  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.
