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Gradient method in function space for solving a minimum path problem

Gradient method in function space for solving a minimum path...

引用

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

作者： E.M. Khazen University of Maryland College Park MD USA

An algorithm of searching for the optimal trajectory with the minimal cost W(x) of reaching the final state x/sub fin/ from the initial state x is presented. A system of ordinary differential equations is suggested to determine an optimal trajectory x(t) and an optimal control u(t). The algorithm represents the gradient method in function space. This work is a continuation of the author's previous work (1998). The close-to-optimal trajectory x(t) and the control u(t) are determined by successive approximations. Learning consists in estimation of the unknown a priori minimal cost W(x) and /spl part/W(x)//spl part/x on the basis of analysis of the trial trajectories x(f) obtained earlier.

关键词： Gradient methods Optimal control Cost function Equations Educational institutions Programmable control Adaptive control Process control Dynamic programming Transforms

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Walking biped robot with distributed hierarchical control system

Walking biped robot with distributed hierarchical control sy...

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

作者： J. Cronin R. Frost R. Willgoss School of Mechanical Engineering University of New South Wales Australia

An anthropomorphic biped robot is presented. The advantage of legged locomotion in rough terrain and in confined spaces has been investigated extensively in recent years. Biped robotic research has focused on imitation of human anthropomorphic form with the ultimate goal being the creation of the "Droid" or artificial human. An alternative use for biped robots is proposed as materials handling platforms for use in confined spaces or areas where irregularities in terrain prohibit access to conventional materials handling vehicles. The operational criteria and design strategy for the device, which has been built, are defined. The major components of the robot's sensory, navigation, actuation and control systems are highlighted. Finally, the control software strategy and initial results are presented.

关键词： Legged locomotion Distributed control control systems Orbital robotics Anthropomorphism Humans Robot sensing systems Materials handling Space vehicles Navigation

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Adaptive control of an autonomous underwater vehicle: experimental results on ODIN

Adaptive control of an autonomous underwater vehicle: experi...

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

作者： G. Antonelli S. Chiaverini N. Sarkar M. West PRISMA Laboratory Dipartimento di Informatica e Sistemistica Università degli Studi di Napoli Federico II Napoli Italy Dipartimento di Automazione Elettromagnetismo Ingegneria dell'Informazione e Matematica Industriale Università degli Studi di Cassino Cassino Frosinone Italy Autonomous Systems Laboratory Mechanical Engineering Department University of Hawai Honolulu HI USA

This paper presents a six degree-of-freedom adaptive controller for autonomous underwater vehicles (AUVs). The proposed control law adopts quaternions to represent attitude errors and thus avoids representation singularities that occur when using Euler angle description of orientation. The adaptive controller has been implemented and experimentally validated on ODIN (Omni-Directional Intelligent Navigator)-an AUV that has been designed and built at the University of Hawaii.

关键词： Adaptive control Underwater vehicles Quaternions Programmable control Attitude control Vehicle dynamics Vectors Symmetric matrices Navigation Remotely operated vehicles

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Detection of aircraft-pilot-coupling caused oscillations

Detection of aircraft-pilot-coupling caused oscillations

引用

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

作者： A.T. Koivo D.W. Repperger A.J. Koivo Purdue University West Lafayette IN USA Air Force Research Laboratory OH USA

The closed loop system consisting of an aircraft and a pilot can exhibit oscillations along any of the basic axis. These oscillations are often referred to as the pilot-induced-oscillations (PIOs) caused by the aircraft-pilot-coupling effects. For the detection of the PIOs, we propose the use of finite-state machine model. The paper discusses the identification of this model from the measurements.

关键词： Closed loop systems Military aircraft Hidden Markov models control systems Process design Automata Feedback loop Teleoperators Interconnected systems Computer crashes

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A rules and communication based multiple robots transportation system

A rules and communication based multiple robots transportati...

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

作者： Tao Wei Min Li Zhe How Khee Yin Goh Cher Hiang Lou Kok Yong DSO National Laboratories Singapore

This paper describes a multiple mobile robot system, called decentralized robot transportation system. A decentralized approach is proposed to control and manage a fleet of autonomous guided vehicles (AGV) for transportation in a structured environment, such as transshipment tasks in harbors and airports, or goods delivery in factory floor. Each individual robot has a two level path planning capability: autonomous and coordination behaviors. A distinguished feature of our approach is to use communication based coordination rules for traffic control. A demonstration system has been built. Numerous testing results have shown the effectiveness of our approach.

关键词： Robot kinematics Mobile robots Mobile communication Communication system control Environmental management Remotely operated vehicles Air transportation Airports Production facilities Path planning

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The co-evolution of model parameters and control programs in evolutionary robotics

The co-evolution of model parameters and control programs in...

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

作者： G.B. Parker Department of Computer Science Connecticut College New London CT USA

Evolutionary robotics is a research area that makes use of the various forms of evolutionary computation to provide a means of designing robot control systems. In this paper, we introduce a new way of integrating the actual robot and its model during evolutionary computation. This method, which involves the co-evolution of model parameters, is applied to the problem of learning gaits for hexapod robots. The form of evolutionary computation used is the cyclic genetic algorithm (CGA), which was introduced in previous work (Parker et al. (1996)) to deal with the issue of evolving controllers for cyclic behaviors. Tests done in simulation show that the CGA operating on the co-evolving model of the robot can adapt to changes in the robot's capabilities to provide a system of any-time learning.

