Automated Coordination Strategy Design Using Genetic Programming for Dynamic Multipoint Dynamic Aggregation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Guanqiang_Gao:Cybernetics,
-
author = "Guanqiang Gao and Yi Mei and Bin Xin and
Ya-Hui Jia and Will N. Browne",
-
title = "Automated Coordination Strategy Design Using Genetic
Programming for Dynamic Multipoint Dynamic
Aggregation",
-
journal = "IEEE Transactions on Cybernetics",
-
year = "2022",
-
volume = "52",
-
number = "12",
-
pages = "13521--13535",
-
keywords = "genetic algorithms, genetic programming",
-
ISSN = "2168-2275",
-
DOI = "doi:10.1109/TCYB.2021.3080044",
-
abstract = "The multipoint dynamic aggregation (MPDA) problem of
the multirobot system is of great significance for its
real-world applications such as bush fire elimination.
The problem is to design the optimal plan for a set of
heterogeneous robots to complete some geographically
distributed tasks collaboratively. In this article, we
consider the dynamic version of the problem, where new
tasks keep appearing after the robots are dispatched
from the depot. The dynamic MPDA problem is a
complicated optimization problem due to several
characteristics, such as the collaboration of robots,
the accumulative task demand, the relationships among
robots and tasks, and the unpredictable task arrivals.
In this article, a new model of the problem considering
these characteristics is proposed. To solve the
problem, we develop a new genetic programming
hyperheuristic (GPHH) method to evolve reactive
coordination strategies (RCSs), which can guide the
robots to make decisions in real time. The proposed
GPHH method contains a newly designed effective RCS
heuristic template to generate the execution plan for
the robots according to a GP tree. A new terminal set
of features related to both robots and tasks and a
cluster filter that assigns the robots to urgent tasks
are designed. The experimental results show that the
proposed GPHH significantly outperformed the
state-of-the-art methods. Through further analysis,
useful insights such as how to distribute and
coordinate robots to execute different types of tasks
are discovered.",
-
notes = "Also known as \cite{9445736}",
- }
Genetic Programming entries for
Guanqiang Gao
Yi Mei
Bin Xin
Ya-Hui Jia
Will N Browne
Citations