The uses of fuzzy logic in autonomous robot navigation

 The development of techniques for autonomous navigation in real-world environments constitutes one of the major trends in the current research on robotics. An important problem in autonomous navigation is the need to cope with the large amount of uncertainty that is inherent of natural environments. Fuzzy logic has features that make it an adequate tool to address this problem. In this paper, we review some of the possible uses of fuzzy logic in the field of autonomous navigation. We focus on four issues: how to design robust behavior-producing modules; how to coordinate the activity of several such modules; how to use data from the sensors; and how to integrate high-level reasoning and low-level execution. For each issue, we review some of the proposals in the literature, and discuss the pros and cons of fuzzy logic solutions. Received: 31 March 1997 / Accepted: 24 September 1997     

Autonomous multi-mobile robot system: simulation and implementation using fuzzy logic

In an autonomous multi-mobile robot environment, path planning and collision avoidance are important functions used to perform a given task collaboratively and cooperatively. This study considers these important and challenging problems. The proposed approach is based on a potential field method and fuzzy logic system. First, a global path planner selects the paths of the robots that minimize the potential value from each robot to its own target using a potential field. Then, a local path planner modifies the path and orientation from the global planner to avoid collisions with static and dynamic obstacles using a fuzzy logic system. In this paper, each robot independently selects its destination and considers other robots as dynamic obstacles, and there is no need to predict the motion of obstacles. This process continues until the corresponding target of each robot is found. To test this method, an autonomous multi-mobile robot simulator (AMMRS) is developed, and both simulation-based and experimental results are given. The results show that the path planning and collision avoidance strategies are effective and useful for multi-mobile robot systems.     

A robust fuzzy logic controller for robot manipulators withuncertainties

Owing to load variation and unmodeled dynamics, a robot manipulator can be classified as a nonlinear dynamic system with structured and unstructured uncertainties. In this paper, the stability and robustness of a class of the fuzzy logic control (FLC) is investigated and a robust FLC is proposed for a robot manipulator with uncertainties. In order to show the performance of the proposed control algorithm, computer simulations are carried out on a simple two-link robot manipulator.     

Stability indices for a self-organizing fuzzy controlled robot: A case study

This study developed a model-free self-organizing fuzzy controller (SOFC) for manipulating multiple-input multiple-output systems. The SOFC has an online learning algorithm that can continually update fuzzy rules during the control process, beginning from an empty rule table. The SOFC was used to control a three-link robot with a complex dynamic model in order to evaluate its applicability. Stability and robustness of the SOFC were demonstrated using a state-space approach. Simulation results confirmed that the control performance of the SOFC outperforms that of the fuzzy logic controller for the control of the robot.     

Mobile robot localization by fuzzy logic fusion of multisensor data

Interest has been growing in the use of different sensors to increase the capabilities of navigation systems of autonomous vehicles. This area has been studied by several researchers. Nevertheless, this problem has not been solved in a fully satisfying manner. We proposed the method of global, long-term position estimation in a known environment. The method does not require any a priori position prediction or estimation. It is especially suitable in the case of using very simple sensors. The algorithm is based on fuzzy logic fusion of data received from several sensors for some time period. We discuss the methodology of solution evaluation and present some examples of typical criteria. Results of first computer simulation are presented.     

The simulation of an emotional robot implemented with fuzzy logic

Recently, researchers have tried to better understand human behaviors so as to let robots act in more human ways, which means a robot may have its own emotions defined by its designers. To achieve this goal, in this study, we designed and simulated a robot, named Shiau_Lu, which is empowered with six universal human emotions, including happiness, anger, fear, sadness, disgust and surprise. When we input a sentence to Shiau_Lu through voice, it recognizes the sentence by invoking the Google speech recognition method running on an Android system, and outputs a sentence to reveal its current emotional states. Each input sentence affects the strength of the six emotional variables used to represent the six emotions, one corresponding to one. After that, the emotional variables will change into new states. The consequent fuzzy inference process infers and determines the most significant emotion as the primary emotion, with which an appropriate output sentence as a response of the input is chosen from its Output-sentence database. With the new states of the six emotional variables, when the robot encounters another sentence, the above process repeats and another output sentence is then selected and replied. Artificial intelligence and psychological theories of human behaviors have been applied to the robot to simulate how emotions are influenced by the outside world through languages. In fact, the robot may help autistic children to interact more with the world around them and relate themselves well to the outside world.     

