A Hierarchical Graph-Based Terrain-Aware Autonomous Navigation Approach for Complementary Multimodal Ground-Aerial Exploration

TL;DR

This paper introduces a hierarchical graph-based framework for terrain-aware autonomous navigation that coordinates ground and aerial robots for efficient exploration in unknown environments.

Contribution

It presents a novel hierarchical graph approach encoding traversability and semantic info, enabling autonomous decision-making for deploying aerial robots.

Findings

Effective in subterranean exploration scenarios

Enables autonomous terrain assessment and robot deployment decisions

Improves exploration efficiency through hierarchical environment representation

Abstract

Autonomous navigation in unknown environments is a fundamental challenge in robotics, particularly in coordinating ground and aerial robots to maximize exploration efficiency. This paper presents a novel approach that utilizes a hierarchical graph to represent the environment, encoding both geometric and semantic traversability. The framework enables the robots to compute a shared confidence metric, which helps the ground robot assess terrain and determine when deploying the aerial robot will extend exploration. The robot's confidence in traversing a path is based on factors such as predicted volumetric gain, path traversability, and collision risk. A hierarchy of graphs is used to maintain an efficient representation of traversability and frontier information through multi-resolution maps. Evaluated in a real subterranean exploration scenario, the approach allows the ground robot to autonomously identify zones that are no longer traversable but suitable for aerial deployment. By leveraging this hierarchical structure, the ground robot can selectively share graph information on confidence-assessed frontier targets from parts of the scene, enabling the aerial robot to navigate beyond obstacles and continue exploration.