Real-Time Static Obstacle Avoidance Using Sensor Fusion in Robotics

Obstacle avoidance is a critical aspect of robotics that plays a vital role in ensuring safe and efficient operations. Detecting obstacles in complex real-time environments poses several challenges in robotics. For example, cameras may struggle to detect objects in low-light conditions or with occlusions and integrating data from multiple sensors adds complexity to the detection process. To overcome these challenges, we propose a new sensor fusion strategy to improve the performance and reliability of static obstacle avoidance robotic systems through the integration of infrared (IR) and ultrasonic sensors. The objective is to enhance the robot's ability to detect and avoid stationary objects efficiently and reliably. The research methodology involves the development and integration of a sensor fusion algorithm designed to seamlessly integrate data from IR and ultrasonic sensors. This fusion enhances the system's ability to accurately perceive and interpret its environment, thereby enabling more robust decision-making processes. We conducted a series of experiments to evaluate the effectiveness of the proposed sensor fusion approach in various indoor scenarios, showcasing its superior performance compared to traditional single-sensor systems. The results show that our approach significantly enhances the static obstacle avoidance system's adaptability in real-time obstacle detection and navigation, as well as its overall reliability in diverse environmental conditions for autonomous robotic systems.