Co-evolution framework of swarm self-assembly robotsby Haiyuan Li, Hongxing Wei, Jiangyang Xiao, Tianmiao Wang

Neurocomputing

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Year
2015
DOI
10.1016/j.neucom.2012.10.047
Subject
Artificial Intelligence / Computer Science Applications / Cognitive Neuroscience

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Co-evolution framework of swarm self-assembly robots

Haiyuan Li a, Hongxing Wei a,n, Jiangyang Xiao b, Tianmiao Wang a a School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China b Institute of Unmanned Aircraft Design, Beihang University, Beijing 100191, China a r t i c l e i n f o

Article history:

Received 7 April 2012

Received in revised form 12 October 2012

Accepted 15 October 2012

Available online 31 July 2014

Keywords:

Co-evolution

Swarm robot

Genetic programming a b s t r a c t

In this paper, we present a co-evolution framework of configuration and control for swarm self-assembly robots, Sambots, in changing environments. The framework can generate different patterns composed of a set of Sambot robots to adapt to the uncertainties in complex environments. Sambot robots are able to autonomously aggregate and disaggregate into a multi-robot organism. To obtain the optimal pattern for the organism, the configuration and control of locomoting co-evolve by means of genetic programming.

To finish self-adaptive tasks, we imply a unified locomotion control model based on Central Pattern

Generators (CPGs). In addition, taking modular assembly modes into consideration, a mixed genotype is used, which encodes the configuration and control. Specialized genetic operators are designed to maintain the evolution in the simulation environment. By using an orderly method of evaluation, we can select some resulting patterns of better performance. Simulation experiments demonstrate that the proposed system is effective and robust in simultaneously constructing the adaptive structure and locomotion pattern. The algorithmic research and application analysis bring about deeper insight into swarm intelligence and evolutionary robotics. & 2014 Elsevier B.V. All rights reserved. 1. Introduction

In nature, biological systems consisting of vast numbers of simple agents can attain functionally rich collective behavior and show impressive collective problem-solving capabilities [1], e.g., ant colonies, schools of fish, and multicellular organisms. The agents of nature allow cooperative and competitive working in large-scale societies. There exist two amazing collective phenomena [2]. First, the swarm agents can work collectively to profit from swarm capacity and intelligence, e.g., collective actuation, foraging, and exploration. Second, the swarm agents can aggregate into a multicellular organism that cannot be fulfilled by a single one or, in some cases, the swarms do not work collectively, e.g., in reaching target areas separated from the swarms by an object.

Such biological phenomena have inspired the development of the robots, especially modular robots to improve adaptation.

We first present the swarm self-assembly robot, called Sambot, which has synthesized the strength of self-reconfigurable robots and self-assembly robots. Each Sambot is a completely autonomous mobile robot, similar to the individual robot in the swarm robotics. Multiple Sambots can form a robotic structure through self-assembly. A unified representation method is proposed to express the configuration. To derive the generic control model, we introduce the Central Pattern Generators (CPGs) by combining the previous unified representation method. Building upon the configuration and control model, a co-evolution framework is proposed to design the organism and the corresponding locomotion pattern that is composed of a swarm of robots. The algorithmic contributions of this work leads to a generic framework that coevolve the configuration and control to allow the robot swarms forming the diverse patterns. In addition, we also present empirical results from implementing this algorithmic framework on a simulation experiment on co-evolution of the organism and locomotion control. Our proposed co-evolution framework is closely related to the evolutionary algorithm in modular robots [2–4]. Our work differs from other designs in the following two ways: (1) In the algorithmic aspect, we propose a generalized configuration and control model to give a unified genotype representation of all of the organisms even as the special operators are generating evolution. (2) In the aspect of application, the whole framework is established on existing swarm self-assembly systems. The systemic modules can autonomously aggregate and disaggregate to achieve a variety of organisms, which give the robot the potential to solve such issues of evolutionary organisms as configuration pattern, assembly, control, and encoding.

The rest of the paper is organized as follows: we begin with a related work review in Section 2. Section 3 introduces the Sambot simulation platform based on a realistic module, followed in

Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/neucom

Neurocomputing http://dx.doi.org/10.1016/j.neucom.2012.10.047 0925-2312/& 2014 Elsevier B.V. All rights reserved. n Corresponding author.

E-mail address: weihongxing@buaa.edu.cn (H. Wei).

Neurocomputing 148 (2015) 112–121

Section 4 by a representation of the symbiotic organism and a unity locomotion control model; Section 5 describes the co-evolutionary framework for achieving an adaptive combination of configuration and control to carry out an intended task; Section 6 demonstrates the simulation of the organism's adaptation; and finally, Section 7 concludes the paper. 2. Related work

The co-evolution of morphology and controllers has proved to be successful for performing particular tasks [5,6]. A genetic language using a directed graph of nodes and connections can define the nature of the creature. Virtual creatures are then expressed in a virtual environment to achieve fitness. These interesting virtual creatures interact with the external environment and then the best is selected. It is noted that in dynamic evolution a complex genotype-fitness relationship is implemented, and in this relationship genotype encoding plays a significant role. As for the evolved organism, encoding the phenotype consisting of unknown structures and adaptive controls is a complex process. Komosinsky et al. compared three encodings differing in several characteristics of the process of co-evolving morphology and the control of virtual stick creatures [7]. The fitness results demonstrated the properties of each encoding and the advantage of the evolved creature beyond that of the individual. Hornby and