Sensor Fusion for Enhanced Robotic Perception

Abstract

In autonomous robots, attaining accurate navigation is a significant problem, requiring improvements in sensor fusion methodologies. This paper examines the crucial function of Micro-Electro-           
Mechanical Systems (MEMS) in augmenting sensor fusion for autonomous navigation. The urgent challenge of attaining precise and instantaneous navigation in fluctuating situations has necessitated the development of novel technologies. The current literature indicates a research deficiency in the seamless integration of MEMS sensors for effective navigation. Traditional sensor fusion techniques often encounter constraints in managing varied and rapidly evolving environmental variables; however, MEMS provide a viable solution to these obstacles. The compact dimensions, little power use, and elevated sensitivity of MEMS sensors render them optimal for delivering comprehensive and dependable data for navigation applications. This study utilizes a full integration of MEMS sensors, including accelerometers, gyroscopes, and magnetometers, inside a single sensor fusion architecture. This framework utilizes sophisticated algorithms to effectively integrate data from several sensors, alleviating the limits of individual sensors and improving overall accuracy. The use of MEMS sensors seeks to enhance the comprehensive awareness of the robot's environment, hence permitting superior decision-making in navigation tasks. The findings of our investigation demonstrate a significant improvement in the precision and efficacy of autonomous navigation inside dynamic situations. MEMS-enhanced sensor fusion demonstrates efficacy in overcoming the hurdles presented by unexpected terrains and obstructions. The robot, outfitted with MEMS sensors, exhibits improved flexibility and reactivity, highlighting its potential for practical applications.