Real-time map building and navigation for autonomous robots in unknown environments.

Area of Science:

• Robotics
• Artificial Intelligence
• Computer Science

Background:

• Autonomous robot navigation in unknown environments presents significant challenges.
• Effective simultaneous localization and mapping (SLAM) are crucial for robot autonomy.

Purpose of the Study:

• To develop an algorithmic solution for autonomous robot motion in completely unknown environments.
• To integrate map building and navigation processes for efficient exploration and path planning.

Main Methods:

• The approach alternates between map building using robot exteroceptive sensors and fuzzy logic for local representation integration into a global map.
• Navigation utilizes an A*-based planner to generate safe, local paths within the explored area, guiding exploration.
• The robot follows paths until encountering obstacles or reaching the explored area boundary.

Main Results:

• Experimental results with a NOMAD 200 mobile robot demonstrate real-time performance.
• The method proved effective in both static and moderately dynamic environments.
• Fuzzy logic enhances map building and modification efficiency, while iterative A* planning leverages local information.

Conclusions:

• The proposed method offers a robust solution for autonomous robot navigation in unknown environments.
• The combination of fuzzy logic for mapping and iterative A* for planning enables efficient and safe exploration.
• The system achieves real-time performance, making it suitable for practical robotic applications.