Control Of Mobile Robot Research Articles

A Leader-follower formation control of mobile robots by position-based visual servo method using fisheye camera

This paper presents a leader-follower formation control of multiple mobile robots by position-based method using a fisheye camera. A fisheye camera has a wide field of view and recognizes a wide range of objects. In this paper, the fisheye camera is first modeled on spherical coordinates and then a position estimation technique is proposed by using an AR marker based on the spherical model. This paper furthermore presents a method for estimating the velocity of a leader robot based on a disturbance observer using the obtained position information. The proposed techniques are combined with a formation control based on the virtual structure. In this paper, the formation controller and velocity estimator can be designed independently, and the stability analysis of the total system is performed by using Lyapunov theorem. The effectiveness of the proposed method is demonstrated by simulation and experiments using two real mobile robots.

A novel saturated PID‐type observer‐based controller for wheeled mobile robots with a guaranteed performance considering path curvature

AbstractThis article proposes a novel saturated PID‐type formation controller for a class of nonholonomic wheeled mobile robots (WMRs) of type (m, s) with mobility m and steerability s. Since the path curvature causes a severe deviation of the followers from the leaders' trajectory in the corners, a new definition of output equations is suggested to construct the coordinates of a virtual reference point in the front of each follower based on the concept of the extended look‐ahead control that utilizes a corrective angle to compensate the trajectory curvature efficiently. A nonlinear transformation and the robot's kinematic and dynamic equations are heuristically combined to develop a novel second‐order Euler–Lagrange formulation of unconstrained errors from constrained errors. Then, a novel saturated PID‐type controller is proposed for WMRs to build a desirable formation with a prescribed performance. An innovative nonlinear observer is presented to estimate followers' velocities. An efficient mixture of a radial basis function neural network (RBFNN) and an adaptive mechanism is proposed to compensate for model uncertainties, external disturbances, and network approximation errors. Lyapunov's direct method verifies semi‐global uniform ultimate boundedness stability by estimating a gain‐dependent region of attraction. Numerical simulations finally show the efficiency of the proposed controller.

Cooperative game-oriented optimal robust control for an uncertain wheeled mobile robot with position constraints

The trajectory tracking control performance of the wheeled mobile robot (WMR) is subject to position constraints, system uncertainties, and external disturbances. This paper explores the mechanism of the position constraints (equality and inequality) and considers the system uncertainties to be time-varying but bounded with fuzzy prescribed bounds. To solve the position constraints and uncertainties problems, a cooperative game-oriented optimal robust control method is proposed to provide high-precision, fast-response performance and robustness for WMR. First, a creative diffeomorphism transformation is proposed to convert the bounded state variables (subject to inequality constraints) into unbounded state variables. Accordingly, a restructured WMR system with the new state variables is established, which is free from inequality constraints. The uncertainties’ bound is described as a fuzzy number by employing fuzzy set theory, which builds the fuzzy dynamic system of the WMR. Second, the control goal for this study is to drive the WMR to follow the position constraints despite uncertainties. An optimal robust control is developed to guarantee that the tracking errors of the WMR converge to a predefined neighborhood of origin. Using Lyapunov stability analysis, a fuzzy performance index is constructed, including steady-state performance, time performance, and control effort. The fuzzy performance index is employed as the cost function based on the cooperative game theory. The optimal control design is formulated to seek the Pareto-optimal solution of control parameters. Finally, the stability analysis and comparative experiments demonstrate that the proposed control algorithm can ensure the WMR’s superior trajectory tracking convergence performance and strong robustness against uncertainties/disturbances.

Motion planning and control of an autonomous mobile robot

The application of autonomous mobile robots (AMRs) has gradually become crucial in smart factories due to the advantages of improving production efficiency and reducing labour costs. Motion planning has been a key part of AMR control development. This paper presents motion planning and position tracking control systems of an omnidirectional wheel AMR powered by a hybrid fuel cell and battery power source. First, the kinematical and dynamic models of the AMR are introduced. The navigation system comprises three loops, with the first loop being motor control, the second loop being position tracking control, and a motion planning layer. The position data of the AMR for feedback control is obtained through sensor fusion of data from the inertial measurement unit (IMU) sensor, encoder sensor, and ranging sensor with simultaneous localisation and mapping (SLAM) algorithm. The motion planning is then applied to obtain an optimal path with the shortest distance and collision-free movement. In addition, the tracking algorithm is designed to drive the AMR to follow the optimal path and achieve high accuracy. The experimental results show a 30% improvement in tracking accuracy compared to traditional approaches and 8 hours of continuous working, which is promising for industrial applications, and the results are satisfactory in terms of both accuracy and efficiency requirements.

