KOVAN Dept. of Computer Eng. Middle East Technical University Ankara, Turkey

Similar documents
SWARM INTELLIGENCE. Mario Pavone Department of Mathematics & Computer Science University of Catania

Design of Adaptive Collective Foraging in Swarm Robotic Systems

SWARM ROBOTICS: PART 2. Dr. Andrew Vardy COMP 4766 / 6912 Department of Computer Science Memorial University of Newfoundland St.

SWARM ROBOTICS: PART 2

Swarm Robotics. Lecturer: Roderich Gross

An Introduction to Swarm Intelligence Issues

1) Complexity, Emergence & CA (sb) 2) Fractals and L-systems (sb) 3) Multi-agent systems (vg) 4) Swarm intelligence (vg) 5) Artificial evolution (vg)

Swarm Intelligence. Corey Fehr Merle Good Shawn Keown Gordon Fedoriw

SWARM-BOT: A Swarm of Autonomous Mobile Robots with Self-Assembling Capabilities

Collective Robotics. Marcin Pilat

Biological Inspirations for Distributed Robotics. Dr. Daisy Tang

biologically-inspired computing lecture 20 Informatics luis rocha 2015 biologically Inspired computing INDIANA UNIVERSITY

CS594, Section 30682:

CS 599: Distributed Intelligence in Robotics

Contact information. Tony White, Associate Professor

Sorting in Swarm Robots Using Communication-Based Cluster Size Estimation

INFORMATION AND COMMUNICATION TECHNOLOGIES IMPROVING EFFICIENCIES WAYFINDING SWARM CREATURES EXPLORING THE 3D DYNAMIC VIRTUAL WORLDS

AIS and Swarm Intelligence : Immune-inspired Swarm Robotics

Biologically-inspired Autonomic Wireless Sensor Networks. Haoliang Wang 12/07/2015

PSYCO 457 Week 9: Collective Intelligence and Embodiment

Self-Organised Task Allocation in a Group of Robots

Structure and Synthesis of Robot Motion

Robotic Systems ECE 401RB Fall 2007

Distributed Robotics From Science to Systems

Aggregation Behaviour as a Source of Collective Decision in a Group of Cockroach-like Robots

Towards Artificial ATRON Animals: Scalable Anatomy for Self-Reconfigurable Robots

Re-embodiment of Honeybee Aggregation Behavior in an Artificial Micro-Robotic System

Adaptive Control in Swarm Robotic Systems

Multi-Feature Collective Decision Making in Robot Swarms

Subsumption Architecture in Swarm Robotics. Cuong Nguyen Viet 16/11/2015

ONE of the many fascinating phenomena

Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks

Paradigms, Models and Technologies for Building and Simulating Self-Organising Systems

Collective Perception in a Robot Swarm

Spatial Computing, Synthetic Biology, and Emerging IP Challenges. Jacob Beal November, 2010

Self-Organized Holonic Manufacturing Systems Combining Adaptation and Performance Optimization

Self-Organizing Networked Systems for Technical Applications: A Discussion on Open Issues

1,024 Kilobot Robots Studying Collective Behaviors & Swarm Intelligence with Little Bitty Robots

Group Transport Along a Robot Chain in a Self-Organised Robot Colony

A Bio-inspired Multi-Robot Coordination Approach

What is Computation? Biological Computation by Melanie Mitchell Computer Science Department, Portland State University and Santa Fe Institute

NAVIGATION OF MOBILE ROBOT USING THE PSO PARTICLE SWARM OPTIMIZATION

Embodiment of Honeybee s Thermotaxis in a Mobile Robot Swarm

In cooperative robotics, the group of robots have the same goals, and thus it is

Evolution 4.0 Ir. Dr. C.J.M. (Chris) Verhoeven

Sector-Search with Rendezvous: Overcoming Communication Limitations in Multirobot Systems

CSCI 445 Laurent Itti. Group Robotics. Introduction to Robotics L. Itti & M. J. Mataric 1

In vivo, in silico, in machina: ants and robots balance memory and communication to collectively exploit information

Programmable self-assembly in a thousandrobot

Mixed-Initiative Remote Characterization Using a Distributed Team of Small Robots

Swarm Intelligence W7: Application of Machine- Learning Techniques to Automatic Control Design and Optimization

Collective Construction Using Lego Robots

Expert Assessment of Stigmergy: A Report for the Department of National Defence

Sequential Task Execution in a Minimalist Distributed Robotic System

K.1 Structure and Function: The natural world includes living and non-living things.

