Frontier Based Exploration for Autonomous Robot

TL;DR

This paper presents Wavefront Frontier Detector (WFD), an autonomous frontier-based exploration algorithm implemented on Gazebo and Kobuki TurtleBot, capable of efficiently exploring both large and cluttered environments and validated against existing ROS mapping methods.

Contribution

Introduction of the Wavefront Frontier Detector (WFD), a novel autonomous exploration strategy for robots that improves exploration efficiency in various environments.

Findings

WFD effectively explores large open and cluttered spaces.

The generated maps are validated and comparable to existing ROS mapping methods.

Implementation on both simulation and hardware demonstrates practical applicability.

Abstract

Exploration is process of selecting target points that yield the biggest contribution to a specific gain function at an initially unknown environment. Frontier-based exploration is the most common approach to exploration, wherein frontiers are regions on the boundary between open space and unexplored space. By moving to a new frontier, we can keep building the map of the environment, until there are no new frontiers left to detect. In this paper, an autonomous frontier-based exploration strategy, namely Wavefront Frontier Detector (WFD) is described and implemented on Gazebo Simulation Environment as well as on hardware platform, i.e. Kobuki TurtleBot using Robot Operating System (ROS). The advantage of this algorithm is that the robot can explore large open spaces as well as small cluttered spaces. Further, the map generated from this technique is compared and validated with the map generated using turtlebot_teleop ROS Package.