Distributed fuzzy discrete event system for robotic sensory information processing

Abstract: This paper presents a novel intelligent sensory information processing technique using a fuzzy discrete event system (FDES) for robotic control. The proposed method combines the predictive control approach of a discrete event system with the approximate reasoning aspect of fuzzy logic. It develops a supervisory control strategy for behavior-based robotic control using distributed FDES. The application of distributed FDES eliminates the formation of complex fuzzy predicates and a large fuzzy rule-base. The FDES-based approach also provides means for analyzing behavior-based decision-making using the observability and controllability of an FDES. The observability of an FDES describes uncertainties in sensory data, and the controllability of an FDES exploits uncertain state transitions in a dynamic environment. Comprehensive experiments on behavior-based mobile robot navigation are presented to authenticate the performance of the proposed methodology.     

Synchronization Control for a Swarm of Unicycle Robots: Analysis of Different Controller Topologies

This paper proposes a nonlinear synchronization controller for a swarm of unicycle robots performing a cooperative task, i.e., following a desired trajectory per robot while maintaining a prescribed formation. The effect of communication between robots is analyzed and several network topologies are investigated, e.g., all‐to‐all, ring type, undirected, among others. The stability analysis of the closed loop system is provided using the Lyapunov method. Experiments with four unicycle robots are presented to validate the control law and communication analysis. Accumulated errors over the experiment time are presented in order to determine which topology is most efficient.     

智能RLED动态目标中机器手的模糊鲁棒控制系统

给出了一种电机驱动机器手中非线性机电模型的模糊鲁棒闭环控制系统,此控制系统可处理非结构环境下的三个主要的智能机器人导航问题：自动化规划、快速连续导航中的避障、处理结构和（或）非结构不确定性。     

普适环境下的模糊访问控制模型研究*

现有的普适访问控制模型的研究都忽视了安全中存在的模糊性问题。在普适计算环境中上下文信息可能是不完备或者模糊的,因此由残缺或模糊的上下文信息推导授权结果就显得十分重要。在使用控制模型的基础上,提出了一个模糊的普适访问控制模型（fuzzy usage control models, FUCM）,给出了模型的形式化定义,并通过实例对模型的授权过程进行分析。结果表明新的模型能够对模糊的上下文信息进行授权决策,并具有更智能的授权能力,更适用于普适计算环境。最后,给出了模型实现的参考监视器体系结构。     

双轮移动机器人安全目标追踪与自动避障算法

设计了双轮移动机器人安全目标追踪算法和双回路的追踪与避障控制方案.内层控制回路是目标追踪的控制律,用来指导机器人追踪到指定目标并保持一定的安全距离,而且兼顾了机器人在运行速度上的限制和追踪的时间效率,其控制的渐近稳定性用Lyapunov函数法进行了证明.当遇到障碍物时,外层控制回路根据超声传感器的信息和阻抗控制的概念产生阻抗虚拟力,将期望目标调整到虚拟位置,使机器人能够自动转向以避开障碍物.仿真研究和实验结果证明了追踪算法的有效性和避障方法的可行性.     

一种独轮机器人的滑模控制

针对一种由一个车轮驱动并控制前向平衡、并由电机驱动惯性轮形成反力矩来控制侧向平衡的独轮机器人系统,提出了一种分组分层滑模变结构控制方法.该方法根据机器人动力学模型特点,将系统看作两个单输入系统组合,并分别针对每个单输入系统设计分层滑模控制器,从而实现机器人的运动平衡控制.从理论上证明了各层滑动平面的渐近稳定性,并通过仿...     

New approach to intelligent control systems with self-exploring process

This paper proposes an intelligent control system called self-exploring-based intelligent control system (SEICS). The SEICS is comprised of three basic mechanisms, namely, controller, performance evaluator (PE), and adaptor. The controller is constructed by a fuzzy neural network (FNN) to carry out the control tasks. The PE is used to determine whether or not the controller's performance is satisfactory. The adaptor, comprised of two elements, action explorer (AE) and rule generator (RG), plays the main role in the system for generating new control behaviors in order to enhance the control performance. AE operates through a three-stage self-exploration process to explore new actions, which is realized by the multiobjective genetic algorithm (GA). The RG transforms control actions to fuzzy rules based on a numerical method. The application of the adaptor can make a control system more adaptive in various environments. A simulation of robotic path-planning is used to demonstrate the proposed model. The results show that the robot reaches the target point from the start point successfully in the lack-of-information and changeable environments.     