CORC 3303 Exploring Robotics. Why Teams?

Artificial Intelligence. Cameron Jett, William Kentris, Arthur Mo, Juan Roman

ADAPTIVE GROWTH USING ROBOTIC FABRICATION

From Tom Thumb to the Dockers: Some Experiments with Foraging Robots

Hole Avoidance: Experiments in Coordinated Motion on Rough Terrain

Distributed Clustering Method for. Energy-Efficient Data Gathering in

Cognitive robots and emotional intelligence Cloud robotics Ethical, legal and social issues of robotic Construction robots Human activities in many

Chaos, Complexity, and Inference (36-462)

Synthetic Brains: Update

Formica ex Machina: Ant Swarm Foraging from Physical to Virtual and Back Again

Swarm Robotics. Clustering and Sorting

Nature Inspired Systems

Portable Sensor Motes as a Distributed Communication Medium for Large Groups of Mobile Robots

Countering Weapons of Mass Destruction (CWMD) Capability Assessment Event (CAE)

A Robotic Swarm for Spill Finding and Perimeter Formation. David J. Bruemmer Donald D. Dudenhoeffer Mark D. McKay Matthew O.

VI51 Project Subjects

Multi robot Team Formation for Distributed Area Coverage. Raj Dasgupta Computer Science Department University of Nebraska, Omaha

Control and Coordination in a Networked Robotic Platform

Towards an Engineering Science of Robot Foraging

On The Role of the Multi-Level and Multi- Scale Nature of Behaviour and Cognition

Cooperative Decision-Making in Decentralized Multiple-Robot Systems: the Best-of-N Problem

Interactive Surface for Bio-inspired Robotics, Re-examining Foraging Models

1 Swarms A long time ago, people discovered the variety of the interesting insect or animal behaviors in the nature. A ock of birds sweeps across the

Advances in insect brain/behavior simulation using HNN and robotics. Jim Zdunek. Insect Behavior. April 10, 2013

Biologically Inspired Embodied Evolution of Survival

Statement May, 2014 TUCKER BALCH, ASSOCIATE PROFESSOR SCHOOL OF INTERACTIVE COMPUTING, COLLEGE OF COMPUTING GEORGIA INSTITUTE OF TECHNOLOGY

Executive Summary. Chapter 1. Overview of Control

Environmental factors promoting the evolution of recruitment strategies in swarms of foraging robots

The Behavior Evolving Model and Application of Virtual Robots

Computational Synthetic Biology

Important Tools and Perspectives for the Future of AI

Cognitive Systems Monographs

Prof. Habiba Drias Laboratoire de Recherche en Intelligence Artificielle LRIA Computer Science Department USTHB Algiers Algeria

Author copy of chapter 8.7 from Organic Computing Technical Systems for Survival in the Real World

Collective Intelligence in Knowledge Management

Building Blocks for Multi-Robot Construction

2018 Research Campaign Descriptions Additional Information Can Be Found at

Distributed Vision System: A Perceptual Information Infrastructure for Robot Navigation

A Review of Probabilistic Macroscopic Models for Swarm Robotic Systems

Multi-Robot Systems, Part II

Springer. Handbook oƒ. Computational Intelligence. Kacprzyk Pedrycz Editors

OFFensive Swarm-Enabled Tactics (OFFSET)

Bio-inspired Multiagent Systems

Information Quality in Critical Infrastructures. Andrea Bondavalli.

