Citation: Yu, T.; Deng, B.; Gui, J.;
Zhu, X.; Yao, W. Efficient Informative
Path Planning via Normalized Utility
in Unknown Environments
Exploration. Sensors 2022, 22, 8429.
https://doi.org/10.3390/s22218429
Academic Editor: Andrzej Stateczny
Received: 13 October 2022
Accepted: 29 October 2022
Published: 2 November 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Efficient Informative Path Planning via Normalized Utility in
Unknown Environments Exploration
Tianyou Yu
†,‡
, Baosong Deng
‡
, Jianjun Gui *, Xiaozhou Zhu and Wen Yao
Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China
* Correspondence: jianjungui@nudt.edu.cn
† Current address: Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China.
‡ These authors contributed equally to this work.
Abstract:
Exploration is an important aspect of autonomous robotics, whether it is for target searching,
rescue missions, or reconnaissance in an unknown environment. In this paper, we propose a solution
to efficiently explore the unknown environment by unmanned aerial vehicles (UAV). Innovatively,
a topological road map is incrementally built based on Rapidly-exploring Random Tree (RRT) and
maintained along with the whole exploration process. The topological structure can provide a set
of waypoints for searching an optimal informative path. To evaluate the path, we consider the
information measurement based on prior map uncertainty and the distance cost of the path, and
formulate a normalized utility to describe information-richness along the path. The informative path
is determined in every period by a local planner, and the robot executes the planned path to collect
measurements of the unknown environment and restructure a map. The proposed framework and
its composed modules are verified in two 3-D environments, which exhibit better performance in
improving the exploration efficiency than other methods.
Keywords: informative path planning; exploration; autonomous robot; navigation
1. Introduction
Recently, autonomous robots have begun to be used to replace human work [
1
–
4
], even
in harsh environments, such as battlefields, caves, and extraterrestrial environments [
5
,
6
].
In such scenarios, communication is infrequent or limited, manual operation is difficult for
persistently collecting environmental data. A robot’s perception of the unknown environ-
ment and independent planning ability in such scenarios is particularly important [7].
The process of robot autonomous movement and environment map building is called
unknown environment exploration [
8
]. Using a Micro Aerial Vehicle (MAV) to explore
in an unstructured environment is common research. Due to its high degree of motion
flexibility, it is able to complete the motion track with high maneuver requirements [
9
].
A MAV equipped with computing units, vision, and positioning sensors can collect the
information measurements to perceive and map the environment in real-time. MAV moves
independently without prior information on the global environment but the real-time map
is based on records from an airborne sensor.
If the environment is completely unknown in advance, it is difficult to formulate
a globally optimal solution to control the MAV by a series of inputs at one time. The
most common method is receding horizon control [
10
–
12
], which iteratively determines
a control input to navigate the robot to scan unknown space. For the navigation that
leads to information measurements, a seminal method is detecting the frontier, which is
identified as the boundary between the known and unknown regions of the map [
13
,
14
].
Even if a robot greedily seeks the frontiers, it indicates a series of feasible exploring actions.
However, it is so simple that it lacks a comprehensive evaluation of the candidate region.
The robot works without considering the information gathered, some decisions that execute
Sensors 2022, 22, 8429. https://doi.org/10.3390/s22218429 https://www.mdpi.com/journal/sensors
