TIPS: Tiered Information-Rich Planning Strategy for Efficient UGV Autonomous Exploration

Abstract

In this letter, we propose a tiered systematic framework 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 distribution 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 knowledge. 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 different 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, with superior performance in narrow regions. Subsequent real-world experiments further validate the strong potential of our proposed method for practical applications.