Review of Soft Computing Techniques used in Robotics Application

Transcription:

Swarm Robotics: From sources of inspiration to domains of application Erol Sahin KOVAN Dept. of Computer Eng. Middle East Technical University Ankara, Turkey http://www.kovan.ceng.metu.edu.tr

What is Swarm Robotics? Yet another novel approach to the control of large group of robots! Study of multi-robot coordination strategies inspired from social insects. Engineering self-organization in physically embodied swarms. Application of Swarm Intelligence to the control of a group of robots. 2/29

Attempting to define the term Swarm Robotics? Need to identify aspects that make swarm robotics approach novel and desirable. aspects that distinguishes swarm robotics from other related studies. 3/29

What s novel and desirable in the Swarm Robotics approach? Emphasis on the system-level functioning properties observed in social insect systems: Robustness Flexibility Scalability Essential for deploying large numbers of robots. 4/29

Robustness Social insects can continue to operate despite large disturbances. Redundancy Decentralized coordination Simplicity of the individuals Distributed sensing 5/29

Flexibility Social insects can offer modularized solutions to tasks of different nature by utilizing different coordination mechanisms. 6/29

Flexibility same swarm, different tasks Foraging Prey retrieval Chain formation 7/29

Scalability Social insects are observed to be able to operate under a wide range of group sizes. That is, coordination mechanisms are rather independent of the number of individuals in the group. 8/29

Putting swarm robotics in place Where is Swarm Robotics placed in relation to other related studies? Aspects that distinguish swarm robotics studies from: other flavors of multi-robot studies other related studies such as Swarm Intelligence, Sensing networks, etc.. 9/29

0 - Individuals should be robots! Individuals should be autonomous robots. Individuals should be situated and autonomous be able to physically interact Mobility of individuals is sufficient, but not required. Metamorphic robotic systems? Yes Sensor networks? No 10/29

1 - Large number of robots The study should be relevant for the coordination of large numbers of robots. Why relevancy? How large is large? 11/29

2 - Few homogeneous groups of robots The robotic system should consist of few homogeneous groups and that the number of robots in each group should be large. Teams are not swarms. Hierarchical robotic systems (for instance swarms with a designated queen ) are less `swarm robotic. What s a homogeneous group? How about individual adaptation? 12/29

3 - Relatively incapable of inefficient robots The robotic system should utilize relatively incapable or inefficient robots with respect to the task at hand. The robots should have difficulties in carrying the task on their own. The deployment of a group of robots should improve the performance of system. The deployment of a group of robots should improve the robustness of the system. 13/29

4 - Robots with only local sensing and communication abilities For coordinating their actions, the robots should utilize only local sensing and communication capabilities. Locality promotes scalability. Existence of global communication channels not used for coordination among the robots does not violate. 14/29

Criteria for Swarm Robotic systems A swarm robotic system should consist of large numbers of robots, few homogeneous groups of robots, robots that are relatively incapable or inefficient, robots with only local sensing and communication abilities. Not a checklist for evaluating a study. But as yardsticks to evaluate how `swarm robotic a given study is. 15/29

Finally a definition Swarm robotics is the study of how large number of relatively simple physically embodied agents can be designed such that a desired collective behavior emerges from the local interactions among agents and between the agents and the environment. 16/29

Sources of inspiration Self-organizing natural systems Social insect systems: ants, termites, wasps, bees, cockroaches, locusts Animals with social behaviors: penguins, birds, fish, sheep... Unicellular organisms: Amoebae, bacteria, viruses Artificial self-organizing systems Amorphous computing Self-assembly of materials 17/29

Aggregation of amoebae into slime mould When food is abundant, amoebae (D. discoideum) acts independently of others, feeding and multiplying (Bonner;1967, Goldbeter;1996). When food supply is depleted amoebae release camp ( a chemo-attractant for amoeba) into the extracellular environment. Amoebae aggregate forming a slug, a multi-cellular organism which can move and sporulate. Summarized from Self-Organization in Biological Systems by Scott Camazine, Jean-Louis Deneubourg, Nigel R. Franks, James Sneyd, Guy Theraulaz, and Eric Bonabeau 18/29

