Original Research ARTICLE
A Linear Objective Function Based Heuristic for Robotic Exploration of Unknown Polygonal Environments
- 1Mechanical Aerospace & Biomedical Engineering, University of Tennessee, Knoxville, United States
This work presents a heuristic for describing the next best view location for an autonomous agent exploring an unknown environment. The approach considers each robot as a point mass with omnidirectional and unrestricted vision of the environment and line-of-sight communication operating in a polygonal environment which may contain holes. The number of robots in the team is always sufficient for full visual coverage of the space. The technique employed falls in the category of distributed visibility based deployment algorithms which seek to segment the space based on each agent's field of view with the goal of deploying each agent into the environment to create a visually connected series of agents which fully observe the previously unknown region. The contributions made to this field are a technique for utilizing linear programming methods to determine the solution to the next best observation (NBO) problem as well as a method for calculating multiple NBO points simultaneously. Both contributions are incorporated into an algorithm and deployed in a simulated environment built with MATLAB for testing. The algorithm successfully deployed agents into polygons which may contain holes. The efficiency of the deployment method was compared with random deployment methods to establish a performance metric for the proposed tactic. It was shown that the heuristic presented in this work performs better the other tested strategies.
Keywords: Multi-robot exploration, visibility based deployment, Art gallery problem, path planning, next best view location
Received: 11 Sep 2017;
Accepted: 14 Feb 2018.
Edited by:Kushal Mukherjee, United Technologies Research Center, Ireland
Reviewed by:Murat Haciomeroglu, Gazi University, Turkey
Matthew J. Bays, Naval Surface Warfare Center, Panama City Division, United States
Copyright: © 2018 Graves and Chakraborty. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Subhadeep Chakraborty, University of Tennessee, Knoxville, Mechanical Aerospace & Biomedical Engineering, DO 208, 1512 Middle Drive, Knoxville, 37996, TN, United States, email@example.com