Sensor-Fusion Based Navigation for Autonomous Mobile Robot.

Area of Science:

• Robotics and Artificial Intelligence
• Sensor Fusion and Navigation Systems

Background:

• Rapid advancements in navigation systems are met with increasingly complex robotic tasks.
• Challenges include dynamic obstacles, multi-robot coordination, and navigating inaccessible environments.
• Cognitive tasks require object evaluation beyond simple detection, necessitating advanced AI.

Purpose of the Study:

• To review and compare sensors used in mobile robot localization.
• To analyze the integration of sensors with path-planning methods.
• To evaluate sensor fusion techniques and propose a concept for channel robot navigation.

Main Methods:

• Comprehensive literature review of sensors for mobile robot localization.
• Comparative analysis of sensor combinations, highlighting advantages and disadvantages.
• Evaluation of artificial intelligence (AI) methods for sensor data processing and trajectory generation.

Main Results:

• Identification of key sensors and their limitations in complex robotic navigation.
• Analysis of sensor fusion techniques for enhanced environmental perception.
• Presentation of a novel concept for channel robot navigation based on existing research.

Conclusions:

• Sensor fusion and AI are crucial for overcoming challenges in modern robotic navigation.
• Effective integration of sensors and path-planning algorithms is essential for robust performance.
• The proposed channel robot navigation concept offers a potential solution for complex environments.