A sampling-based multi-tree fusion algorithm for frontier detection

Wenchuan Qiao,Zheng Fang,Bailu Si

doi:10.1177/1729881419865427

Abstract

Autonomous exploration is a key step toward real robotic autonomy. Among various approaches for autonomous exploration, frontier-based methods are most commonly used. One efficient method of frontier detection exploits the idea of the rapidly-exploring random tree and uses tree edges to search for frontiers. However, this method usually needs to consume a lot of memory resources and searches for frontiers slowly in the environments where random trees are not easy to grow (unfavorable environments). In this article, a sampling-based multi-tree fusion algorithm for frontier detection is proposed. Firstly, the random tree’s growing and storage rules are changed so that the disadvantage of its slow growing under unfavorable environments is overcome. Secondly, a block structure is proposed to judge whether tree nodes in a block play a decisive role in frontier detection, so that a large number of redundant tree nodes can be deleted. Finally, two random trees with different growing rules are fused to speed up frontier detection. Experimental results in both simulated and real environments demonstrate that our algorithm for frontier detection consumes fewer memory resources and shows better performances in unfavorable environments.

Highlights

With the continuous development of robotic technologies, robots, especially autonomous mobile robots, have been integrated into more and more fields of human society, which puts forward higher requirements for robotic autonomy
Autonomous exploration is a key step toward real robotic autonomy
In order to verify performances of our sampling-based multi-tree fusion algorithm (SMF) algorithm for frontier detection, we compare it with the frontier detection algorithm based on the classical rapidly-exploring random tree (RRT) (RFD).[10]

Summary

Introduction

With the continuous development of robotic technologies, robots, especially autonomous mobile robots, have been integrated into more and more fields of human society, which puts forward higher requirements for robotic autonomy. Researchers proposed exploration methods based on the rapidly-exploring random tree (RRT).[7] The RRT refers to sampling randomly in a certain area and forming tree edges and nodes through sampled points and their nearest valid tree nodes By doing this repeatedly, it can form an integrated structure which is similar to a tree. We propose a block structure to judge whether tree nodes in a block play a decisive role in frontier detection, so as to delete a large number of redundant tree nodes generated during exploration. We validate performances of our SMF in both simulated and real environments in the fourth section and fifth section concludes the article

Related works

Experiments and results

Experiments in simulation environments

Experiments in the real environment

Results analysis

Declaration of conflicting interests