Path Tracking Of Mobile Robot Research Articles

Multiple Nonholonomic Wheeled Mobile Robots Trajectory Tracking While Maintaining Time-Varying Formation via Synchronous Controller

A new synchronization control method is developed for multiple nonholonomic wheeled mobile robot path tracking while maintaining time-varying formations. Every robot is controlled to track its desired path while its movement is synchronized with nearby robots to maintain the desired time-varying formation. A new derivation for dynamic model of the nonholonomic wheeled mobile robot (WMR) is proposed based on the Lagrange methods. The robot model is divided into translational and rotational models, such that, each model will be controlled individually. Furthermore, synchronous controller for each robot's translation is developed to guarantee the asymptotic stability of both position and synchronization errors. In addition, an orientation controller is proposed to ensure that the robot is always oriented towards its desired position. The simulation results validate the effectiveness of the proposed synchronous controller in the formation control tasks.

Adaptive tracking and obstacle avoidance for a class of mobile robots in the presence of unknown skidding and slipping

An adaptive control approach is proposed for path tracking and obstacle avoidance of mobile robots considering unknown skidding and slipping. The proposed adaptive controller consisting of a kinematic controller and a torque controller for the dynamic model is derived to compensate the unknown skidding and slipping effect. From Lyapunov-stability analysis, it is proved regardless of unknown skidding and slipping that all signals of the controlled closed-loop system are semiglobally uniformly ultimately bounded, the point tracking errors converge to an adjustable neighbourhood of the origin outside the obstacle detection region and the obstacle avoidance is guaranteed inside the obstacle detection region. The performance and stability of the proposed control system are verified from simulation results.

Optimum synthesis of fuzzy logic controller for trajectory tracking by differential evolution

Differential Evolution (DE) and Genetic Algorithms (GA) are population based search algorithms that come under the category of evolutionary optimization techniques. In the present study, these evolutionary methods have been utilized to conduct the optimum design of a fuzzy controller for mobile robot trajectory tracking. Comparison between their performances has also been conducted. In this paper, we will present a fuzzy controller to the problem of mobile robot path tracking for a CEDRA rescue robot. After designing the fuzzy tracking controller, the membership functions will be optimized by evolutionary algorithms in order to obtain more acceptable results.

Iterative Path Tracking of an Omni-Directional Mobile Robot

We propose a path-tracking algorithm that is developed using an iterative learning control (ILC) technique and use the algorithm to control an omni-directional mobile robot. The proposed algorithm can be categorized as an open–closed PD-type ILC; it generates robot velocity commands by a PD-type ILC update rule using both previous and current information. When applied to the omni-directional mobile robot, it can decrease position errors and track the desired trajectory. Under the general problem setting that includes a mobile robot, we show that the proposed algorithm guarantees that the system states, outputs and control inputs converge to within small error bounds around the desired ones even under state disturbances, measurement noises and initial state errors. By using simulation and experimental tests, we demonstrate that the proposed algorithm converges fast to the desired path, and results in small root-mean-square (r.m.s.) position error under various surface conditions. The proposed algorithm shows better path-tracking performance than the conventional PID algorithm and achieves faster convergence and lower r.m.s. error than the existing two ILC algorithms.

Model algorithm control for path tracking of wheeled mobile robots

This paper proposes a model algorithm control (MAC) method for the path tracking control of differentially steered wheeled mobile robots (WMRs) subject to nonholonomic constraints. The continuous dynamic model of the wheeled mobile robot is presented and used as the model to be controlled. The MAC controller is designed based on the sampled-data representation of the system. In this paper the case that there exists time delay in the control input is also considered. A time discretization method using the Taylor series and the zero-order-hold (ZOH) assumption is proposed to discretize the continuous dynamic model of the WMR. This time discretization method is especially useful in the case of the time delayed system. It can provide finite dimensional and more accurate discretized form model of the mobile robot with input time delay and convert it into a general nonlinear time discretized form to which the MAC controller can be applied. Simulations are conducted to show the performance and feasibility of the proposed control strategy. In these simulations the WMR is controlled to track two difference reference paths such as the “8” shape path and the circular path. The bounded inertial parameters uncertainties and some disturbance are also considered in the model of the control system.

A Simple Adaptive Control Approach for Trajectory Tracking of Electrically Driven Nonholonomic Mobile Robots

Almost all existing controllers for nonholonomic mobile robots are designed without considering the actuator dynamics. This is because the presence of the actuator dynamics increases the complexity of the system dynamics, and makes difficult the design of the controller. In this paper, we propose a simple adaptive control approach for path tracking of uncertain nonholonomic mobile robots incorporating actuator dynamics. All parameters of robot kinematics, robot dynamics, and actuator dynamics are assumed to be uncertain. For the simple controller design, the dynamic surface control methodology is applied and extended to mobile robots that the number of inputs and outputs is different. We also adopt the adaptive control technique to treat all uncertainties and derive adaptation laws from the Lyapunov stability theory. Finally, simulation results demonstrate the effectiveness of the proposed controller.

