Automated pattern generation for swarm robots using constrained multi-objective genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{FAN:2023:swevo,
-
author = "Zhun Fan and Zhaojun Wang and Wenji Li and
Xiaomin Zhu and Bingliang Hu and An-Min Zou and Weidong Bao and
Minqiang Gu and Zhifeng Hao and Yaochu Jin",
-
title = "Automated pattern generation for swarm robots using
constrained multi-objective genetic programming",
-
journal = "Swarm and Evolutionary Computation",
-
volume = "81",
-
pages = "101337",
-
year = "2023",
-
ISSN = "2210-6502",
-
DOI = "doi:10.1016/j.swevo.2023.101337",
-
URL = "https://www.sciencedirect.com/science/article/pii/S2210650223001104",
-
keywords = "genetic algorithms, genetic programming, Gene
regulatory network (GRN), Entrapping pattern
generation, Self-organization, Constrained
multi-objective genetic programming (CMOGP)",
-
abstract = "Swarm robotic systems (SRSs), which are widely used in
many fields, such as search and rescue, usually
comprise a number of robots with relatively simple
mechanisms collaborating to accomplish complex tasks. A
challenging task for SRSs is to design local
interaction rules for self-organization of robots that
can generate adaptive patterns to entrap moving
targets. Biologically inspired approaches such as gene
regulatory network (GRN) models provide a promising
solution to this problem. However, the design of GRN
models for generating entrapping patterns relies on the
expertise of designers. As a result, the design of the
GRN models is often a laborious and tedious
trial-and-error process. In this study, we propose a
modular design automation framework for GRN models that
can generate entrapping patterns. The framework employs
basic network motifs to construct GRN models
automatically without requiring expertise. To this end,
a constrained multi-objective genetic programming is
used to simultaneously optimize the structures and
parameters of the GRN models. A multi-criteria
decision-making approach is adopted to choose the
preferred GRN model for generating the entrapping
pattern. Comprehensive simulation results demonstrate
that the proposed framework can obtain novel GRN models
with simpler structures than those designed by human
experts yet better performance in complex and dynamic
environments. Proof-of-concept experiments using e-puck
robots confirmed the feasibility and effectiveness of
the proposed GRN models",
- }
Genetic Programming entries for
Zhun Fan
Zhaojun Wang
Wenji Li
Xiaomin Zhu
Bingliang Hu
An-Min Zou
Weidong Bao
Minqiang Gu
Zhifeng Hao
Yaochu Jin
Citations