Predictive fuzzy control of an autonomous mobile robot with forecast learning function

Maeda, Mikio; Shimakawa, Manabu; Murakami, Shuta

doi:10.1016/0165-0114(94)00271-8

Cited by 25 publications

(7 citation statements)

References 7 publications

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“…A few proposals have appeared in the literature that use different techniques for obtaining the data needed to build a fuzzy controller; these include building the controller from a fuzzy model of the system [119], and extracting these data from the observation of the actions of a human operator [115,69]. Several researches have also explored the use of learning techniques [10,70,23,42,51,123,19].…”

Section: Discussionmentioning

confidence: 99%

“…The second approach has been used by Yen and Pfluger [131] and by Baxter and Bumby [7] for integrating path tracking and obstacle avoidance (only tested in simulation); and by Maeda et al [70] for integrating vision-based wall following and obstacle avoidance on a Hero 2000 robot. The last work is peculiar in that the authors use predictive fuzzy control for the obstacle avoidance part.…”

Section: Complex Behaviorsmentioning

confidence: 99%

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The uses of fuzzy logic in autonomous robot navigation

Saffiotti¹

1997

Soft Computing - A Fusion of Foundations, Methodologies and App

311

140

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Complex Behaviorsmentioning

confidence: 99%

The uses of fuzzy logic in autonomous robot navigation

Saffiotti¹

1997

Soft Computing - A Fusion of Foundations, Methodologies and App

311

140

View full text Add to dashboard Cite

show abstract

“…Fuzzy control systems such as those developed by Maeda et al (1995) and Castellano et al (1997) directly map the sensory input to control commands without using any internal representation of the data. Output commands are given weightings depending upon the comparison with pre-set patterns within the set of input data, allowing the robot to interact with an environment that it does not have complete understanding of.…”

Section: Roboticsmentioning

confidence: 99%

The state of play in machine/environment interactions

Aitkenhead

McDonald

2006

Artif Intell Rev

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Due to the breadth of the subject, it is no longer possible to provide a review of all of the work being carried out in the field of Artificial Intelligence. However, a more localised review of research taking place in the overlap between engineering, AI and psychology can be meaningfully performed. We show here that while there have been marked successes in the past few years, there is an identifiable set of 'classic' problems that remain to be solved, and which largely direct the work ongoing in this area. This review aims to discuss the directions being taken at the current time, in particular the developing and maturing possibilities provided by neural networks and evolutionary computation, and by the use of our knowledge of the mind in developing artificial agents capable of mimicking our abilities to interact with the environment.

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“…Other applications of PKBC include car parking [32] and the control of mobile robots [33]. In both cases, the controller uses prediction to evaluate a set of candidate actions and choose the one deemed most appropriate.…”

Section: • Complementnotmentioning

confidence: 99%

Jitter and Wander Reduction for a SONET DS3 Desynchronizer Using Predictive Fuzzy Control

Stanton

2000

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Predictive fuzzy control of an autonomous mobile robot with forecast learning function

Cited by 25 publications

References 7 publications

The uses of fuzzy logic in autonomous robot navigation

The uses of fuzzy logic in autonomous robot navigation

The state of play in machine/environment interactions

Jitter and Wander Reduction for a SONET DS3 Desynchronizer Using Predictive Fuzzy Control

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