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MoNIF: A Modular Neuro-Fuzzy controller for race car navigation

MoNIF: A Modular Neuro-Fuzzy controller for race car navigat...

引用

Proceedings of the 1997 ieee International symposium on computational intelligence in Robotics and automation, CIRA

作者： Ng, Kim C. Scorcioni, Ruggero Trivedi, Mohan M. Lassiter, Nils Univ of California - San Diego La Jolla United States

This paper characterizes how well the modular neuro-fuzzy controller, MoNiF, can perform in robotic applications by testing it in a race-car simulator. The performance of this controller is compared with that of a neural network controller (NNC) and the best programmed car (the ideal car). During the phase of learning from the ideal car, MoNiF reaches its stable performance (when it demonstrates good control) at lap 300;while the NNC requires 700 laps. Without further learning and when competing in the same learning track, MoNiF completes 20 laps of the track about 24 seconds later than the ideal car but 29 second ahead of the NNC car. However, when they all are competing in different unfamiliar tracks, MoNiF performs comparably to the ideal car, while NNC has completely lost its ability to drive. Observing the behaviors of NNC and MoNiF, we conclude that MoNiF learns to refine the output of expert rules and thus can drive in most tracks. Meanwhile NNC learns to drive in a particular track but loses its generality for other tracks. It is be shown that this modular control method, equipped with the pretrained knowledge of only few simple expert rules, learns much faster than a NNC without any apriori knowledge.

关键词： Fuzzy control

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Fuzzy sliding mode control for a robot manipulator

Fuzzy sliding mode control for a robot manipulator

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： B.W. Bekit J.F. Whidborne L.D. Seneviratne Department of Mechanical Engineering King''s College London UK

A sliding mode control algorithm combined with a fuzzy control scheme is developed for the trajectory control of a robot manipulator. The scheme is used to compensate for the influence of unmodeled dynamics and to reduce chattering. Simulation results show that the proposed controller gives good system performance in the face of uncertain system parameters and external disturbances.

关键词： Fuzzy control Sliding mode control Robots Manipulator dynamics Payloads Robust control Acceleration Transmission line matrix methods Fuzzy sets Mechanical engineering

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Multiscale motion planning

Multiscale motion planning

引用

ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： J. Albus A. Meystel S. Uzzaman Intelligent Syst. Division National Institute for Standards and Technology Gaithersburg MD USA Department of Electrical & Computer Engineering Drexel University Philadelphia PA USA

Multiscale planning is introduced as a nested system of state space search processes which provide for exceptionally efficient computational algorithms. It is demonstrated that search in the output space allows for efficient planning/control procedures. The results of 2-level nested hierarchical planning are given as a representative simulation example. It is demonstrated that multiscale searching achieves results similar to those obtained by human strategies of planning.

关键词： Motion planning State-space methods control system synthesis Space technology Humans Strategic planning Automatic control Vehicle dynamics Switches computational intelligence

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An evolution strategy with competing subpopulations

An evolution strategy with competing subpopulations

引用

ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： K. Izumi M.M.A. Hashem K. Watanabe Department of Mechanical Engineering Saga University Saga Japan

Evolution strategies, based on the natural evolution, are the algorithms to solve the parameter optimization problems numerically. In this paper, a new evolution strategy (ES) is proposed to solve optimal control problems. With a view to making a balance between exploration and exploitation, a competing subpopulation based arithmetical crossover technique is proposed. The effectiveness of the proposed ES is illustrated by some simulations for the push-cart (a discrete-time optimal control model) control problem.

关键词： Optimal control Electronic switching systems Genetic mutations Mechanical engineering Optimization methods Genetic algorithms Data structures Convergence Input variables Sampling methods

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Autonomous navigation using an adaptive hierarchy of multiple fuzzy-behaviors

Autonomous navigation using an adaptive hierarchy of multipl...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： E. Tunstel H. Danny T. Lippincott M. Jamshidi Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA NASA Center for Autonomous Control Engineering Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

Adaptive behavioral capabilities are necessary for robust mobile robot navigation in non-engineered environments. Robust behavior requires that uncertainty be accommodated in the robot control system, especially when autonomy is desired. Fuzzy logic control technology enables development of controllers which can provide the necessary computational intelligence in real-time. This paper presents the incorporation of fuzzy logic, into the framework of behavior-based control. An architecture for hierarchical behavior control is presented in which control decisions result from a consensus of behavioral recommendations. Multiple fuzzy-behavior coordination is discussed and applied to autonomous navigation without explicit maps. Performance and robustness is demonstrated by implementation on a mobile robot with significant mechanical imperfections.

