An Enhanced Sampling-Based Method with Modified Next-Best View Strategy For 2D Autonomous Robot Exploration

Abstract

The sampling-based exploration strategy is the most effective for Unmanned Aerial Vehicles, Micro Aerial Vehicles, and other three-dimensional outdoor robots. Its objective is to send robots to cover new unexplored areas by planning an optimal destination and path using an optimal utility function. Sampling-based Frontier Detection and Next Best View theories are the most powerful among the existing strategies for autonomous exploring and mapping techniques. This study proposes an improved sampling-based method for indoor robot exploration. The base algorithm's sampling task is adjusted to generate samples until the Rapidly-exploring Random Trees (RRTs) endpoints become frontiers. These samples are then evaluated using the enhanced utility function. The information obtained from the environments is estimated using occupied and uncertain probability. The initial results indicate that our modified NBV approach achieves a significantly larger explored area while reducing distance and time on Gazebo platform-simulated environments. These findings show our proposed approach's promising autonomous exploration potential in 2D environments.