地空协同机器人通过分层图结构智能判断何时派无人机探索。
A Hierarchical Graph-Based Terrain-Aware Autonomous Navigation Approach for Complementary Multimodal Ground-Aerial Exploration
- 用分层图表示地形，融合几何与语义可通行性信息。
- 地面机器人自主识别不可行区域并触发无人机部署。
- 适合复杂地形下的多模态协同探索任务。
在未知环境中实现自主导航是机器人学的核心挑战，尤其是在协调地面与空中机器人以最大化探索效率方面。本文提出一种新方法，利用分层图表示环境，编码几何与语义可通行性信息。该框架使机器人能够计算共享置信度指标，帮助地面机器人评估地形，并决定何时部署空中机器人以拓展探索范围。路径的可信度基于预测体积增益、路径可通行性及碰撞风险等因素。采用多分辨率地图维护分层图结构，高效表达可通行性与前沿信息。在真实地下探索场景中评估表明，该方法使地面机器人能自主识别已不可通行但适合空中部署的区域。通过此分层结构，地面机器人可选择性共享经置信度评估的前沿目标信息，使空中机器人得以越过障碍继续探索。
原文摘要 · Abstract (English)
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.
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