Aggregation mechanism Amoebae secrete camp leading to spiral waves that streams the cells to the center Positive feedback mechanisms Release camp with a certain period (oscillatory mode). If hit by a camp pulse, amplify it (relay mode). Negative feedback mechanism High camp concentrations briefly desensitize the receptors. Amoebae moves in the direction of increasing camp concentration (at 1/10 of the camp wave speed). Cell-to-cell adhesion makes amoeba clumps persistent. 19/29

Take-home lesson/inspiration The mechanism discussed aggregate 10,000-100,000 cells! In a recent study (Dorigo et al.;2004), it is shown that aggregating tens of robots (equipped with simulated speakers and microphones) is very challenging. Stigmergy seems to be a key element for scalability! Stigmergy in a swarm of robots Natural stigmergy: Using water, chemicals, etc.. Stigmergy using embedded systems: e.g. Gnats (Balch;) Stigmergy using robots: Use some of the robots as the medium while others aggregate. 20/29

Quorum sensing in bacteria Bacteria seem to have interesting communication mechanisms to increase their survival. V. fischeri produces light when its population reach a critical size. V. cholarae delays the production of virulence factor until they reach a certain mass, to ensure a successful infection against the infection system. Recent studies show that bacteria use certain auto-inducers to detect their density in the environment. B.L. Bassler, How bacteria talk to each other: regulation of gene expression by quorum sensing. Current Opinions in Microbiology 1999 Dec;2(6): 582-7. 21/29

Take home lesson/inspiration Quorum sensing will be an essential problem for swarm robotic systems. The density of individuals is an important parameter in natural swarms which can lead to bifurcations in swarm behavior. Density measurement w/o stigmergy is likely to be an interesting challenge. 22/29

Information exchange in bacteria Bacterial colonies can be more resistant to antibiotics than bacteria living in suspension! Hypothesis: Bacteria form a genomic web communicating with each other: Inducive communication: a chemical signal triggers a certain action in other bacteria. Informative communication: the message received is interpreted by the cell, and its response is determined by its history as well as its current state. E. Ben-Jacob, Bacterial self-organization: co-enhancement of complexification and adaptability in a dynamic environment. Phil. Trans. R. Soc. Lond. A, 361, pp 1283 1312, 2003. 23/29

Take home lesson/inspiration In real life, some individuals of a swarm robotic systems will probably discover certain hazards the hard way. Individuals should be able to pass lastminute signals and information to the rest of the swarm. 24/29

Amorphous computing Challenge: How can prespecified, coherent behavior be engineered from the cooperation of vast numbers of unreliable parts interconnected in unknown and time-varying ways? Medium: a system of irregularly placed, asynchronous, locally interacting computing elements. Inspiration and approach: morphogenetic processes in biological systems such as tissue growth. Amorphous Computing, Abelson et al, Communications of the ACM, Volume 43, Number 5, May 2001. 25/29

Take home lesson/inspiration Amorphous computing nodes [if and when they become available] can be active intelligent pheromones of swarm robotic systems. Swarm robotic systems, when immobile, are amorphous computing mediums and can utilize their programming paradigms. 26/29

Self-assembly Self-assembly: self-organization by making physical bond formation Individuals lose some of their motility. This creates some interesting dynamics. Social insects and breakable bonds in chemistry Self-assembly of materials is described as the autonomous organization of components into patterns or structures without [external] intervention. Whitesides and Grzybowski (Science; 2002) Self-assembly is a promising method for fabricating regular structures: nano-scale self-assembly is promising for building large numbers of micro- electro-mechanical systems (MEMS), improving the robotic assembly processes. 27/29

Take home lesson/inspiration Use of templates for scaffolding the selfassembly/organization process to reduce defects in the structure. Catalytic agents to improve the selfassembly process. 28/29

Thanks for listening..