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Updated: Jul 7, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Automata learning and intelligent tertiary searching for stochastic point location.

B J Oommen¹, G Raghunath

¹Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 8, 2008

Summary

This study introduces a novel robot learning scheme for locating points on a line, utilizing a controlled random walk and adaptive search. The epsilon-optimal strategy enhances nonlinear optimization by determining optimal parameters for improved convergence.

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Last Updated: Jul 7, 2026

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Robotics
Machine Learning
Optimization Theory

Background:

Robots often need to locate specific points in an environment.
Existing methods for point localization in random environments may have limitations, such as operating in discretized spaces.

Purpose of the Study:

To develop a new, epsilon-optimal learning scheme for robots to locate a point on a line.
To apply this scheme to improve parameter selection in nonlinear optimization.

Main Methods:

A controlled random walk on the underlying space.
Intelligent space pruning using an adaptive tertiary search.
Integration of various learning principles.

Main Results:

The proposed learning scheme is demonstrated to be epsilon-optimal.
The strategy effectively addresses the challenge of parameter selection in nonlinear optimization, preventing sluggish convergence or oscillation.

Conclusions:

The new learning scheme offers an effective solution for point localization in uncertain environments.
This approach provides a robust method for determining optimal parameters in nonlinear optimization processes.