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 Morphogenetic Multi-Robot Pattern Formation Using Hierarchical Gene Regulatory Networks
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Morphogenetic Multi-Robot Pattern Formation Using Hierarchical Gene Regulatory Networks

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Yaochu Jin and Hyondong Oh: University of Surrey …

Yaochu Jin and Hyondong Oh: University of Surrey

Presentation from ECAL 2013

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  • 1. 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)
  • 2. Outline • Introduction • Biological Background • Adaptive Pattern Formation using H-GRN Model • Future Research Direction
  • 3. 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
  • 4. Biological Background
  • 5. 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
  • 6. 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
  • 7. Multi-Cellular Interactions Cell 1 Cell 2 The genes create GRNs that exhibit complex dynamic behavior to control development + - + - Gene codes for cell actions: divide, die, communicate, change cell-type + + - Cell-cell communication is achieved by diffusive coupling Gene
  • 8. Morphogenetic Swarm Robots
  • 9. 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
  • 10. 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
  • 11. Layer 1: Pattern Generation
  • 12. Layer 2: Robot Guidance
  • 13. Preliminary Experimental Results
  • 14. 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
  • 15. 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
  • 16. Future Research Direction
  • 17. 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
  • 18. 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
  • 19. 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 80 (10*) charge, power, program distance, ambient light vibration / 1 cm/s 3 2 3.5-10 2. Kilobot** 1 3-24 3. Formica 4. Jasmine wheel 15* none ambient light 3 1.5 Kilobot – commercially available & inexpensive / N/A system for testing collaborative behaviour in a distance, bearing, wheel 90* charge 3 1-2 / N/A very large (> 100)light color of robots swarm 3 4 5 5. E-puck** 600 none camera, distance, bearing wheel / 13 cm/s 7.5 6. R-One 150* none light, accel/gyro, IR sensors, encoders wheel / 30 cm/s 10 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 7 8 6 7. SwarmBot (MIT) 6 1-10 *part cost only / **commercially available
  • 20. Thanks for your attention. Any question?
  • 21. 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
  • 22. 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

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