机器人前方跟随应用中一种基于 MCTS-DRL 的障碍物与遮挡规避方法
An MCTS-DRL Based Obstacle and Occlusion Avoidance Methodology in Robotic Follow-Ahead Applications
AI总结：
针对机器人前方跟随中的碰撞与遮挡问题，提出融合 MCTS 和深度强化学习的高层决策算法生成短期导航目标，并通过实验验证其优于现有方法。
摘要：
我们提出了一种用于机器人前方跟随应用的新颖方法，以应对障碍物和遮挡规避这一关键挑战。我们的方法能够有效地为机器人导航，同时确保规避由周围物体导致的碰撞和遮挡。为实现这一目标，我们开发了一种高层决策算法，用于为移动机器人生成短期导航目标。该方法将 Monte Carlo Tree Search（MCTS，蒙特卡洛树搜索）与 Deep Reinforcement Learning（DRL，深度强化学习）方法相结合，以提升决策过程的性能，并生成更可靠的导航目标。通过大量实验和分析，我们证明，与现有的前方跟随式人类跟随机器人方法相比，所提方法具有有效性和优越性。我们的代码可在 https://github.com/saharLeisiazar/follow-ahead-ros 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