Robust tracking control of an electrically driven robot: adaptive fuzzy logic approach

This paper is concerned with the robust tracking control of an electrically driven robot with the model uncertainties in the robot dynamics and the motor dynamics. The motors driving the joints of the robot are assumed to be equipped with only the joint position and the current measurement devices. Adaptive fuzzy logic and adaptive backstepping method are employed to provide the solution to the control problem. The suggested method does not require the measurement of the velocity nor the acceleration. Simulation results from a two-link electrically driven robot show the satisfactory performance of the proposed control scheme even in the presence of internal model uncertainties in both the robot and motor dynamics and external disturbances.     

Uniform ultimate boundedness analysis and synthesis for linear systems with dead‐zone in the actuators

The presence of the dead‐zone nonlinearity is common in many physical systems and in general degrades the control systems performance or stability. This note considers an interconnection between a continuous time multiple‐input linear system and a dead‐zone nonlinearity associated with a state feedback. The issue of analyzing the property of uniform ultimate boundedness (UUB) of a system and the issue of designing a control under UUB constraint are studied. Firstly, the dead‐zone is replaced via a linear transformation introducing the saturation. Secondly, we improve the size of the UUB area that can be found in the literature, by expressing the saturation in terms of parametrized piecewise affine bounds. This allows also a link between standard approaches such ones using piecewise affine models or ones considering global cone bounded sector conditions. In the framework of analysis and synthesis, sufficient conditions are obtained by linear matrix inequalities. Illustrative examples are given to spotlight the efficiency of our approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Nonlinear ultimate boundedness control and stabilization of a flexible robotic arm

We treat the question of control of an elastic robotic arm of two links. Of course, this approach can be extended to other elastic arms. A nonlinear ultimate boundedness controller (UBC) is synthesized such that in the closed-loop system the joint angle tracking error is uniformly bounded and tends to a certain small neighborhood of the origin. The controller includes a reference joint angle trajectory generator and integral error feedback. Although the joint angles are controlled using the UBC, elastic modes are excited. A feedback stabilizer is designed for the linearized model including the UBC about the terminal state that is switched only in the vicinity of the equilibrium state for stabilization. Simulation results are presented to show that in the closed-loop system including the UBC and the stabilizer accurate joint angle trajectory following and elastic mode stabilization are accomplished in the presence of uncertainty.     

Determining PID controller coefficients for the moving motor of a welder robot using fuzzy logic

In this paper, a fuzzy algorithm was used for determining the coefficients of a PID controller using an online method. The plant used in this system is a welder robot, which is used for welding oil and gas pipelines. This robot rest on the pipe and weld it by moving around. The speed is adjusted using a motor moving the robot around the pipe. A digital controller also used for implementation. In this research, in order to choose the rules the skills transmit method and that how the PID response is required has been used. In this paper, the type of algorithms that continuously assigns PID coefficients using an online method depending on the characteristics of the system which is based on fuzzy logic was used. First, the rules associated to each output in relation to inputs are depicted in it. Using the mentioned method, simulation results and then implementation results were obtained and discussed.     

基于模糊逻辑行为融合路径规划方法

采用基于模糊逻辑的路径规划方法,提出了一种在未知环境中基于模糊逻辑行为融合路径规划方法,设计了移动机器人路径规划方案,并介绍了该控制器的结构组成和设计过程.将模糊逻辑控制和行为控制相结合,构建了一个由避障行为、目标导向行为、模糊行为监督器组成的基于模糊逻辑行为控制的路径规划器.仿真研究和实验结果表明了避障方法的有效性和可行性.     

Exponential ultimate boundedness of impulsive stochastic delay difference systems

This paper is concerned with the exponential ultimate boundedness problems for the impulsive stochastic delay difference systems. Several sufficient conditions on the global pth moment exponential ultimate boundedness are presented by using the Lyapunov methods and the algebraic inequality techniques, and the estimated exponential convergence rate and the ultimate bound are provided as well. As an application, the boundedness criteria are applied to a class of discrete impulsive stochastic neural networks with delays. The obtained results show that the impulses not only can stabilize an unstable stochastic difference delay system but also can make an unbounded stochastic difference delay system into a bounded system. Examples and simulations are also provided to demonstrate the effectiveness of the derived theoretical results.     

