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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

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Multirobot object localization: a fuzzy fusion approach.

Kevin LeBlanc¹, Alessandro Saffiotti

¹Department of Technology, Center for Applied Autonomous Sensor Systems, Orebro University, Orebro, Sweden.

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

|April 14, 2009

Summary

This study introduces a novel fuzzy logic approach for multi-robot object localization, achieving consensus by fusing uncertain position data. The method accurately propagates self-localization uncertainty for improved object position estimates.

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Area of Science:

Robotics
Artificial Intelligence
Information Fusion

Background:

Multirobot systems require accurate object localization.
Existing methods often achieve compromise rather than consensus in data fusion.
Propagating self-localization uncertainty is crucial for reliable object position estimation.

Purpose of the Study:

To develop a novel fuzzy logic-based approach for multi-robot object localization.
To achieve consensus among information sources during data fusion.
To fully propagate self-localization uncertainty to object-position estimates.

Main Methods:

Utilizing fuzzy logic and fuzzy sets to represent uncertain position information.
Employing fuzzy intersection for fusing uncertain data from multiple sources.

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Published on: December 15, 2023

Developing a method to fully propagate self-localization uncertainty.

Main Results:

The proposed method achieves consensus among information sources.
Demonstrated accurate propagation of self-localization uncertainty.
Systematic experiments characterized performance across various input error types and amounts.

Conclusions:

Fuzzy logic offers a robust framework for multi-robot object localization.
The consensus-based fusion approach enhances reliability in uncertain environments.
The method provides a comprehensive understanding of performance under varying error conditions.