Obstacle Avoidance in Snake Robot Navigation using Artificial Potential Field

Abstract

This paper introduces a novel approach to advancing snake robot navigation through the integration of head tracking and obstacle avoidance using the Artificial Potential Field (APF) algorithm. In this paradigm, head tracking is employed as the primary mechanism for following a predetermined reference trajectory, while obstacle avoidance is seamlessly integrated to enhance the robot's adaptability and responsiveness in complex environments. Head tracking plays a pivotal role in the methodology, serving as the foundation for precise navigation along a predefined trajectory. By closely aligning the robot's orientation with a reference path, it improved the overall accuracy and efficiency of its movement. This approach is particularly valuable in scenarios where specific routes or points of interest need to be precisely traversed. To augment the snake robot's navigation capabilities, the APF algorithm is integrated for real-time ob-stacle avoidance. The robot dynamically senses its environment and responds to obstacles by generating attractive forces towards the trajectory and repulsive forces around obstacles. The paper provides a comprehensive analysis of the proposed methodology, including simulation results.