An Introduction to

Alcherio Martinoli

SNSF Professor in Computer and Communication Sciences, EPFL Part-Time Visiting Associate in Mechanical Engineering, CalTech

Swarm-Intelligent Systems Group École Polytechnique Fédérale de Lausanne CH-1015 Lausanne, Switzerland http://swis.epfl.ch/ [email protected]

Tutorial at -06, Bruxelles, September 4, 2006 Outline • Background – Mobile robotics – – • Model-Based Analysis of SRS – Methodological framework – Examples • Machine-Learning-Based Synthesis of SRS – Methodological framework – Combined method (model/machine-learning-based) – Examples • From SRS to other Real-Time, Embedded Platforms • Conclusion and Outlook Background: Mobile robotics An Example of Mobile : Khepera (Mondada et al., 1993)

actuators

sensors

microcontrollers batteries

5.5 cm Strengths: size and modularity! Perception-to-Action Loop

• Reactive (e.g., linear or nonlinear transform) •sensors • Reactive + memory (e.g. filter, •actuators state variable) • Deliberative (e.g. planning)

Computation Action Perception

Environment Autonomy in Mobile Robotics

Human-Guided Task Complexity Robotics

Swarm Robotics ? Research Autonomous Robotics Industry Autonomy Different levels/degrees of autonomy: • Energetic level • Sensory, actuatorial, and computational level • Decisional level Background: Swarm-Intelligent Systems Swarm Intelligence Definitions

• Beni and Wang (1990): – Used the term in the context of cellular automata (based on cellular concept of Fukuda) – Decentralized control, lack of synchronicity, simple and (quasi) identical members, self-organization • Bonabeau, Dorigo and Theraulaz (1999) – Any attempt to design algorithms or distributed solving devices inspired by the collective behavior of social colonies and other animal societies • Beni (2004) – Intelligent swarm = a group of non-intelligent robots (“machines”) capable of universal computation – Usual difficulties in defining the “intelligence” concept (non predictable order from disorder, creativity) Swarm-Intelligent Systems: Features • Bio-inspirationBeyond bio-inspiration: combine natural principles– social insect with societiesengineering knowledge and– ,technologies shoaling in vertebrates • Unit coordination – fully distributed control (+ env. template) – individual autonomy – self-organization • Communication – direct local communication (peer-to-peer) – indirect communication through signs in the environment () • Scalability • RobustnessRobustness vs. efficiency trade-off – redundancy – balance exploitation/exploration – individual simplicity • System cost effectiveness – individual simplicity – mass production Current Tendencies • IEEE SIS-05 – self-organization, distributedness, parallelism, local communication mechanisms, individual simplicity as invariants – More interdisciplinarity, more engineering, biology not the only reservoir for ideas

•ANTS-06, IEEE SIS-06 follow the tendency; IEEE SIS-07 even more so Background: Swarm Robotics First Swarm-Robotics Demonstration Using Real Robots (Beckers, Holland, and Deneubourg, 1994) Swarm Robotics: A new Engineering Discipline?

• Why does it work? • What are the principles? • Is a new paradigm or just an isolated experiment? • If yes, can we define it? • Can we generalize these results to other tasks and experimental scenarios? • How can we design an efficient and robust SR system? Methods? • How can we optimize a SR system? •… Swarm Robotics – Features

Dorigo & Sahin (2004) • Relevant to the coordination of large number of robots • The robotic system consists of a relatively few homogeneous groups, number of robots per group is large • Robots have difficulties in carrying out the task on their own or at least performance improvement by the swarm • Limited local sensing and communication ability Swarm Robotics – [Selected/Pruned] Definitions • Beni (2004) The use of labels such as swarm robotics should not be in principle a function of the number of units used in the system. The principles underlying the multi-robot system coordination are the essential factor. The control architectures relevant to swarms are scalable, from a few units to thousands or million of units, since they base their coordination on local interactions and self-organization.

• Sahin, Spears, and Winfield (2006) 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. It is a novel approach to the coordination of large numbers of robots. SWIS Mobile Robotic Fleet

Moorebot II – PC 104, XScale processor, Linux, WLAN 802.11; available robots: # 4

Khepera III – XScale processor, Linux, WLAN 802.11, Bluetooth; #20

E-puck – dsPIC, PICos, WLAN 802.15.4, Bluetooth; #100 24 cm Alice II – 11 cm PIC, no OS, WLAN Size & modularity ! 802.15.4, IR 6 cm com; #40 Standards, com, and batt. changing! 2 cm size SWIS Research Thrusts System engineering & integration (single node)

Multi-level modeling, Automatic (machine- model-based methods learning-based) design & optimization Model-Based Approach

(main focus: analysis) S S s s S s S s Multi-Level Modeling Methodology S S a a S a S a dN dt n ( t field approach,wholeswarm Macroscopic 1 agent=robot only relevantrobotfeaturecaptured, Microscopic ) details reproduced faithfully and environment (e.g.,physics) Realistic environmental features controller, S&A, morphology and Physical reality = ∑∑ nn ′′ W ( n | n : intra-robot(e.g., S&A) ′ , t ) : multi-agentmodels, N : rateequations,mean n ′ ( t ) − : Infoon W ( n ′ | n , t ) N n ( t )

Experimental time

Abstraction

Common metrics Originality and Differences with other Research Contributions • The proposed multi-level modeling method is specifically target to self-organized (miniature) collective systems (mainly artificial up to date); exploit robust control design techniques at individual level (e.g. BB, ANN) and predict collective performance through models

