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.     

Digital fuzzy logic controller: design and implementation

In this paper, various aspects of digital fuzzy logic controller (FLC) design and implementation are discussed, Classic and improved models of the single-input single-output (SISO), multiple-input single-output (MISC), and multiple-input multiple-output (MIMO) FLCs are analyzed in terms of hardware cost and performance. A set of universal parameters to characterize any hardware realization of digital FLCs is defined. The comparative study of classic and alternative MIMO FLCs is presented as a generalization of other controller configurations. A processing element for the parallel FLC architecture realizing improved inferencing of MIMO system is designed, characterized, and tested. Finally, as a case feasibility study, a direct data stream architecture for complete digital fuzzy controller is shown as an improved solution for high-speed, cost-effective, real-time control applications     

Modelling the software development process using an expert simulation system having fuzzy logic

This paper describes an intelligent computerized tool designed to aid managers of software development projects in planning, managing and controlling the development process of medium- to large-scale software projects. Systems dynamics is used to model and simulate the dynamic process of software development. The software development process is affected by some imprecise and vague variables that are treated as fuzzy variables. The simulation model is integrated with two expert systems. The fuzzy input variables to the system dynamics simulation model are handled by an input expert system having fuzzy logic. This expert system is designed to check the consistency of input variables. The simulation results are analysed by an output expert system having fuzzy logic. This expert system is designed to make recommendations based on experimentation with the simulation model.     

Information system project selection using fuzzy logic

The allocation of scarce business resources is becoming a major problem for management nowadays. Limited by lack of resources, management needs to make project selection decisions under the constraints of available information, and often makes decisions based on incomplete information. Traditionally, quantitative techniques dominate decision-making in selecting management information systems (MIS) projects. However, encapsulating or simply discarding the qualitative concerns makes a project economically sound but not operationally sound. This is often the reason that causes a project to fail. This paper establishes a model by incorporating fuzzy logic as a decision tool, which smoothly aids decision makers dealing with uncertain or incomplete information without losing existing quantitative information     

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.     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

机器人虚拟仿真及远程控制系统的研究及实现

机器人虚拟仿真及其与远程控制相结合,表现出独特的优势,应用前景广阔。应用OpenGL和WinSock技术,设计并实现了机器人虚拟仿真及其远程控制系统。建立基于C#与OpenGL的虚拟仿真平台,实现了机器人虚拟现实的运动仿真;建立基于WinSock的远程控制系统,提出基于反射原理的解释器模式,实现了机器人远程面板或程序控制;提出基于速度向量场的改进无碰轨迹规划算法,有效对静态与动态障碍物安全避障。系统测试结果表明该机器人虚拟仿真及远程控制系统有较好的准确性和稳定性。     

RENOIR: An expert system using fuzzy logic for rheumatology diagnosis

A new expert system (ES) to aid the nonspecialist physician in diagnosing arthritis and collagen diseases has been developed. Here we present the structure of RENOIR and the results of its implementation. This rule-based ES has been programmed using the MILORD environment. This is a shell to develop ES using a closed set of linguistic labels to express uncertainty. A feature of RENOIR is its five levels of knowledge representation, which permits to build a very flexible knowledge base (KB) and express knowledge with high accuracy. Those rules directed to similar goals are grouped in modules to improve computational performance and for higher clarity of the KB. Control of the reasoning process is assured by several mechanisms, one of the main being metarules specifically designed for almost all the knowledge levels of the KB. We have used public domain knowledge (books, criteria tables) and personal heuristics from one of the authors (Belmonte-Serrano) to implement the KB of RENOIR. In its present form, our KB comprises 1 058 rules, 978 facts, 220 metarules, and 34 modules. A first validation process has shown good performance of the ES compared to 12 physicians with diverse levels of experience in rheumatic diseases. New ongoing versions of the system with improved interfaces and reasoning capabilities are expected before verifying RENOIR's clinical acceptability. © 1994 John Wiley & Sons, Inc.     

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     

FuzzyShell: a large-scale expert system shell using fuzzy logic foruncertainty reasoning

There exist in the literature today many contributions dealing with the incorporation of fuzzy logic in expert systems. However, unfortunately, much of what has been proposed can only be applied to small-scale expert systems; that is, when the number of rules is in the dozens as opposed to in the hundreds. The more traditional (nonfuzzy) expert systems are able to cope with large numbers of rules by using Rete networks for maintaining matches of all the rules and all the facts. (A Rete network obviates the need to match the rules with the facts on every cycle of the inference engine.) In this paper, we present a more general Rete network that is particularly suitable for reasoning with fuzzy logic. The generalized Rete network consists of a cascade of three networks: the pattern network, the join network, and the evidence aggregation network. The first two layers are modified versions of similar layers for the traditional Rete networks and the last, the aggregation layer, is a new concept that allows fuzzy evidence to be aggregated when fuzzy inferences are made about the same fuzzy variable by different rules     

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.     

Design and implementation of the tree-based fuzzy logic controller

In this paper, a tree-based approach is proposed to design the fuzzy logic controller. Based on the proposed methodology, the fuzzy logic controller has the following merits: the fuzzy control rule can be extracted automatically from the input-output data of the system and the extraction process can be done in one-pass; owing to the fuzzy tree inference structure, the search spaces of the fuzzy inference process are largely reduced; the operation of the inference process can be simplified as a one-dimensional matrix operation because of the fuzzy tree approach; and the controller has regular and modular properties, so it is easy to be implemented by hardware. Furthermore, the proposed fuzzy tree approach has been applied to design the color reproduction system for verifying the proposed methodology. The color reproduction system is mainly used to obtain a color image through the printer that is identical to the original one. In addition to the software simulation, an FPGA is used to implement the prototype hardware system for real-time application. Experimental results show that the effect of color correction is quite good and that the prototype hardware system can operate correctly under the condition of 30 MHz clock rate.     

Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic

Most formal rehabilitation facilities are situated in a hospital or care center setting, which may not always be conveniently accessible for patients, especially those in geographically isolated areas. Home-based rehabilitation has potential to offer greater accessibility and thus increase consistent uptake. In addition, the exercise performed in conventional rehabilitation contexts may be insufficient to ensure the patient's speedy recovery, with complimentary rehabilitation exercises at home required to make a difference. The goal is to provide effective home-based rehabilitation offering outcomes similar to those obtained through hospital-based rehabilitation under the supervision of an occupational therapist. This paper presents the development of a Kinect-based system for ensuring home-based rehabilitation using a Dynamic Time Warping (DTW) algorithm and fuzzy logic. The ultimate goal is to assist patients in conducting safe and effective home-based rehabilitation without the immediate supervision of a physician.     

Word recognition using fuzzy logic

This paper presents an offline word-recognition system based on structural information in the unconstrained written word. Oriented features in the word are extracted with the Gabor filters. We estimate the Gabor filter parameters from the grayscale images. A two-dimensional fuzzy word classification system is developed where the spatial location and shape of the membership functions are derived from the training words. The system achieves an average recognition rate of 74% for the word being correctly classified in the top position and an average of 96% for the word being correctly classified within the top five positions     

Leaving inconsistency using fuzzy logic

Current software development methods do not provide adequate means to model inconsistencies and therefore force software engineers to resolve inconsistencies whenever they are detected. Certain kinds of inconsistencies, however, are desirable and should be maintained as long as possible. For instance, when multiple conflicting solutions exist for the same problem, each solution should be preserved to allow further refinements along the development process. An early resolution of inconsistencies may result in loss of information and excessive restriction of the design space. This paper aims to enhance the current methods by modeling and controlling the desired inconsistencies through the application of fuzzy logic-based techniques. It is shown that the proposed approach increases the adaptability and reusability of design models.     

Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic

The objective of the present study is to develop/establish a web-based medical diagnostic support system (MDSS) by which health care support can be provided for people living in rural areas of a country. In this respect, this research provides a novel approach for medical diagnosis driven by integrating fuzzy and intuitionistic fuzzy (IF) frameworks. Subsequently, based on the proposed approach a web-based MDSS is developed. The proposed MDSS comprises of a knowledge base (KB) and intuitionistic fuzzy inference system (IFIS). Based on the observation that medical data cannot be described with both precision and certainty, a medical KB is constructed in the form of a set of if-then decision rules by employing both fuzzy and IF logics. After constructing the medical KB, a new set of patients is considered for diagnosing the diseases. For each patient, linguistic values of the patients’ symptoms are considered as inputs of the proposed IFIS and modeled by using the generalized triangular membership functions. Subsequently, integrated fuzzy and IF rule-based inference system is used to find a valid conclusion for the new set of patients. In a nutshell, in this paper fuzzy rule-based and IFS based inference systems are combined for better and more realistic representation of uncertainty of the medical diagnosis problem and for more accurate diagnostic result. The method is composed of following four steps: (1) the modeling of antecedent part of the rules, which consist of linguistic assessments of the patients’ symptoms provided by the doctors/medical experts with their corresponding confidence levels, by using generalized fuzzy numbers; (2) the modeling of consequent part, which reveals the degree of association and the degree of non-association of diseases into the patient, by using IFSs; (3) the use of IF aggregation operator in inference process; (4) the application of relative closeness function to find the final crisp output for a given diagnosis. Finally, the applicability of the proposed approach is illustrated with a suitable case study. This article has also justified the proposed approach by using similarity measurement.     

A complementary fuzzy logic system

This article discusses complementary (C) fuzzy logic system that is one of continuous multiary logic systems that satisfies a complementary law differently from usual fuzzy logic systems. This article includes formulation of the C fuzzy logic system, derivation of tautologies, and mentions an example that typically shows a difference in inference computation between the C fuzzy logic system and a usual fuzzy logic system.     

Learning method of the ADALINE using the fuzzy logic system

We propose a learning method for the ADALINE. The proposed method exploits fuzzy logic system for automatic tuning of the weights of the ADALINE. The inputs of the fuzzy logic system are error and change of error, and the output is the weight variation. We used same membership functions and different scaling factor for each weights. In order to verify the effectiveness of the proposed method, we performed the simulation and experimentation for the cases of the noise cancellation and the inverted pendulum control. The results show that the proposed method does not need the learning rate and the derivative, and improves the performance compared to the Widrow–Hoff delta rule for ADALINE.     

Autonomous fuzzy parking control of a car-like mobile robot

This paper is devoted to design and implement a car-like mobile robot (CLMR) that possesses autonomous garage-parking and parallel-parking capability by using real-time image processing. For fuzzy garage-parking control (FGPC) and fuzzy parallel-parking control (FPPC), feasible reference trajectories are provided for the fuzzy logic controller to maneuver the steering angle of the CLMR. We propose two FGPC methods and two FPPC methods to back-drive or head-in the CLMR to the garage and the parking lot, respectively. Simulation results illustrate the effectiveness of the developed schemes. The overall experimental setup of the parking system developed in this paper is composed of a host computer, a communication module, a CLMR, and a vision system. Finally, the image-based real-time implementation experiments of the CLMR demonstrate the feasibility and effectiveness of the proposed schemes.