Swarm Robotics an overview Vito Trianni, PhD Institute of - PowerPoint PPT Presentation

Swarm Robotics 
 – an overview – Vito Trianni, PhD Institute of Cognitive Sciences and Technologies National Research Council vito.trianni@istc.cnr.it

swarm robotics • swarm robotics studies robotic systems composed of 
 a multitude of interacting units homogeneous systems or few heterogeneous groups • each unit is relatively simple and inexpensive • • individual limitations, absence of global information limitations can be physical or functional • access to local and incomplete information only • • decentralised control no single point of failure • redundancy is built-in in the system • • expected properties: • parallelism • robustness • adaptivity • scalability • efficiency

swarm robotics • simple individuals and simple behaviours • complexity results from cooperation • research mainly focuses on: development of specific hardware to support • communication and physical interactions development and test of swarm control systems • • problem: how to define individual rules?

design of decentralised systems distributed SWARM WIRELESS • ROBOTICS SENSOR large number of • NETWORKS interconnected agents self-organised • ? DESIGN macroscopic individual PROBLEM behaviour agent rules Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M., & Trianni, V. (2015). A Design Pattern for Decentralised Decision Making. PLoS ONE, 10(10), e0140950–18.

design patterns • reusable solutions for a specific class of problems • leverage on the principled understanding of 
 theoretical models of collective systems

what design rationale 
 for robot swarms?

super-organisms

Swarm-Bots (2004)

Swarmanoid (2011)

Kilobots (2014)

Verity Studios (2017)

perspectives • potential application domains • agriculture and precision farming • security, search&rescue • logistics • space exploration • swarm robotics still confined into the lab • more research needed for higher cognitive skills • collective decision-making • task allocation • categorisation • learning

collective decisions

collective decisions • definition : 
 the process that leads a group to identify 
 the best option out of several alternatives • precondition : 
 partial/noisy information about the available alternatives • postcondition : 
 the group (or a large majority) shares the same choice • constraints : 
 individuals cannot know/compare all alternatives

decentralised decision making • best-of- n decision problem • set of n options • each option i has a quality v i • GOAL: select the best (or equal-best) option ? macroscopic individual behaviour agent rules - discover the options which rules should 
 - estimate their qualities each agent follow? - select the best one

design rationale nest-site selection in honeybees + attains near-optimal 
 speed-accuracy tradeoff + no need of direct comparison between option qualities + adaptive mechanisms to tune decision speed and break symmetry deadlocks

collective decisions in bees a swarm needs to select the new nesting site

collective decisions in bees scout bees identify the available alternatives and share information through the ‘waggle dance’

modelling collective decisions committed agents U A uncommitted agents B …

modelling collective decisions U A discovery of alternatives B γ A U A γ B U B

modelling collective decisions U A abandonment of commitment B α A A U α B B U

modelling collective decisions A U A B recruitment to discovered alternatives B ρ A U+A A+A ρ B U+B B+B

nest-site selection model discovery: ˙  Ψ A = γ A Ψ U − α A Ψ A + ρ A Ψ A Ψ U U A  ˙ Ψ B = γ B Ψ U − α B Ψ B + ρ B Ψ B Ψ U U B Ψ U = 1 − Ψ A − Ψ B  abandonment: A U Ψ B σ B A B B U Ψ A σ A recruitment: γ B A+U A+A Ψ A ρ A γ A Ψ B ρ B B+U B+B α A α B U

nest-site selection model discovery: ˙  Ψ A = γ A Ψ U − α A Ψ A + ρ A Ψ A Ψ U U A  ˙ Ψ B = γ B Ψ U − α B Ψ B + ρ B Ψ B Ψ U U B Ψ U = 1 − Ψ A − Ψ B  abandonment: A U Ψ B σ B A B B U Ψ A σ A recruitment: γ B A+U A+A Ψ A ρ A γ A Ψ B ρ B B+U B+B α A α B U

modelling collective decisions A A B switch of alternatives B σ B B+A A+A σ A A+B B+B

nest-site selection model discovery: ˙  = γ A Ψ U − α A Ψ A + ρ A Ψ A Ψ U − ( σ B − σ A ) Ψ A Ψ B Ψ A  U A ˙ = γ B Ψ U − α B Ψ B + ρ B Ψ B Ψ U − ( σ A − σ B ) Ψ A Ψ B Ψ B U B = 1 − Ψ A − Ψ B Ψ U  abandonment: A U Ψ B σ B A B B U Ψ A σ A recruitment: γ B A+U A+A Ψ A ρ A γ A Ψ B ρ B B+U B+B α A α B direct switch: U A+B A+A B+A B+B

nest-site selection model discovery: ˙  = γ A Ψ U − α A Ψ A + ρ A Ψ A Ψ U − ( σ B − σ A ) Ψ A Ψ B Ψ A  U A ˙ = γ B Ψ U − α B Ψ B + ρ B Ψ B Ψ U − ( σ A − σ B ) Ψ A Ψ B Ψ B U B = 1 − Ψ A − Ψ B Ψ U  abandonment: A U Ψ B σ B A B B U Ψ A σ A recruitment: γ B A+U A+A Ψ A ρ A γ A Ψ B ρ B B+U B+B α A α B direct switch: U A+B A+A B+A B+B

nest-site selection model discovery: ˙  = γ A Ψ U − α A Ψ A + ρ A Ψ A Ψ U − ( σ B − σ A ) Ψ A Ψ B Ψ A  U A ˙ = γ B Ψ U − α B Ψ B + ρ B Ψ B Ψ U − ( σ A − σ B ) Ψ A Ψ B Ψ B U B = 1 − Ψ A − Ψ B Ψ U  abandonment: A U Ψ B σ B A B B U Ψ A σ A recruitment: γ B A+U A+A Ψ A ρ A γ A Ψ B ρ B B+U B+B α A α B direct switch: U A+B A+A B+A B+B

