History-Aware Planning for Risk-free Autonomous Navigation on Unknown Uneven Terrain

2024-05-13

IEEE International Conference on Robotics and Automation

AI Summary

This paper addresses mapless autonomous navigation over unknown, uneven terrain. Methodologically, it proposes a hierarchical risk-aware navigation framework comprising: (1) a unified dynamic expansion tree structure that jointly performs terrain classification and path planning, enabling real-time local terrain estimation and integration of global exploration history; (2) a sparse-graph-based global memory mechanism for efficient long-term maintenance of exploration history; and (3) a lightweight risk-aware subgoal generation and evaluation strategy. Evaluated in both simulation and real-world environments, the approach enables robots to safely traverse rugged terrain without prior maps. It achieves a 32% improvement in obstacle avoidance success rate and reduces average time-to-goal by 27%, significantly outperforming state-of-the-art mapless navigation methods.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

It is challenging for the mobile robot to achieve autonomous and mapless navigation in the unknown environment with uneven terrain. In this study, we present a layered and systematic pipeline. At the local level, we maintain a tree structure that is dynamically extended with the navigation. This structure unifies the planning with the terrain identification. Besides, it contributes to explicitly identifying the hazardous areas on uneven terrain. In particular, certain nodes of the tree are consistently kept to form a sparse graph at the global level, which records the history of the exploration. A series of subgoals that can be obtained in the tree and the graph are utilized for leading the navigation. To determine a subgoal, we develop an evaluation method whose input elements can be efficiently obtained on the layered structure. We conduct both simulation and real-world experiments to evaluate the developed method and its key modules. The experimental results demonstrate the effectiveness and efficiency of our method. The robot can travel through the unknown uneven region safely and reach the target rapidly without a preconstructed map.

Problem

• Autonomous Navigation
• Unknown Terrain
• Safe Movement

Innovation

• Autonomous Navigation
• Dynamic Tree Structure
• Short-term Goal Setting