Hybrid Frontier Detection Strategy for Autonomous Exploration in Multi-obstacles Environment

Abstract

Rapidly-exploring Random Tree (RRT) algorithm is widely used in path planning, while the RRT is inefficient for robotic exploration in large-scale environments with multi-obstacles and narrow entrances. Here, we propose a Hybrid Frontier Detection (HFD) strategy for autonomous exploration which incorporates a variable step-size random tree global frontier detector, a multi-root nodes random tree frontier detector, and a grid-based frontier detector algorithm. The proposed strategy enables a robot to quickly search for the frontier in real-time. Compared with the traditional RRT-based strategy, the exploration time and traveling length of the proposed HFD strategy are respectively decreased by over 15% and 12% in the simulation environment and decreased by over 14% and 11% under the same experimental conditions in the experimental environment. The results indicate that the HFD strategy effectively solves the problem of autonomous exploration in the environment with multi-obstacles and narrow entrances.