College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Sensors (Basel). 2023 May 15;23(10):4766. doi: 10.3390/s23104766.
Autonomous exploration and mapping in unknown environments is a critical capability for robots. Existing exploration techniques (e.g., heuristic-based and learning-based methods) do not consider the regional legacy issues, i.e., the great impact of smaller unexplored regions on the whole exploration process, which results in a dramatic reduction in their later exploration efficiency. To this end, this paper proposes a Local-and-Global Strategy (LAGS) algorithm that combines a local exploration strategy with a global perception strategy, which considers and solves the regional legacy issues in the autonomous exploration process to improve exploration efficiency. Additionally, we further integrate Gaussian process regression (GPR), Bayesian optimization (BO) sampling, and deep reinforcement learning (DRL) models to efficiently explore unknown environments while ensuring the robot's safety. Extensive experiments show that the proposed method could explore unknown environments with shorter paths, higher efficiencies, and stronger adaptability on different unknown maps with different layouts and sizes.
自主探索未知环境是机器人的一项关键能力。现有的探索技术（例如基于启发式和基于学习的方法）没有考虑到区域遗留问题，即较小未探索区域对整个探索过程的巨大影响，这导致它们的后期探索效率急剧降低。为此，本文提出了一种局部和全局策略（LAGS）算法，该算法将局部探索策略与全局感知策略相结合，考虑并解决自主探索过程中的区域遗留问题，以提高探索效率。此外，我们进一步整合了高斯过程回归（GPR）、贝叶斯优化（BO）采样和深度强化学习（DRL）模型，以在确保机器人安全的同时，有效地探索未知环境。大量实验表明，该方法可以在不同布局和大小的不同未知地图上以更短的路径、更高的效率和更强的适应性探索未知环境。
