Hybrid Map-Based Path Planning for Robot Navigation in Unstructured Environments

TL;DR

This paper introduces a hybrid map-based path planning method combining 2D and 2.5D maps to improve safety and efficiency for ground robots navigating unstructured outdoor environments, validated through simulations and real-world tests.

Contribution

It proposes a novel hybrid map representation and a path planning algorithm that explicitly considers robot safety constraints during traversability estimation.

Findings

Outperforms baseline methods in safety and path quality.

Demonstrates effectiveness in both simulated and real environments.

Provides publicly available implementation for reproducibility.

Abstract

Fast and accurate path planning is important for ground robots to achieve safe and efficient autonomous navigation in unstructured outdoor environments. However, most existing methods exploiting either 2D or 2.5D maps struggle to balance the efficiency and safety for ground robots navigating in such challenging scenarios. In this paper, we propose a novel hybrid map representation by fusing a 2D grid and a 2.5D digital elevation map. Based on it, a novel path planning method is proposed, which considers the robot poses during traversability estimation. By doing so, our method explicitly takes safety as a planning constraint enabling robots to navigate unstructured environments smoothly.The proposed approach has been evaluated on both simulated datasets and a real robot platform. The experimental results demonstrate the efficiency and effectiveness of the proposed method. Compared to state-of-the-art baseline methods, the proposed approach consistently generates safer and easier paths for the robot in different unstructured outdoor environments. The implementation of our method is publicly available at https://github.com/nubot-nudt/T-Hybrid-planner.