Path Tracking Control for a Wheeled Mobile Robot

In this paper a model algorithm control (MAC) method is proposed to do the path tracking control of a wheeled mobile robot (WMR). This mobile robot is a three-wheel differentially steered wheeled mobile robot subject to nonholonomic constraints. The kinematic model of this mobile robot is presented and used as the mobile robot model to be controlled. Simulations are conducted to show the performance and feasibility of the proposed control strategy for the path tracking of a wheeled mobile robot.

A Study on an Adaptive Robust Fuzzy Controller with GAs for Path Tracking of a Wheeled Mobile Robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. Genetic algorithms are employed to optimize the fuzzy rules of FBFN. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm

This paper proposes a TSK-type recurrent neuro fuzzy system (TRNFS) and hybrid algorithm- GA_BPPSO to develop a direct adaptive control scheme for stable path tracking of mobile robots. The TRNFS is a modified model of the recurrent fuzzy neural network (RFNN) to obtain generalization and fast convergence. The TRNFS is designed using hybridization of genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO), called GA_BPPSO. For the tracking control of mobile robot, two TRNFSs are designed to generate the control inputs by direct adaptive control scheme and hybrid algorithm GA_BPPSO. Through simulation results, we demonstrate the effectiveness of our proposed controller.

Adaptive robust fuzzy control for path tracking of a wheeled mobile robot

This paper proposes an adaptive robust fuzzy control scheme for path tracking of a wheeled mobile robot with uncertainties. The robot dynamics including the actuator dynamics is considered in this work. The presented controller is composed of a fuzzy basis function network (FBFN) to approximate an unknown nonlinear function of the robot complete dynamics, an adaptive robust input to overcome the uncertainties, and a stabilizing control input. The stability and the convergence of the tracking errors are guaranteed using the Lyapunov stability theory. When the controller is designed, the different parameters for two actuator models in the dynamic equation are taken into account. The proposed control scheme does not require the accurate parameter values for the actuator parameters as well as the robot parameters. The validity and robustness of the proposed control scheme are demonstrated through computer simulations.

Stability of autonomous vehicle path tracking with pure delays in the control loop

This paper presents a new method to analyze the stability of a general class of mobile robot path-tracking algorithms taking into account explicitly the computation and communication delays in the control loop. These pure delays are present in autonomous vehicles due to position estimation. The problem is analyzed by solving directly the transcendental characteristic equation that appears when the time delay is considered. The analysis has been done for straight paths and paths of constant curvature. The method has been applied to the pure pursuit path-tracking algorithm, one of the most widely used. The paper presents several tests with two different outdoor autonomous vehicles (ROMEO-3R and a computer controlled HMMWV), in spite of difficulties of practical experiments with real vehicles close to the stability limits. These tests pointed out how the predictions of the stability of the proposed methods obtained by using simple models are verified in practice with two different outdoor vehicles.

Generalized predictive control based on self-recurrent wavelet neural network for stable path tracking of mobile robots: adaptive learning rates approach

In this paper, a generalized predictive control (GPC) method based on self-recurrent wavelet neural network (SRWNN) is proposed for stable path tracking of mobile robots. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system although the SRWNN has less mother wavelet nodes than the wavelet neural network. Thus, the SRWNN is used as a model identifier for approximating on-line the states of the mobile robot. In our control system, since the control inputs, as well as the parameters of the SRWNN identifier are trained by the gradient descent method with the adaptive learning rates (ALRs), the optimal learning rates which are suitable for the time-varying trajectory of the mobile robot can be found rapidly. The ALRs for training the parameters of the SRWNN identifier and those for learning the control inputs are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the GPC system. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control strategy.

A higher level path tracking controller for a four-wheel differentially steered mobile robot

A variety of approaches for path tracking control of wheeled mobile robots have been implemented. While most of these are based on controlling the robot dynamics, they are not applicable if the robot dynamics are inaccessible. In this paper, a fuzzy logic controller (FLC) for the path tracking of a wheeled mobile robot based on controlling the robot at a higher level is presented. The controller is highly robust and flexible and automatically follows a sequence of discrete waypoints, and no interpolation of the waypoints is needed to generate a continuous reference trajectory. The speeds are varied depending on the variations in the path and on the posture of the robot. The heuristic rules of the FLC are based on an analogy with a human driving a car and the optimization of the controller is based on experimentation. The implementation on a P3-AT mobile robot shows the effectiveness of the proposed approach.

Nonlinear regulation and path tracking of a wheeled mobile robot in polar coordinates†

This paper presents regulation and path tracking control methods for wheeled mobile robots (WMRs) associated with kinematic models in two‐dimensional polar coordinates. The proposed two regulation control laws are designed via the Lyapunov indirect and direct stability theorems in order to asymptotically achieve nonlinear regulation. With the Lyapunov‐based stabilization law, a novel path tracking control method is proposed to achieve reference path following missions on a global scale except for an arbitrary small region around the origin; in particular, two special path tracking examples are analyzed with large initial tracking errors. Computer simulations and experimental results are described to confirm the efficacy of these proposed control approaches.