Differential-drive Mobile Robot Controller with ROS 2 Support

Los robots conocidos con el nombre de AMR (Autonomous Mobile Robots) se utilizan en la logística interna en muchos tipos de industrias y sectores de la producción. Este tipo de robots sustituyen a los tradicionales AGVs (Automated Guided Vehicles) en los cuales el camino a seguir está definido previamente y no tienen la capacidad de elegir un camino diferente. Por otro lado, los AMRs resultan más flexibles, seguros y precisos, debido a la incorporación de tecnologías que hasta hace poco estaban reservadas al ámbito de la investigación, tales como: navegación autónoma, sistemas de visión por computadoras, tecnología de SLAM (Simultaneous Localization and Mapping), entre otras. Muchas de estas tecnologías se implementan utilizando ROS (Robot Operating System). ROS es un conjunto de bibliotecas de software y herramientas de código abierto y libre para el desarrollo de aplicaciones de robots, cuya nueva versión ROS 2 tiene como uno de sus objetivos ser aplicable a entornos de producción. El presente trabajo describe el desarrollo de un controlador para robots de tracción diferencial tipo AMR con soporte para ROS 2 utilizando la implementación para sistemas embebidos micro-ROS. Este controlador es la evolución de una versión anterior utilizada en diferentes robots por más de 10 años en el CIII (UTN). Vale aclarar que este trabajo está enfocado principalmente en el desarrollo de hardware. Sin embargo, se han realizado algunas pruebas preliminares de software, principalmente, para evaluar el correcto funcionamiento del controlador de tracción diferencial. En primer lugar, se definen los requerimientos de diseño y se selecciona un microcontrolador con soporte nativo para micro-ROS. Luego se describe el desarrollo de cada etapa del controlador, tales como: la alimentación, la comunicación USB, el sensado de tensión de batería, el puerto de depuración y el diseño final del PCB. Por último, se hace mención a las pruebas iniciales de software que permiten verificar el correcto funcionamiento del controlador y las mejoras respecto a la versión anterior.

A novel hedge algebra formation control for mobile robots

Hedge algebra is gaining increasing attention due to its high efficiency and fast calculation speed compared to fuzzy logic. This paper proposes the first algebra-based formation controller with a leader–follower approach to maintain the desired formation of multiple robots. In each time step, the controller computes the desired positions of the followers and strives to minimize the distance between them and their desired positions. We evaluate the performance of this controller on our robots using ROS in a virtual environment with Gazebo. Our work proves hedge algebra’s significant potential in the mobile robots’ domain, specifically in formation control. HAFC allows not only the precise formation of a robot swarm into any desired formation, with minimal deviation but also adaptive transition to the different formations. Our results show that HAFC can effectively maintain the desired formation of multiple robots, even in certain extraordinary situations. Furthermore, HAFC exhibits higher efficiency than the fuzzy logic-based controller, as evidenced by its faster adaptation rate and shorter computation time. The proposed approach has the potential to be applied in various real-world scenarios, such as surveillance, transportation, and exploration, where the robot has to move in formation and change formation accordingly with the terrain.

A Novel Approach for Target Attraction and Obstacle Avoidance of a Mobile Robot in Unknown Environments Using a Customized Spiking Neural Network

In recent years, implementing reinforcement learning in autonomous mobile robots (AMRs) has become challenging. Traditional methods face complex trials, long convergence times, and high computational requirements. This paper introduces an innovative strategy using a customized spiking neural network (SNN) for autonomous learning and control of mobile robots (AMR) in unknown environments. The model combines spike-timing-dependent plasticity (STDP) with dopamine modulation for learning. It utilizes the Izhikevich neuron model, leading to biologically inspired and computationally efficient control systems that adapt to changing environments. The performance of the model is evaluated in a simulated environment, replicating real-world scenarios with obstacles. In the initial training phase, the model faces significant challenges. Integrating brain-inspired learning, dopamine, and the Izhikevich neuron model adds complexity. The model achieves an accuracy rate of 33% in reaching its target during this phase. Collisions with obstacles occur 67% of the time, indicating the struggle of the model to adapt to complex obstacles. However, the model’s performance improves as the study progresses to the testing phase after the robot has learned. Its accuracy surges to 94% when reaching the target, and collisions with obstacles reduce it to 6%. This shift demonstrates the adaptability and problem-solving capabilities of the model in the simulated environment, making it more competent for real-world applications.

