# HPHS: Hierarchical Planning based on Hybrid Frontier Sampling for Unknown Environments Exploration

arXiv: https://arxiv.org/abs/2407.10660 · HTML: https://mlsanity.com/paper/2407.10660/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2024.

## Built on by (most cited first, 3 of 3)

- [HEADER: Hierarchical Robot Exploration via Attention-Based Deep Reinforcement Learning with Expert-Guided Reward](https://mlsanity.com/paper/2510.15679/) (2025)
- [FARE: Fast-Slow Agentic Robotic Exploration](https://mlsanity.com/paper/2601.14681/) (2026)
- [GRATE: a Graph transformer-based deep Reinforcement learning Approach for Time-efficient autonomous robot Exploration](https://mlsanity.com/paper/2509.12863/) (2025)

## Abstract (arXiv)

Rapid sampling from the environment to acquire available frontier points and timely incorporating them into subsequent planning to reduce fragmented regions are critical to improve the efficiency of autonomous exploration. We propose HPHS, a fast and effective method for the autonomous exploration of unknown environments. In this work, we efficiently sample frontier points directly from the LiDAR data and the local map around the robot, while exploiting a hierarchical planning strategy to provide the robot with a global perspective. The hierarchical planning framework divides the updated environment into multiple subregions and arranges the order of access to them by considering the overall revenue of the global path. The combination of the hybrid frontier sampling method and hierarchical planning strategy reduces the complexity of the planning problem and mitigates the issue of region remnants during the exploration process. Detailed simulation and real-world experiments demonstrate the effectiveness and efficiency of our approach in various aspects. The source code will be released to benefit the further research.