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.     

Localization of a wheeled mobile robot by sensor data fusion based on a fuzzy logic adapted Kalman filter

Accurate estimates of mobile robot location, if available, can be used to improve the performance of a vehicle dynamics control system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman filter, providing robot position and orientation estimates at each sampling instant. To cope with the tracking of long trajectories, the performance of the filter is improved by introducing an on-line fuzzy-rule-based adaptation scheme.     

An algorithm of uniform ultimate boundedness for a class of switched linear systems

Uniform ultimate boundedness of a class of switched linear systems is studied. A switched linear system in this class consists of m subsystems, and none of the individual subsystems need to be stabilizable. The switched linear system is shown to be uniformly ultimately bounded for any pre-given bound if the union of individual controllable subspaces covers the entire state space. An algorithm for guaranteeing uniform ultimate boundedness is given to provide the design of continuous controllers and the switching strategy.     

Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic

Many robot controllers require not only joint position measurements but also joint velocity measurements; however, most robotic systems are only equipped with joint position measurement devices. In this paper, a new output feedback tracking control approach is developed for the robot manipulators with model uncertainty. The approach suggested herein does not require velocity measurements and employs the adaptive fuzzy logic. The adaptive fuzzy logic allows us to approximate uncertain and nonlinear robot dynamics. Only one fuzzy system is used to implement the observer-controller structure of the output feedback robot system. It is shown in a rigorous manner that all the signals in a closed loop composed of a robot, an observer, and a controller are uniformly ultimately bounded. Finally, computer simulation results on three-link robot manipulators are presented to show the results which indicate good position tracking performance and robustness against payload uncertainty and external disturbances.     

基于LoRa无线通信的工业机器人远程监控系统设计

目前设计的工业机器人远程监控系统存在抗干扰能力差、功耗过高的问题。基于LoRa无线通信设计了一种新的工业机器人远程监控系统,系统硬件主要设计了主控芯片、工业机器人接口、电源电路和位移传感器四部分。主控芯片为SX1265/7/8型号射频芯片,选用STM32F1103C8T6型号MCU主控芯片作为LoRa无线通信的组网处理硬件,根据MCU主控芯片性能建立信号链,采用SPI接口使SX1276无线收发装置和单片机设备的接口关联,利用锂电池作为电源电路,通过AME8800AEETL稳压芯片稳定锂电池电源电压,使用WXY31拉线式位移传感器实现传感。引入LoRa无线通信技术,建立LoRa无线通信信号储存时序,接收采集平台下发监控指令,更新显示屏上的机器人工作状态,实现远程监控软件操作。实验结果表明,设计的基于LoRa无线通信的工业机器人远程监控系统抗干扰能力得到有效加强,功耗明显降低。     

Stability of multiloop LQ regulators with nonlinearities--Part II: Regions of ultimate boundedness

This note investigates the closed-loop stability of linear time-invariant systems controlled by linear quadratic (LQ) regulators when there are nonlinearities in the loops which escape the (0.5, infty) stability sector in a bounded region containing the origin. An estimate of the region of ultimate boundedness is obtained, which provides methods for selecting the performance function weights to get better regulator designs.     

On ultimate boundedness around non-assignable equilibria of linear time-invariant systems

In this note we investigate the following questions: given a (finite-dimensional) linear time-invariant (LTI) multivariable system and a constant desired value for its output, say y_?. Assume there is no assignable equilibrium point corresponding to y_?. How “close” to y_? can we ultimately keep the output using LTI static state-feedback stabilizing controllers? Can this neighborhood of y_? be reduced with dynamic, nonlinear, time-varying controllers? Our main contributions are the proof that the optimal ultimate boundedness neighborhood is achieved with LTI static state-feedback, the explicit computation of the neighborhood's size and the proof, under some reasonable rank assumptions, that the system has non-assignable values for the output if and only if it has a transmission zero at zero. Interestingly, there is no connection between this problem and the more familiar concepts of controllability and observability.