Event-Triggered Model-Free Adaptive Control for Wheeled Mobile Robot With Time Delay and External Disturbance Based on Discrete-Time Extended State Observer

Abstract In this paper, an improved model-free adaptive control strategy is proposed for the trajectory tracking problem of the wheeled mobile robot (WMR) with time-delay and bounded disturbance. Firstly, the original nonlinear time delay system is transformed into a data model by applying the full-form dynamic linearization method (FFDL). Secondly, the discrete-time extended state observer (DESO) is applied to estimate the unknown residual nonlinear time-varying term. A full-form dynamic linearization model-free adaptive control scheme based on discrete-time extended state observer (DESO-based FFDL MFAC) is proposed. In addition, a full-form dynamic linearization event-triggered model-free adaptive control based on discrete-time extended state observer (DESO-based FFDL ET-MFAC) is established by designing an event-triggering condition to assure Lyapunov stability. The control input signal is updated only if the system indicator meets the provided event-triggering condition; otherwise, the control input remains unchanged which can address limited communication bandwidth effectively. Finally, the effectiveness of the proposed method is verified by simulation.

Backstepping Controller for Mobile Robot in Presence of Disturbances and Uncertainties

The objective of this work is to devise an effective control system for addressing the trajectory tracking challenge in nonholonomic mobile robots. Two primary control approaches, namely kinematic and dynamic strategies, are explored to achieve this goal. In the kinematic control domain, a backstepping controller (BSC) is introduced as the core element of the control system. The BSC is utilized to guide the mobile robot along the desired trajectory, leveraging the robot’s kinematic model. To address the limitations of the kinematic control approach, a dynamic control strategy is proposed, incorporating the dynamic parameters of the robot. This dynamic control ensures real-time control of the mobile robot. To ensure the stability of the control system, the Lyapunov stability theory is employed, providing a rigorous framework for analyzing and proving stability. Additionally, to optimize the performance of the control system, a genetic algorithm is employed to design an optimal control law. The effectiveness of the developed control approach is demonstrated through simulation results. These results showcase the enhanced performance and efficiency achieved by the proposed control strategies. Overall, this study presents a comprehensive and robust approach for trajectory tracking in nonholonomic mobile robots, combining kinematic and dynamic control strategies while ensuring stability and performance optimization.

Event-Triggered Integral Formation Controller for Networked Nonholonomic Mobile Robots: Theory and Experiment

Event-Triggered Integral Formation Controller for Networked Nonholonomic Mobile Robots: Theory and Experiment

Hybrid Dynamic Event-Triggered Tracking Control of Wheeled Mobile Robots Against Stochastic DoS Attacks

Hybrid Dynamic Event-Triggered Tracking Control of Wheeled Mobile Robots Against Stochastic DoS Attacks

Dynamic surface control for omnidirectional mobile robot with full state constrains and input saturation

Dynamic surface control for omnidirectional mobile robot with full state constrains and input saturation

Concurrent Learning-Based Neuroadaptive Robust Tracking Control of Wheeled Mobile Robot: An Event-Triggered Design

In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number of control computations while minimizing network resources without sacrificing the stability of the sampled data closed-loop control system. A finite sampling frequency is guaranteed for the designed triggering condition by establishing a positive lower bound on the inter-event execution time which is equivalent to the Zeno-free behaviour of the system. Finally, the proposed event-based neuro-adaptive robust controller is implemented on a practical system (Q-bot 2e) to show the effectiveness of the proposed design.

Event-Based Wireless Tracking Control for a Wheeled Mobile Robot Against Reactive Jamming Attacks

In this paper, an event-based defense strategy is studied for a wireless remote tracking control system of a wheeled mobile robot suffering from reactive jamming attacks. First, utility functions of both the reactive jammer and control center are established based on the signal to interference plus noise ratio and residual energies. Then, interactions between the reactive jammer and control center are described through a Stackelberg game scheme, for which the existence and uniqueness of the game equilibrium solution are guaranteed. To defend this type of attacks, an event-driven communication scheme is proposed to save transmission energies at the control center. By making full use of the saved energies, a retransmission mechanism is employed to improve the reliability of the communication. Meanwhile, the unavailable data are estimated for both the robot and control center by proposing a model-based estimation method. With the combination of the event-driven communication scheme, the retransmission mechanism and the estimation method, an event-based defense strategy is established. It can be shown that the states of tracking error system under the reactive jamming attacks is guaranteed to be stochastically ultimately bounded (SUB). Finally, the efficiency of the proposed scheme is verified by simulation results.