T. D. Seeley, P. K. Visscher, T. Schlegel, P. M. Hogan, N. R. Franks, and J. A. R. Marshall, “Stop Signals Provide Cross Inhibition in Collective Decision-Making by Honeybee Swarms”. Science, vol. 335, no. 6064, pp. 108–111, 2012.

modelling collective decisions A A B U cross-inhibition σ A B+A U+A σ B B A+B U+B

nest-site selection model discovery: ˙  Ψ A = γ A Ψ U − α A Ψ A + ρ A Ψ A Ψ U − ( σ A − σ B ) Ψ A Ψ B U A  ˙ Ψ B = γ B Ψ U − α B Ψ B + ρ B Ψ B Ψ U − ( σ B − σ A ) Ψ A Ψ B , U B Ψ U = 1 − Ψ A − Ψ B  abandonment: A U Ψ B σ B B U A B Ψ A σ A recruitment: A+U A+A γ B Ψ A ρ A γ A Ψ B ρ B B+U B+B α A α B direct switch: A+B A+A U B+A B+B

nest-site selection model discovery: ˙  Ψ A = γ A Ψ U − α A Ψ A + ρ A Ψ A Ψ U − σ B Ψ A Ψ B U A  ˙ Ψ B = γ B Ψ U − α B Ψ B + ρ B Ψ B Ψ U − σ A Ψ A Ψ B , U B Ψ U = 1 − Ψ A − Ψ B  abandonment: A U B U A B recruitment: A+U A+A γ B γ A Ψ A ρ A B+U B+B Ψ B ρ B α A Ψ B σ B α B cross-inhibition Ψ A σ A A+B A+U U B+A B+U

nest-site selection model discovery: ˙  Ψ A = γ A Ψ U − α A Ψ A + ρ A Ψ A Ψ U − σ B Ψ A Ψ B U A  ˙ Ψ B = γ B Ψ U − α B Ψ B + ρ B Ψ B Ψ U − σ A Ψ A Ψ B , U B Ψ U = 1 − Ψ A − Ψ B  abandonment: A U B U A B recruitment: A+U A+A γ B γ A Ψ A ρ A B+U B+B Ψ B ρ B α A Ψ B σ B α B cross-inhibition Ψ A σ A A+B A+U U B+A B+U

nest-site selection model discovery: ˙  Ψ A = γ A Ψ U − α A Ψ A + ρ A Ψ A Ψ U − σ B Ψ A Ψ B U A  ˙ Ψ B = γ B Ψ U − α B Ψ B + ρ B Ψ B Ψ U − σ A Ψ A Ψ B , U B Ψ U = 1 − Ψ A − Ψ B  abandonment: A U B U A B recruitment: A+U A+A γ B γ A Ψ A ρ A B+U B+B Ψ B ρ B α A Ψ B σ B α B cross-inhibition Ψ A σ A A+B A+U U B+A B+U

design pattern solution multi-level description of the decision process Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M., & Trianni, V. (2015). A Design Pattern for Decentralised Decision Making. PLoS ONE, 10(10), e0140950–18.

design pattern solution multi-level description of the decision process Microscopic Macroscopic Macroscopic description description description infinite-size 
 finite-size 
 agent-based 
 deterministic 
 stochastic 
 stochastic 
 time continuous time continuous time discrete System of ODEs Master equation PFSM A C 1 q j ” =1 P Ψ j P σ j P α 1 ⇢ ˙ 4 n δ P γ 1 Ψ i = γ i Ψ U � α i Ψ i + ρ i Ψ i Ψ U � ∑ j 6 = i σ j Ψ i Ψ j ∑ δ t P ( N , t ) = [ β k � P ( N , t ) Q k ] , 8 N P Ψ 1 P ρ 1 Ψ U = 1 � ∑ i Ψ i k = 1 C U . . . P Ψ n P ρ n P γ n P α n q j ” = n P Ψ j P σ j C n Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M., & Trianni, V. (2015). A Design Pattern for Decentralised Decision Making. PLoS ONE, 10(10), e0140950–18.

design pattern solution multi-level description of the decision process A C 1 j ” =1 P Ψ j P σ j q P α 1 P γ 1 P Ψ 1 P ρ 1 C U . . . P Ψ n P ρ n P γ n P α n j ” = n P Ψ j P σ j q C n Reina, A., Valentini, G., Fernández-Oto, C., Dorigo, M., & Trianni, V. (2015). A Design Pattern for Decentralised Decision Making. PLoS ONE, 10(10), e0140950–18.