 0, due to the effective exploitation of the system's internal damping. The design principle stems from an analysis of the energy balance of EL systems, yielding a physically interpretable design. Moreover, the exploitation of the system's internal damping is thoroughly discussed, which is of practical significance for applications of the proposed design. Besides, a sliding-mode ESO is designed to improve the estimation performance over traditional linear ESO. Finally, the proposed control framework is illustrated through tracking control of an omnidirectional mobile robot. Extensive experimental tests are conducted to verify the proposed design as well as the discussions.

The Development of a Visual Tracking System for a Drone to Follow an Omnidirectional Mobile Robot

This research aims to develop a visual tracking system for a UAV which guides a drone to track a mobile robot and accurately land on it when it stops moving. Two LEDs with different colors were installed on the bottom of the drone. The visual tracking system on the mobile robot can detect the heading angle and the distance between the drone and mobile robot. The heading angle and flight velocity in the pitch and roll direction of the drone were modified by PID control, so that the flying speed and angle are more accurate, and the drone can land quickly. The PID tuning parameters were also adjusted according to the height of the drone. The embedded system on the mobile robot, which is equipped with Linux Ubuntu and processes images with OpenCV, can send the control command (SDK 2.0) to the Tello EDU drone through WIFI with UDP Protocol. The drone can auto-track the mobile robot. After the mobile robot stops, the drone can land on the top of the mobile robot. From the experimental results, the drone can take off from the top of the mobile robot, visually track the mobile robot, and finally land on the top of the mobile robot accurately.

Trajectory tracking of redundantly actuated mobile robot by MPC velocity control under steering strategy constraint

Four-wheel independent-steering redundantly-actuated omnidirectional mobile robot (FIR-OMR) has attracted wide attention for its excellent motion performance such as multi-mode steering and flexible attitude adjustment. However, for the 8-drive redundant and strong nonlinear system of FIR-OMR, the complexity and time-consuming of model solutions make it hard for the embedded system to achieve fast and efficient trajectory tracking. To copy with that, an optimal velocity model predictive control under steering strategy constraint (USSC-MPC) is presented, which is combined with pose correction by local micro error (LME-PC) for independent wheel angle feedback control. Specifically, the steering modes of FIR-OMR are divided into fixed independent wheel steering (IWS), zero-angle pure differential steering (DS), and Ackerman steering (AS), which is ultimately selected by steering fuzzy selector (SFS). Then a steer-based model prediction controller is designed to optimize the wheel velocity and the micro-error feedback wheel angle of pose correction pointing to the desired point is calculated by FIR-OMR in real-time. As a result, by controlling the wheel velocity and wheel angle, a satisfactory path tracking control effect can be obtained. In our proposed method, the USSC-MPC ensures the optimality of wheel velocity, and the LME-PC can achieve efficient trajectory tracking with a faster error convergence rate. Through simulations and experiments, complex motions such as multi-section paths and side parking can be realized, which verifies the feasibility and superiority of our method.

Systematic Odometry Error Evaluation and Correction in a Human-Sized Three-Wheeled Omnidirectional Mobile Robot Using Flower-Shaped Calibration Trajectories

Odometry is a simple and practical method that provides a periodic real-time estimation of the relative displacement of a mobile robot based on the measurement of the angular rotational speed of its wheels. The main disadvantage of odometry is its unbounded accumulation of errors, a factor that reduces the accuracy of the estimation of the absolute position and orientation of a mobile robot. This paper proposes a general procedure to evaluate and correct the systematic odometry errors of a human-sized three-wheeled omnidirectional mobile robot designed as a versatile personal assistant tool. The correction procedure is based on the definition of 36 individual calibration trajectories which together depict a flower-shaped figure, on the measurement of the odometry and ground truth trajectory of each calibration trajectory, and on the application of several strategies to iteratively adjust the effective value of the kinematic parameters of the mobile robot in order to match the estimated final position from these two trajectories. The results have shown an average improvement of 82.14% in the estimation of the final position and orientation of the mobile robot. Therefore, these results can be used for odometry calibration during the manufacturing of human-sized three-wheeled omnidirectional mobile robots.

Frictional forces and torques compensation based cascaded sliding-mode tracking control for an uncertain omnidirectional mobile robot

The work presented here deals with the trajectory-tracking control of omnidirectional mobile robots using a novel robust control technique. The aim is to achieve good tracking performance in the presence of frictional forces and torques, uncertainties, and saturated control inputs. First, a dynamic model of the omnidirectional mobile robot incorporating motor dynamics and frictional forces and torques is derived leading to a higher order system model. Subsequently, a novel cascaded sliding-mode tracking control scheme is proposed to perform trajectory-tracking with the omnidirectional mobile robot, which includes direct and indirect sliding-mode controllers in a cascaded structure. The indirect sliding-mode controller is utilized to attain tracking of the indirect reference input (i.e., desired trajectory) in the first subsystem and constitutes a direct reference input for the second one. A direct sliding-mode controller (i.e., actual control) is then designed to achieve tracking of the direct reference input. This way, the indirect reference input can be effectively manipulated through the proposed cascaded sliding-mode tracking control and individual subsystems are guaranteed to have not only Uniform Ultimate Boundedness but also asymptotic convergent performance. The stability of the closed-loop system is demonstrated via the Lyapunov stability criteria and validated through simulations. It is shown that the cascaded sliding-mode tracking control leads to good trajectory-tracking performance in the presence of uncertainties, friction, and saturated control input.

An LSTM‐based approach to precise landing of a UAV on a moving platform

AbstractA machine learning‐based method for the precise landing of an unmanned aerial vehicle on a moving mobile platform is proposed. The proposed approach attempts to predict the mobile platform's future trajectory based on the past states of the mobile platform. To that end, it combines a long short‐term memory‐based neural network with a Kalman filter. Hence, it aims at combining the advantages of a machine learning method with those of a state estimation method from established control theory. Based on the predicted trajectory, the unmanned aerial vehicle attempts to land precisely on the moving mobile platform. The experiment is conducted in the Gazebo simulation platform with a quadrotor and an omnidirectional mobile robot, and the proposed method is compared with the single‐method approaches of using only either the Kalman filter or the machine learning method alone.

Disinfecting Omnidirectional Mobile Robot with Vision Capabilities

Disinfecting Omnidirectional Mobile Robot with Vision Capabilities by Waqas Qaisar * , Muhammad Tanveer Riaz , Abdul Basit, Yasir Naseem, Zohaib Nazir Department of Mechanical, Mechatronics and Manufacturing Engineering, University of Engineering and Technology Lahore, Faisalabad Campus, Faisalabad, 38000, Pakistan * Author to whom correspondence should be addressed. Journal of Engineering Research and Sciences,