关键词： Evolutionary computation Testing Robot control Real time systems Robot sensing systems Computer science Educational institutions Adaptive systems Joining processes Gratings

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Soft computing-based design and control for mobile robot path tracking

Soft computing-based design and control for mobile robot pat...

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

作者： A. Homaifar D. Battle E. Tunstel NASA ACE Center Department of Electrical Engineering North Carolina Agriculture and Technical State University Greensboro NC USA Lucent Technologies Inc. Whippany NJ USA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

A variety of evolutionary algorithms, operating according to Darwinian concepts, have been proposed to approximately solve problems of common engineering applications. Increasingly common applications involve automatic learning of nonlinear mappings that govern the behavior of control systems. In many cases where robot control is of primary concern, the systems used to demonstrate the effectiveness of evolutionary algorithms often do not represent practical robotic systems. In this paper, genetic programming (GP) is the evolutionary strategy of interest. It is applied to learn fuzzy control rules for a practical autonomous vehicle steering control problem, namely, path tracking. GP handles the simultaneous evolution of membership functions and rule bases for the fuzzy path tracker. As a matter of practicality, robustness of the genetically evolved fuzzy controller is demonstrated by examining the effects of sensor measurement noise and an increase in the robot's nominal forward velocity.

关键词： Robot control Mobile robots Genetic programming Automatic control Evolutionary computation Fuzzy control Nonlinear control systems control systems Robotics and automation Remotely operated vehicles

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Evolving control for distributed micro air vehicles

Evolving control for distributed micro air vehicles

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

作者： A.S. Wu A.C. Schultz A. Agah Naval Research Laboratory Inc. Washington D.C. DC USA Department of EECS University of Kansas Lawrence KS USA

We focus on the task of large area surveillance. Given an area to be surveilled and a team of micro air vehicles (MAVs) with appropriate sensors, the task is to dynamically distribute the MAVs appropriately in the surveillance area for maximum coverage based on features present on the ground, and to adjust this distribution over time as changes in the team or on the ground occur. We have developed a system that learn rule sets for controlling the individual MAVs in a distributed surveillance team. Since each rule set governs an individual MAV, control of the overall behavior of the entire team is distributed; there is no single entity controlling the actions of the entire team. Currently, all members of the MAV team utilize the same rule set; specialization of individual MAVs through the evolution of unique rule sets is a logical extension to this work. A genetic algorithm is used to learn the MAV rule sets.

关键词： Distributed control Vehicles Surveillance Laboratories Payloads Robot sensing systems control systems Robustness Fault tolerance Air transportation

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Learning to grasp by using visual information

Learning to grasp by using visual information

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

作者： A. Anglani F. Taurisano R. de Giuseppe C. Distante Dipartimento di Ingegneria dell'Innovazione Università di Lecce Lecce Italy

This paper presents a solution to the problem of manipulation control: target identification and grasping. The proposed controller is designed for a real platform in combination with a monocular vision system. The objective of the controller is to learn an optimal policy to reach and to grasp a spherical object of known size, randomly placed in the environment. In order to accomplish this, the task has been treated as a reinforcement problem, in which the controller learns by a trial and error approach the situation-action mapping. The optimal policy is found by using the Q-learning algorithm, a model free reinforcement learning technique, that rewards actions that move the arm closer to the target. The vision system uses geometrical computation to simplify the segmentation of the moving target and determines an estimate of the target parameters. The simulated knowledge has been ported on the industrial robot manipulator PUMA 560. Experimental results demonstrate the effectiveness of the adaptive controller that does not require an explicit global target position using direct perception of the environment.

关键词： Machine vision control systems Optimal control Size control Error correction Learning Computer vision computational modeling Electrical equipment industry Service robots

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A hybrid genetic algorithm for optimizing sensing parameters in 3D motion estimation applications

A hybrid genetic algorithm for optimizing sensing parameters...

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

作者： P. Tsui O.A. Basir School of Engineering University of Guelph Guelph ONT Canada

This paper introduces an active vision approach for object motion estimation. The approach is formulated as a problem of controlling the pose of a vision system with the goal of minimizing the uncertainties of the motion estimates. A Kalman filter is employed as the object motion estimation algorithm. The uncertainties of the motion estimates are represented by the variances of the estimates produced by Kalman filter. These variances are updated by a Riccati equation which is constructed as a function of the vision system parameters. A hybrid genetic algorithm is proposed to search for the optimal vision system parameters that minimize the uncertainties of the motion estimates. This hybrid algorithm incorporates the concept of Boltzman's probability from simulated annealing. To improve the speed and accuracy of the proposed algorithm an adaptive gradient based search method is also developed.

关键词： Genetic algorithms Motion estimation Machine vision Motion control control systems Application software Uncertainty Riccati equations Computer vision Motion measurement

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