Fuzzy mobile-robot positioning in intelligent spaces using wireless sensor networks

Area of Science:

• Robotics
• Artificial Intelligence
• Sensor Networks

Background:

• Mobile robot localization is crucial for autonomous systems.
• Wireless Sensor Networks (WSNs) offer a scalable solution for data collection.
• Existing localization methods struggle with noisy sensor data.

Purpose of the Study:

• To develop and evaluate a fuzzy logic-based method for mobile robot localization.
• To address the challenge of unreliable range measurements from WSNs.
• To compare the fuzzy logic approach with probabilistic localization techniques.

Main Methods:

• Utilized fuzzy logic for modeling and managing uncertain inter-node range measurements.
• Estimated range measurements using radio frequency signal strength attenuation.
• Implemented a mobile robot localization system within an Intelligent Space.

Main Results:

• The fuzzy logic method demonstrated robustness in handling noisy and unreliable sensor data.
• Fuzzy logic successfully modeled and processed uncertain information from WSNs.
• The proposed fuzzy approach outperformed a probabilistic technique in highly uncertain scenarios.

Conclusions:

• Fuzzy logic provides an effective solution for mobile robot localization in challenging WSN environments.
• The developed method enhances the reliability of localization despite sensor unreliability.
• This approach offers a viable alternative for robust robot navigation in intelligent spaces.