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

arXiv: https://arxiv.org/abs/2309.16884 · HTML: https://mlsanity.com/paper/2309.16884/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2023.

## Built on by (most cited first, 5 of 5)

- [VLA-Reasoner: Empowering Vision-Language-Action Models with Reasoning via Online Monte Carlo Tree Search](https://mlsanity.com/paper/2509.22643/) (2025)
- [MALinZero: Efficient Low-Dimensional Search for Mastering Complex Multi-Agent Planning](https://mlsanity.com/paper/2511.06142/) (2025)
- [NonZero: Interaction-Guided Exploration for Multi-Agent Monte Carlo Tree Search](https://mlsanity.com/paper/2605.00751/) (2026)
- [Navigating the Proximity-Safety Balance: Constraint Decomposition for Human Following in Pedestrian Crowds](https://mlsanity.com/paper/2608.10056/) (2026)
- [Towards Adaptive Interaction Strategies for Human Companion Robot via Deep Reinforcement Learning](https://mlsanity.com/paper/2609.25031/) (2026)

## Abstract (arXiv)

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.