morphogenetic multi-robot pattern formation using hierarchical gene regulatory networks

Professor Yaochu Jin and Dr. Hyondong Oh*

Nature Inspired Computing and Engineering (NICE) Group

Department of Computing, University of Surrey, UK

Morphogenetic Multi-Robot Pattern Formation

Using Hierarchical Gene Regulatory Networks

FOCAS workshop, 2nd September 2013, Taormina, Italy

*EC FP7 project: Genetically-programmable self-patterning swarm-organs (Swarm-Organ)

Outline

• Introduction

• Biological Background

• Adaptive Pattern Formation using H-GRN Model

• Future Research Direction

Introduction

• Multi-robot systems (MRSs) are to collectively accomplish complex tasks that are beyond the capability of any single robot

in the presence of uncertainties or with incomplete information

where a distributed control or asynchronous computation is required

flexible, robust, and adaptive

Search and rescue, cooperative transportation, mapping, and monitoring

• Morphogenetic robotics is a new emerging field of robotics for self-organisation of swarm or modular robots

which employs genetic and cellular mechanisms, inspired from

Biological morphogenesis and gene regulatory networks (GRNs)

• Morphogenetic pattern formation which can be highly adaptable to unknown environmental changes

Biological Background

Biological Morphogenesis

• Morphogenesis is a biological process in which cells divide and differentiate, and finally resulting in the mature morphology of a biological organism.

• Morphogenesis is under the governance of a developmental gene regulatory network (GRN) and the influence of the environment represented as morphogen gradients.

• Morphogen gradients are either directly present in the environment of fertilised cell or generated by a few cells known as organisers.

Movements of epidermal cells (green) and neurons (red) during epidermal enclosure of C. elegans

Frames from digital 4D movie of C. elegans embryo development.

Gene Regulatory Networks (GRNs)

Gene 1

Gene 2 Gene 3

activator

activator

repressor

activator

g1

g2 g3

Negative

feedback

Positive

feedback

Gene Regulatory Network

A gene regulatory network with three genes Transcriptional regulatory network controlling metabolism in E. coli bacteria

A gene regulatory network is a collection of DNA segments that interact with other chemicals in its own cell or other cells, thereby governing the expression rate at which the genes are transcribed into mRNA and proteins

Cell 1

Cell 2

Gene

Cell-cell communication is achieved by diffusive coupling

+ -

-

+

+

+

-

-

The genes create GRNs that exhibit complex dynamic behavior to control development

Gene codes for cell actions: divide, die, communicate, change cell-type

Multi-Cellular Interactions

Morphogenetic Swarm Robots

Multi-Cellular System Multi-Robot Systems

Concentration of gene G1 x-position

Concentration of gene G2 y-position

Concentration of gene P1 Internal state in x-coordinate

Concentration of gene P2 Internal state in y-coordinate

Cell-cell interactions through TF diffusion

Robot-robot local interaction

Morphogen gradient Target pattern to be formed

Cell-Robot Metaphor

I. Adaptive Pattern Formation Using a

Hierarchical GRN

• Biological organisers imply a temporal / spatial hierarchy in gene expression

– For morphogenetic robotics, hierarchy facilitates local adaptation

– Improvement of robustness and evolvability

• Two-layer H-GRN structure for target entrapping pattern formation

– Layer 1: pattern generation

– Layer 2: Robot guidance

• GRN model parameters are evolved using a multi-objective evolutionary algorithm

Layer 1: Pattern Generation

Layer 2: Robot Guidance

Preliminary Experimental Results

II. Adaptive Pattern Formation Using H-

GRN with Region-based Shape Control

• Predefined Simple Shape

– Desired region as a ring and obstacle avoidance

– Single moving target tracking

Movement (pos. & vel.) of a target is assumed

to be known or can be estimated

• Complex Entrapping Shape from Layer 1

– Stationary target with neighbourhood size adaptation

Adjusted by sensing (max) and bumper range (min)

– Tracking of multiple moving targets

[unknown/known target velocity]

III. Adaptive Pattern Formation Using

H-GRN with Evolving Network Motifs

• Evolving layer with network motifs

– Utilise basic building blocks for gene regulation: positive, negative, OR, AND, XOR, etc.

– Evolving GRN structures with evolutionary optimisation to find the GRN model which entraps multiple targets efficiently

Future Research Direction

• Morphogenetic approach to self-organised adaptive multi-robot pattern formation using a hierarchical GRN (H-GRN)

• Highly adaptable to environmental changes resulting from unknown target movements

• Applications: contaminant/hazardous material boundary monitoring or isolation and transport/herding target objects to a goal position

Conclusions

• More biologically –inspired approaches to swarm robotics

• Realistic distributed system considering a swarm of robots’ sensing / communication / computation capability

• Implementation with swarm robot testbed

– Kilobot: a low cost scalable robot designed for collective behaviours

Future Research Direction

Swarm Robot Testbed

Comparison of Small Collective Robot Systems

Robot Cost

(GBP)

Scalable

operation Sensing

Locomotion

/ speed

Body

size (cm)

Battery

(hours)

1. Alice 30* none distance wheel

/ 4 cm/s 2 3.5-10

2. Kilobot** 80

(10*)

charge,

power, program

distance,

ambient light

vibration

/ 1 cm/s 3 3-24

3. Formica 15* none ambient light wheel

/ N/A 3 1.5

4. Jasmine 90* charge distance, bearing,

light color

wheel

/ N/A 3 1-2

5. E-puck** 600 none camera,

distance, bearing

wheel

/ 13 cm/s 7.5 1-10

6. R-One 150* none light, accel/gyro, IR

sensors, encoders

wheel

/ 30 cm/s 10 6

7. Swarm-

Bot (MIT) N/A

charge,

power, program

distance, bearing,

camera, bump

wheel

/ 50 cm/s 12.7 3

8. Swarm-

Bot (EPFL) N/A none

distance, bearing,

accel/gyro, camera

treel

/ N/A 17 4-7

*part cost only / **commercially available

1

5

2

6

7

4 3

Kilobot – commercially available & inexpensive

system for testing collaborative behaviour in a

very large (> 100) swarm of robots

8

Thanks for your attention.

Any question?

Swarm Robot Testbed

• Locomotion

– 2 vibration motors (255 power levels)

– 1 cm/s & 45 deg/s

• Communication & Sensing

– Infrared light transmitter/receiver

3 bytes up to 7 cm away

Distance by signal strength

– Ambient light sensor

• Controller

– Atmega 328 Microprocessor

– C language with WinAVR compiler

Kilobot Specifications

• Controller board

– Send a new program to all Kilobots at once

– Control the Kilobots (pausing or power on/off)

– One-meter diameter area

• Kilobot charger

– Charge ten Kilobots at one time

• Applications

– Foraging, leader following, transport, and etc.

– Need to be fairly simple due to limited capabilities

Swarm Robot Testbed

Kilobot Scalability

*References: http://www.k-team.com/mobile-robotics-products/kilobot

http://www.eecs.harvard.edu/ssr/projects/progSA/kilobot.html

M. Rubenstein et al., Kilobot: A Low Cost Scalable Robot System for Collective Behaviors, IEEE ICRA, USA, 2012

M. Rubenstein et al., Collective Transport of Complex Objects by Simple Robots: Theory and Experiments, AA-MAS, USA, 2013