Morphogenetic Multi-Robot Pattern Formation Using ... - FoCAS

FOCAS workshop, 2nd September 2013, Taormina, Italy

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

*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 selforganisation 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.

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

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

Gene Regulatory Networks (GRNs) 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

Gene Regulatory Network activator activator

g1 Gene 1

Negative

repressor feedback

g2 Gene 2

Positive feedback activator

g3 Gene 3

A gene regulatory network with three genes

Transcriptional regulatory network controlling metabolism in E. coli bacteria

Multi-Cellular Interactions Cell 1 Cell 2 The genes create GRNs that exhibit complex dynamic behavior to control development

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-

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

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+

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Cell-cell communication is achieved by diffusive coupling

Gene

Morphogenetic Swarm Robots

Cell-Robot Metaphor 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

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 HGRN 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 [unknown/known target velocity] • 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

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

Conclusions

• 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

Future Research Direction

• 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

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 4. Jasmine

wheel 15* none– commercially ambient light 3 1.5 Kilobot available / N/A & inexpensive system for testingdistance, collaborative behaviour in a bearing, wheel 90* charge 3 1-2 color / N/A very large (> 100)light swarm of robots

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. SwarmBot (MIT)

N/A

charge, power, program

distance, bearing, camera, bump

wheel / 50 cm/s

12.7

3

8. SwarmBot (EPFL)

N/A

none

distance, bearing, accel/gyro, camera

treel / N/A

17

4-7

*part cost only / **commercially available

1

3

2

4

5

6

7

8

Thanks for your attention. Any question?

Swarm Robot Testbed Kilobot Specifications • 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

Swarm Robot Testbed Kilobot Scalability • 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 *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