Autonomous exploration is a new technology in the field of robotics that has found widespread application due to its objective to help robots independently localize, scan maps, and navigate any terrain without human control. Up to present, the sampling-based exploration strategies have been the most effective for aerial and ground vehicles equipped with depth sensors producing three-dimensional point clouds. Those methods utilize the sampling task to choose random points or make samples based on Rapidly-exploring Random Trees (RRT). Then, they decide on frontiers or Next Best Views (NBV) with useful volumetric information. However, most state-of-the-art sampling-based methodology is challenging to implement in two-dimensional robots due to the lack of environmental knowledge, thus resulting in a bad volumetric gain for evaluating random destinations. This study proposed an enhanced sampling-based solution for indoor robot exploration to decide Next Best View (NBV) in 2D environments. Our method makes RRT until have the endpoints as frontiers and evaluates those with the enhanced utility function. The volumetric information obtained from environments was estimated using non-uniform distribution to determine cells that are occupied and have an uncertain probability. Compared to the sampling-based Frontier Detection and Receding Horizon NBV approaches, the methodology executed performed better in Gazebo platform-simulated environments, achieving a significantly larger explored area, with the average distance and time traveled being reduced. Moreover, the operated proposed method on an author-built 2D robot exploring the entire natural environment confirms that the method is effective and applicable in real-world scenarios.

翻译：自主探索是机器人领域的一项新兴技术，其目标在于帮助机器人无需人类控制即可自主定位、扫描地图并导航至任何地形，因此得到广泛应用。迄今为止，基于采样的探索策略对配备能生成三维点云的深度传感器的空中与地面车辆最为有效。这些方法利用采样任务选择随机点，或基于快速探索随机树（RRT）生成样本，随后根据有用体积信息判断前沿区域或下一最佳视点（NBV）。然而，现有最先进的基于采样方法在二维机器人中实施困难，因缺乏环境认知，导致评估随机目的地时的体积增益不佳。本研究提出一种增强的基于采样解决方案，用于室内机器人探索，以在二维环境中确定下一最佳视点（NBV）。我们的方法不断生成RRT直至其端点成为前沿区域，并利用增强效用函数对这些端点进行评估。采用非均匀分布估计从环境中获得的体积信息，以确定被占据及具有不确定概率的单元格。与基于采样的前沿检测和后退时域NBV方法相比，本方法在Gazebo平台模拟环境中表现更优，实现了显著更大的探索区域，且平均行进距离与时间均有减少。此外，将所提方法应用于作者自建的二维机器人，使其探索整个自然环境，证实该方法在真实场景中有效且适用。