Autonomous Robot Navigation Using Adaptive Potential Field

This work presents a novel approach to mobile robot autonomous navigation, utilizing enhanced artificial potential fields in conjunction with path planning,obstacle avoidance and SLAM . In traditional adaptive potential fields, which integrate sensor data to enable robots to navigate safely and efficiently through various environments. The robot's perception of its surroundings, obtained through sensors like cameras, ultrasonic devices, is processed to create a dynamic potential field that reflects the spatial distribution of obstacles, goals, and other relevant features.We propose the design and implementation of an autonomous robot-based real-time vision-based system for detecting and tracking features in a structured environment ensuring robust obstacle avoidance and path planning. Keywords- Adaptive Potential Fields, Autonomous Navigation, Autonomous Systems, Machine Learning in Robotics, Motor driver, Obstacle Avoidance, Path Planning, Raspberry Pi, Real-time Adaptation, ROS (Robot Operating System), Sensor-based Navigation, Servo Motor, SLAM (Simultaneous Localization and Mapping),Ultrasonic Sensors