|
|
|
Bio-inspired environmental adaptability of swarm active matter |
| Yangkai Jin(金阳凯)1,†, Gao Wang(王高)2,3,†, Daming Yuan(袁大明)1, Peilong Wang(王培龙)1, Jing Wang(王璟)2,3, Huaicheng Chen(陈怀城)2, Liyu Liu(刘雳宇)2,4,‡, and Xingjie Zan(昝兴杰)1,2,§ |
1. School of Ophthalmology and Optometry, Eye Hospital, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325035, China;
2. Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325011, China;
3. School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
4. Chongqing Key Laboratory of Soft Condensed Matter Physics and Smart Materials, College of Physics, Chongqing University, Chongqing 401331, China |
|
|
|
|
Abstract How biologically active matters survive adaptively in complex and changeable environments is a common concern of scientists. Genetics, evolution and natural selection are vital factors in the process of biological evolution and are also the key to survival in harsh environments. However, it is challenging to intuitively and accurately reproduce such long-term adaptive survival processes in the laboratory. Although simulation experiments are intuitive and efficient, they lack fidelity. Therefore, we propose to use swarm robots to study the adaptive process of active matter swarms in complex and changeable environments. Based on a self-built virtual environmental platform and a robot swarm that can interact with the environment, we introduce the concept of genes into the robot system, giving each robot unique digital genes, and design robot breeding methods and rules for gene mutations. Our previous work [Proc. Natl. Acad. Sci. USA 119 e2120019119 (2022)] has demonstrated the effectiveness of this system. In this work, by analyzing the relationship between the genetic traits of the population and the characteristics of environmental resources, and comparing different experimental conditions, we verified in both robot experiments and corresponding simulation experiments that agents with genetic inheritance can survive for a long time under the action of natural selection in periodically changing environments. We also confirmed that in the robot system, both breeding and mutation are essential factors. These findings can help answer the practical scientific question of how individuals and swarms can successfully adapt to complex, dynamic, and unpredictable actual environments.
|
Received: 20 March 2023
Revised: 19 April 2023
Accepted manuscript online: 18 May 2023
|
|
PACS:
|
87.23.-n
|
(Ecology and evolution)
|
| |
87.23.Kg
|
(Dynamics of evolution)
|
| |
87.85.St
|
(Robotics)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant No.12174041), China Postdoctoral Science Foundation (Grant No.2022M723118), and the seed grants from the Wenzhou Institute, University of Chinese Academy of Sciences (Grant No.WIUCASQD2021002). We thank professor Robert H. Austin, Trung V. Phan and Shengkai Li for the early work support. |
Corresponding Authors:
Liyu Liu, Xingjie Zan
E-mail: liu@iphy.ac.cn;zanxj@ucas.ac.cn
|
Cite this article:
Yangkai Jin(金阳凯), Gao Wang(王高), Daming Yuan(袁大明), Peilong Wang(王培龙), Jing Wang(王璟), Huaicheng Chen(陈怀城), Liyu Liu(刘雳宇), and Xingjie Zan(昝兴杰) Bio-inspired environmental adaptability of swarm active matter 2023 Chin. Phys. B 32 088703
|
[1] Wang G, Phan T V, Li S, Wang J, Peng Y, Chen G, Qu J, Goldman D I, Levin S A, Pienta K, Amend S, Austin R H and Liu L 2022 Proc. Natl. Acad. Sci. USA 119 e2120019119
[34] Adami C, Schossau J and Hintze A 2016 Phys. Life Rev. 19 1
[35] Phan T V, Wang G, Do T K, Kevrekidis I G, Amend S, Hammarlund E, Pienta K, Brown J, Liu L and Austin R H 2021 J. Biol. Phys. 47 387
[36] Fitzgerald D M, Hastings P J and Rosenberg S M 2017 Annu. Rev. Cancer Biol. 1 119
[37] Duarte M, Costa V, Gomes J, Rodrigues T, Silva F, Oliveira S M and Christensen A L 2016 Plos One 11 e0151834 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
