TIPS: Tiered Information-Rich Planning Strategy for Efficient UGV Autonomous Exploration
Zhuoxuan Wang, Shuguo Pan, Jinle Xu, Xianlu Tao, Wang Gao, Qiang Wang
AI summary
Problem
Existing AGV exploration methods suffer from greedy strategies that cause redundant paths and dead zones, while struggling to accurately map narrow regions due to inefficient sensor models and high computational costs.
Approach
The proposed TIPS framework uses a local layer with a multi-cause triggering sensor model and optimized Bayesian optimization for fast target selection, paired with a global layer that maintains an information-rich sparse roadmap to guide dead-zone escape.
Key results
- Novel Multi-cause Triggering Sensor Model (MTSM) enhances map updates in narrow passages
- Probabilistic frontier definition optimizes Bayesian optimization training pool for faster convergence
- Information-Rich Sparse Roadmap (IRSR) enables heuristic global guidance to escape dead zones
- Simulations and real-world tests demonstrate superior runtime, efficiency, and coverage over state-of-the-art methods
Why it matters
Provides a practical, computationally efficient solution for AGVs to achieve complete and fast exploration in complex, constrained environments like underground or industrial sites.
Abstract
In this letter, we propose a tiered systematic frame- work to enhance the overall efficiency and environmental coverage of autonomous exploration for Autonomous Ground Vehicle (AGV) in complex environments with narrow regions.At the local level, we introduce a novel Multi-cause Triggering Sensor Model (MTSM) to improve informative observation acquisition in narrow regions. Furthermore, the Frontier set is defined from a probabilistic dis- tribution perspective and utilized to optimize the initial training pool of Bayesian optimization, thereby accelerating convergence toward the optimal navigation target point. At the global level, we incrementally maintain an Information-Rich Sparse Roadmap (IRSR) by leveraging accumulated historical exploration knowl- edge. When a dead zone situation is detected, the heuristic guidance is activated and realized by graph search considering information content and distance between IRSR vertices, enabling AGV to maintain a continuous and sustained exploration process. Three simulation scenarios with increasing complexity are designed, in which comprehensive comparisons and evaluations against differ- ent types of state-of-the-art approaches are conducted. The results demonstrate that our framework achieves a favorable balance between algorithm runtime, exploration efficiency and coverage completeness,withsuperiorperformanceinnarrowregions.Subse- quent real-world experiments further validate the strong potential of our proposed method for practical applications.