关键词： Mobile robots Fuzzy control Robust control Competitive intelligence Fuzzy logic Intelligent robots Aircraft navigation Programmable control Adaptive control Uncertainty

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A cerebellar approach to adaptive locomotion for legged robots

A cerebellar approach to adaptive locomotion for legged robo...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： J. Hoff G.A. Bekey Department of Computer Science Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

This paper describes a neural learning architecture for control of legged robots inspired by mammalian neurophysiology. Biological studies indicate that the cerebellum is a key part of an adaptive control system which enables mammals to display remarkable limb coordination during locomotion. We present a distributed control system using reinforcement learning methods and mechanisms inspired by the cerebellum. Embedded within a framework of base locomotion controllers, the system is tasked with learning modulatory control signals which optimize gait performance measures. We briefly describe simulation studies in progress for a four-legged robot.

关键词： Legged locomotion Robot kinematics control systems Robot control Neurophysiology Adaptive control Displays Distributed control Learning Brain modeling

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Live-constraint-based control for contact transitions

Live-constraint-based control for contact transitions

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： N. Sarkar Xiaoping Yun R. Ellis Department of Mechanical Engineering University of Hawai Honolulu HI USA Department of Electrical & Computer Engineering Naval Postgraduate School Monterrey CA USA Department of Computing and Information Science Queen's University Kingston Canada

The concept and theoretical derivation of a new, continuous controller for contact transition tasks was presented earlier by Sarkar et al. (1996). The design of this new controller was derived based on an analysis of impulsive constraints. Unlike other control methods that require switching control strategies before and after contacts, this design is able to accomplish contact transition tasks with a single control strategy, avoiding control discontinuities. In this paper, the proposed controller design is further developed, and results from computer simulation and physical experiments are presented to demonstrate the feasibility and effectiveness of the controller design.

关键词： Force control Motion control Position control Pi control Proportional control Contacts Manipulators Robust control Mechanical engineering Information science

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Adaptive obstacle avoidance with a neural network for operant conditioning: experiments with real robots

Adaptive obstacle avoidance with a neural network for operan...

引用

ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： P. Gaudiano C. Chang Department of Cognitive and Neural Systems Boston University Neurobotics Laboratory Boston MA USA

Gaudiano et al. (1996) have shown that a neural network model of classical and operant conditioning can be trained to control the movements of a wheeled mobile robot. The neural network learns to avoid obstacles as the robot moves around without supervision in a cluttered environment. The neural network does not require any knowledge about the quality or configuration of the sensors. In this article we report results using our neural network with the real mobile robot Khepera.

关键词： Neural networks Biological neural networks Mobile robots Animals Robot sensing systems Navigation Kinematics Psychology Programmable control Adaptive control

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A new approach to design fuzzy controllers for mobile robots navigation

A new approach to design fuzzy controllers for mobile robots...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： O. Aycard F. Charpillet J.-P. Haton CNRS INRIA Lorraine Batiment Loria CRIN Vandoeuvre-les-Nancy France

This article presents a new method to design, in two levels, fuzzy controller for reactive navigation of a mobile robot in a structured unknown environment. At the first level, adjacent sensors are grouped in areas and are used to define focal behaviors. These local behaviors are then gathered at the second level in order to define a global behavior. Two experiments with different local behaviors and different mechanism of integration are presented on our Nomad200 mobile robot.

关键词： Fuzzy control Robot control Mobile robots Navigation Infrared sensors Fuzzy logic Fuzzy systems control systems Tactile sensors Design methodology

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Learning the perceptual control manifold for sensor-based robot path planning

Learning the perceptual control manifold for sensor-based ro...

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ieee International symposium on computational intelligence in Robotics and automation (CIRA)

作者： M. Zeller K. Schulten R. Sharma Beckman Institute and Department of Physics University of Illinois Urbana-Champaign Urbana IL USA Department of Computer Science and Engineering Pennsylvania State University University Park PA USA

The perceptual control manifold is a concept that extends the notion of the robot configuration space to include sensor feedback for robot motion planning. In this paper, we propose a framework for sensor-based robot motion planning using the topology representing network algorithm to develop a learned representation of the perceptual control manifold. The topology preserving features of the neural network lend themselves to yield, after learning, a diffusion-based path planning strategy for flexible obstacle avoidance. Simulations on path control and flexible obstacle avoidance demonstrate the feasibility of this approach for motion planning and illustrate the potential for further robotic applications.

关键词： Robot sensing systems Motion control Motion planning Orbital robotics Robot motion Network topology Path planning Robot control Neurofeedback Neural networks

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