  We propose a novel methodology for robotic follow-ahead applications that
address the critical challenge of obstacle and occlusion avoidance. Our
approach effectively navigates the robot while ensuring avoidance of collisions
and occlusions caused by surrounding objects. To achieve this, we developed a
high-level decision-making algorithm that generates short-term navigational
goals for the mobile robot. Monte Carlo Tree Search is integrated with a Deep
Reinforcement Learning method to enhance the performance of the decision-making
process and generate more reliable navigational goals. Through extensive
experimentation and analysis, we demonstrate the effectiveness and superiority
of our proposed approach in comparison to the existing follow-ahead
human-following robotic methods. Our code is available at
https://github.com/saharLeisiazar/follow-ahead-ros.
