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.

翻译：我们提出了一种针对机器人前向跟随应用的新方法，旨在解决障碍物与遮挡规避这一关键挑战。该方法能够有效引导机器人导航，同时确保避免由周围物体引发的碰撞和遮挡。为实现此目标，我们开发了一种高层决策算法，为移动机器人生成短期导航目标。蒙特卡洛树搜索与深度强化学习方法相融合，以提升决策过程的性能并生成更可靠的导航目标。通过广泛的实验与分析，我们证明了所提方法相较于现有前向跟随人体机器人方法的有效性与优越性。我们的代码开源在 https://github.com/saharLeisiazar/follow-ahead-ros。