Autonomous and smart cleaning mobile robot system to improve the maintenance efficiency of solar photovoltaic array

A solar photovoltaic (PV) array is part of a PV power plant as a generation unit. PV array that are usually placed on top of buildings or the ground will be very susceptible to dirt and dust. Thus, this dirt and dust will be able to reduce the performance and work efficiency of the generation unit. Cleaning PV arrays by manpower requires high effort, cost, and risk, especially in higher location. This study presents the design of a mobile robot that is used to replace human labor to clean PV arrays. That way, the PV array maintenance steps can reduce operational costs and risks. This intelligent controlled mobile robot can maneuver safely and efficiently over PV arrays. gyroscope and proximity sensors are used to detect and follow the sweep path over the entire PV array area. Proportional integral derivative (PID) control test makes the robot can stabilize in about 5.72 seconds to keep on the track. The smart PV cleaning robot has average operation time about 13 minutes in autonomous mode and 20-24 minutes in manual mode. The operation of the robot is effective to give more efficiency on the use of energy, time, and maintenance costs of PV array system.

Formation control of nonholonomic wheeled mobile robots using adaptive distributed fractional order fast terminal sliding mode control

Formation control of nonholonomic wheeled mobile robots using adaptive distributed fractional order fast terminal sliding mode control

Adaptive fault-tolerant formation tracking control of networked mobile robots with input delays

This paper addresses a fault-tolerant formation tracking problem for multiple mobile robots, where the dynamics of each robot is nonholonomic with time-varying input delays and actuator faults. The control problem is challenging since the actuator faults (e.g., loss of effectiveness and biased faults) are unknown in the time-delay multi-agent systems. To this end, an adaptive compensator is proposed for the uncertain time-delay systems induced by the loss of effectiveness faults. With the aid of neural networks and auxiliary variables, a novel adaptive fault-tolerant formation tracking controller for each robot is developed without any restrictions on the rate of their unknown variables induced by the biased faults. The uniformly ultimately boundness for the closed-loop system is derived according to the Lyapunov–Krasovskii functional. Numerical simulations verify the effectiveness of the proposed method.

Differential flatness-based adaptive robust tracking control for wheeled mobile robots with slippage disturbances

Wheeled mobile robots (WMRs) have a wide range of applications in logistics transportation and industrial productions, among which the motion control has always been one of the hot spots in the current WMR researches. However, most of previous designed controllers assumed that the WMR motion had no slippage. Ignoring the slippage factors usually results in a decrease in control performance and even leads to unstable motion. To address such a challenge, a kinematic model with differential flatness is established through dynamic feedback-linearization, which comprehensively considers the multidirectional slippage of mobile robot, including longitudinal and steering slippage. Subsequently, benefited from the one-to-one mapping of states and inputs to flat outputs in differential flat system, an adaptive robust control (ARC) method is proposed to stabilize the system. Different from previous robust control studies, even if the knowledge of the upper bound of system uncertainties is unknown in advance, the proposed adaptive robust controller can still achieve satisfying performance by adaptive estimation of the upper bound of system uncertainties. The effectiveness and feasibility of the proposed method are confirmed by comparative experiments on WMR with slippage disturbance.

Trajectory tracking control of nonholonomic wheeled mobile robots using only measurements for position and velocity

This paper studies the trajectory tracking control of nonholonomic wheeled mobile robots, and only measurements for Cartesian position and velocity are used. A full-order observer is proposed to estimate the unmeasurable attitude and filter the position signal. Its effectiveness relies on the persistent excitation (PE) condition of non-zero linear velocity. Using the observer states, we design a local tracking controller that guarantees: (a) the linear velocity input is positive, and (b) the position tracking error is convergent to zero locally asymptotically. Experiments performed on a robot built in-house illustrate the control algorithm.

Robustness in formation control of mobile robots using leader-follower method

This paper presents the robustness in formation control of multiple mobile robots using leader-follower method. The uncertainty considered is the measured error which is included in the relative state. The robust stability conditions against the relative state error are derived by Lyapunov’s stability theory (direct method). We also obtain the formation steady-state deviation when formation stability is ensured. The formation control environment is constructed on Simulink. The validity of the stability condition and the steady-state deviation is demonstrated by numerical simulation. It is seen that the L-F method provides a robust control law against the relative state error although large formation steady-state deviation is occurred in some cases.