이동 로봇의 경로 추종을 위한 웨이블릿 신경 회로망 기반 일반형 예측 제어에 관한 연구

본 논문에서는 다중 입<TEX>$\cdot$</TEX>출력을 갖는 이동 로봇의 경로 추종을 위해 웨이블깃 신경 회로망에 기반한 예측 제어 방법을 제안한다. 제안된 방법에서 상태 예측기로는 학습 능력이 뛰어난 신경 회로망의 특성 및 웨이블릿 분해의 특성을 합성한 웨이블릿 신경 회로망을 사용한다. 예측기는 경사 하강법을 사용하여 웨이블릿 신경회로망의 출력에 대한 실제 이동 로봇의 상태 오차를 최소화하도록 학습된다. 또한 이동 로봇의 제어 신호인 직진 속도 및 각속도는 추종하고자 하는 기준 경로에 대한 이동 로봇의 예측 상태 오차를 이용하여 정의된 비용 함수를 최소화하도록 구해진다. 컴퓨터 모의 실험에서 변화되는 기준 경로에 대한 경로 추종 성능을 통해 제안한 예측 제어 시스템의 적용 가능성 및 효율성을 보인다. In this paper, we propose a wavelet neural network(WNN) based predictive control method for path tracking of mobile robots with multi-input and multi-output. In our control method, we use a WNN as a state predictor which combines the capability of artificial neural networks in learning processes and the capability of wavelet decomposition. A WNN predictor is tuned to minimize errors between the WNN outputs and the states of mobile robot using the gradient descent rule. And control signals, linear velocity and angular velocity, are calculated to minimize the predefined cost function using errors between the reference states and the predicted states. Through a computer simulation for the tracking performance according to varied track, we demonstrate the efficiency and the feasibility of our predictive control system.

Kinematic path-tracking of mobile robot using iterative learning control

This paper develops a kinematic path-tracking algorithm for a nonholonomic mobile robot using an iterative learning control (ILC) technique. The proposed algorithm produces a robot velocity command, which is to be executed by the proper dynamic controller of the robot. The difference between the velocity command and the actual velocity acts as state disturbances in the kinematic model of the mobile robot. Given the kinematic model with state disturbances, we present an ILC-based path-tracking algorithm. An iterative learning rule with both predictive and current learning terms is used to overcome uncertainties and the disturbances in the system. It shows that the system states, outputs, and control inputs are guaranteed to converge to the desired trajectories with or without state disturbances, output disturbances, or initial state errors. Simulations and experiments using an actual mobile robot verify the feasibility and validity of the proposed learning algorithm. © 2005 Wiley Periodicals, Inc.

A practical fuzzy logic controller for the path tracking of wheeled mobile robots

This article tackles the path-tracking problem of wheeled mobile robots (WMRs) used in the Micro Robot Soccer Tournament (MicroSot). An heuristic fuzzy logic controller (FLC) has been designed based on a model-free approach. Hardware experimental results have been presented to verify that the FLC can control a WMR. The performance of a fine-tuned P-controller has been compared with that of the proposed fuzzy logic controller. The response time of the fuzzy-logic-controlled WMR is two times faster. Good tracking control performance was also obtained from the proposed controller.

Robust PID Tuning. Application to a Mobile Robot Path Tracking Problem

Abstract This paper presents a methodology for tuning PIDs considering the nominal performance and the robustness as control specifications. The synthesis procedure is similar to the Ziegler-Nichols method for PID controllers and can be easily used for industrial processes. As a workbench for testing the PID controller a mobile robot has been used. The path tracking problem of a mobile robot has been used as a workbench for testing the PID controller.

A Smith-predictor-based generalised predictive controller for mobile robot path-tracking

This paper shows how to improve the robustness of mobile robot path tracking when predictive control algorithms are used. When uncertainties in a mobile robot are considered, it is shown how the classical generalised predictive controller (GPC) may lead to an unstable behaviour of the mobile robot. The equivalence between the GPC and a structure composed by an optimal predictor and a classical controller is given, and it is shown that use of a Smith predictor instead of an optimal predictor increases the robustness of the system. A new strategy, the Smith-predictor-based GPC, is proposed. Experimental tests carried out on a LABMATE mobile robot validate the performance of the proposed controller.

Mobile robot path planning and tracking using simulated annealing and fuzzy logic control

This article describes the development and implementation of an automatic controller for path planning and navigation of an autonomous mobile robot using simulated annealing and fuzzy logic. The simulated annealing algorithm was used to obtain a collision-free optimal trajectory among fixed polygonal obstacles. C-space was used to represent the working space and B-spline curves were used to represent the trajectories. The trajectory tracking was performed with a fuzzy logic algorithm. A detailed explanation of the algorithm is given. The objectives of the control algorithm were to track the planned trajectory and to avoid collision with moving obstacles. Simulation and implementation results are shown. A Nomadic 200 mobile robot was used to perform the experiments.