An MCTS-DRL Based Obstacle and Occlusion Avoidance Methodology in Robotic Follow-Ahead Applications

TL;DR

This paper introduces a novel obstacle and occlusion avoidance method for robotic follow-ahead applications by combining Monte Carlo Tree Search with Deep Reinforcement Learning to improve navigation reliability.

Contribution

The paper presents a new integrated MCTS-DRL approach for obstacle and occlusion avoidance in robotic follow-ahead tasks, enhancing decision-making and navigation safety.

Findings

Demonstrated superior performance over existing methods

Validated effectiveness through extensive experiments

Achieved reliable obstacle and occlusion avoidance

Abstract

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.