基于行为的移动机器人避障系统的设计

随着机器人应用范围的不断扩展,机器人所面临的工作环境也越来越复杂,多数是未知的、动态的和非结构化的。通过对基于行为的机器人控制技术的研究,提出了一种实用的自主移动机器人智能避障控制方案,阐述了其具体实现技术;将基于行为的控制技术融合进模糊控制的思想中,使移动机器人的行为通过运用模糊控制和基于优先度的行为决策来实现。试验证明了这一方法的有效性和实时性。     

机器人足球智能控制系统分析与设计

该文结合智能控制理论和多智能体系统理论分析了基于多智能体系统(MABS)的分级递阶智能控制系统结构,并将该控制结构用于机器人足球系统,提出了机器人足球系统的分级递阶智能控制结构,并运用基于人工神经网络的模糊控制系统为足球机器人真正实现智能化控制提供了一种可行的控制方案。     

Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms

We describe a tracking controller for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on type-2 fuzzy logic theory and genetic algorithms. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems.     

A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot

Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.     

Optimal two-degree-of-freedom fuzzy control for locomotion control of a hydraulically actuated hexapod robot

Locomotion control of legged robots is a very challenging task because very accurate foot trajectory tracking control is necessary for stable walking. An electro-hydraulically actuated walking robot has sufficient power to walk on rough terrain and carry a heavier payload. However, electro-hydraulic servo systems suffer from various shortcomings such as a high degree of nonlinearity, uncertainty due to changing hydraulic properties, delay due to oil flow and dead-zone of the proportional electromagnetic control valves. These shortcomings lead to inaccurate analytical system model, therefore, application of classical control techniques result into large tracking error. Fuzzy logic is capable of modeling mathematically complex or ill-defined systems. Therefore, fuzzy logic is becoming popular for synthesis of control systems for complex and nonlinear plants. In this investigation, a two-degree-of-freedom fuzzy controller, consisting of a one-step-ahead fuzzy prefilter in the feed-forward loop and a PI-like fuzzy controller in the feedback loop, has been proposed for foot trajectory tracking control of a hydraulically actuated hexapod robot. The fuzzy prefilter has been designed by a genetic algorithm (GA) based optimization. The prefilter overcomes the flattery delay caused by the hydraulic dead-zone of the electromagnetic proportional control valve and thus helps to achieve better tracking. The feedback fuzzy controller ensures the stability of the overall system in the face of model uncertainty associated with hydraulically actuated robotic mechanisms. Experimental results exhibit that the proposed controller manifests better foot trajectory tracking performance compared to single-degree-of-freedom (SDF) fuzzy controller or optimal classical controller like state feedback LQR controller.     

基于传感器和模糊规则的机器人在动态障碍环境中的智能运动控制

提出了一种基于传感器和模糊规则的智能机器人运动规划方法,该方法运用了基于调和函数分析的人工势能场原理。采用模糊规则可减少推导势能函数所必须的计算,同时给机器人伺服系统发出指令,使它能够自动地寻找通向目标的路径。提出的方法具有简单、快速的特点,而且能对n自由度机械手的整个手臂实现避碰,建立在非线性机器人动力学之上的整个闭环系统和模糊控制器的稳定性由李雅普诺夫原理保证。仿真结果证明了该方法的有效性,通过比较分析显示出文中所提出的避障算法的优越性。     

Emergent behaviors of a fuzzy sensory-motor controller evolved bygenetic algorithm

Recently, there has been extensive work on the construction of fuzzy controllers for mobile robots by a genetic algorithm (GA); therefore, we can realize evolutionary optimization as a promising method for developing fuzzy controllers. However, much investigation on the evolutionary fuzzy controller remains because most of the previous works have not seriously attempted to analyze the fuzzy controller obtained by evolution. This paper develops a fuzzy logic controller for a mobile robot with a GA in simulation environments and analyzes the behaviors of the controller with a state transition diagram of the internal model. Experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved wen enough to smoothly drive the robot in different environments. The robot produces emergent behaviors by the interaction of several fuzzy rules obtained.     

Visual servoing of mobile robots for posture stabilization: from theory to experiments

On the basis of the kinematic model of a unicycle mobile robot in polar coordinates, an adaptive visual servoing strategy is proposed to regulate the mobile robot to its desired pose. By regarding the unknown depth as model uncertainty, the system error vector can be chosen as measurable signals that are reconstructed by a motion estimation technique. Then, an adaptive controller is carefully designed along with a parameter updating mechanism to compensate for the unknown depth information online. On the basis of Lyapunov techniques and LaSalle's invariance principle, rigorous stability analysis is conducted. Because the control law is elegantly designed on the basis of the polar‐coordinate‐based representation of error dynamics, the consequent maneuver behavior is natural, and the resulting path is short. Experimental results are provided to verify the performance of the proposed approach. Copyright © 2013 John Wiley & Sons, Ltd.