• Different from traditional modeling approach in robotics for collective robotic systems: start from unrealistic assumptions (noise free, perfectly controllable trajectories, no com delays, etc.) and relax assumptions compensating with best devices available & computationally intensive on-board algorithms

• Different from traditional modeling approaches in biology (and similar in physics, chemistry) for insect/animal societies: as simple as possible macroscopic models targeting a given scientific question; free parameters + fitting based on macroscopic measurements since often microscopic information not available/accurate Micro/Macro Modeling Assumptions • Nonspatial metrics for swarm performance

• Environment and multi-agent system can be described as Probabilistic FSM; the state granularity of the description is arbitrarily established by the researcher as a function of the abstraction level and design/optimization interest

• Both multi-agent system and environment are (semi-) Markovian: the system future state is a function of the current state (and possibly amount of time spent in it)

• Mean spatial distribution of agents is either not considered or assumed to be homogeneous, as they were randomly hopping on the arena (trajectories not considered, mean field approach) Microscopic Level

Ss Sa R11 Se Sd Rn1 S S S R i s a 12 … …

… Se Sd Rnm Ss Sa R1l Si Caste 1 Caste n Robotic System (N PFSM; Coupling (e.g., manipulation, sensing) N = total # agents)

S S S S a b … a b ……

O O11 O1p q1 Oqr

Environment (Q PFSM; Q = total # objects) Macroscopic Level (1)

Robotic (PFSM)

Ss Sa Caste1 • average quantities • central tendency prediction (1 run) • continuous quantities: +1 ODE per Se Sd state for all robotic castes and object S Caste n i types (metric/task dependent!) • -1 ODEif substituted with conservation equations (e.g., total # of Coupling robots, total # of objects of type q, … )

Type 1 Sa Sb Environment (PFSM) Type q Macroscopic Level (2) If Markov properties are fulfilled, this is what we are looking for:

dNn (t) = W (n | n′,t)N ' (t) − W (n′ | n,t)N (t) ∑∑n n Rate Equation dt nn′′ (time-continuous) inflow outflow n, n’ = states of the agents

Nn = average # of robots in state n at time t W = transition rates (linear, nonlinear)

N ((k +1)T ) = N (kT) + TW (n | n′,kT)N ' (kT) − TW (n′ | n,kT)N (kT) n n ∑ n ∑ n nn′′ Time-discrete version. k = iteration index, T time step (often left out) Model Calibration - Theory • Goal: calibration method for achieving 0-free parameter models, gray-box approach: – As cheap as possible calibration procedure – Models should not only explain but have also predictive power – Parameters should match as much as possible with design choices • Two types of parameters: – Interaction times – Encountering probabilities • Calibration procedures: – Idea 1: run “orthogonal” experiments on local a priori known interactions (robot-to-robot, robot-to-environment) → use for all type of interactions happening these values – Idea 2: use all a priori known information (e.g., geometry) without running experiments → get initial guesses → fine-tune parameters automatically on the target experiment with a as cheap as possible calibration (e.g., fitting algorithm using a subset of the system) Linear Example: Wandering and Obstacle Avoidance A Simple Linear Model

Example: search (moving forwards) and obstacle avoidance

© Nikolaus Correll 2006 A simple Example

Start Start

ps Search Search Avoidance Avoid., τa ps Ss Sa τa

Obstacle? Obstacle? ppa N Y 1-pa a

Nonspatiality PFSM (Markov Chain) & microscopic Deterministic characterization Probabilistic robot’s flowchart agent’s flowchart Linear Model – Constant P Option

ps=1/Ta Search Avoidance, T pa a

Ns(k+1) = Ns(k) - paNs(k) + psNa(k)

Na(k+1) = N0 – Ns(k+1)

Ta = mean obstacle avoidance duration N (0) = N ;N(0) = 0 s 0 a pa = probability of moving to obstacle av. ps = probability of resuming search Ns = average # robots in search Na= average # robots in obstacle avoidance N0 = # robots used in the experiment k = 0,1, … (iteration index) Linear Model – Time Out Option

1 Search Avoidance, T pa a

Ns(k+1) = Ns(k) - paNs(k) + paNs(k-Ta)

Na(k+1) = N0 – Ns(k+1)

Ta = mean obstacle avoidance duration pa = probability moving to obstacle avoidance ! Ns(k) = Na(k) = 0 for all k<0 ! Ns = average # robots in search Ns(0) = N0 ;Na(0) = 0 Na= average # robots in obstacle avoidance N0 = # robots used in the experiment k = 0,1, … (iteration index) Linear Model – Sample Results

Na*/N0

Realistic to micro comparison Micro to macro comparison (different controllers, dynamic/static (same robot density but wall surface scenarios, allocentric/egocentric become smaller with bigger arenas) measures) Nonlinear Example – Stick-Pulling A Case Study: Stick-Pulling

Physical Set-Up Collaboration via indirect communication

• 2-6 robots Arm elevation IR reflective •4 sticks Proximity sensor band • 40 cm radius arena sensors Systematic Experiments

Real robots Realistic simulation

•[Martinoli and Mondada, ISER, 1995] •[Ijspeert et al., AR, 2001] Experimental and Realistic Simulation Results

Nrobots > Nsticks

Nrobots ≤ Nsticks • Real robots (3 runs) and realistic simulations (10 runs) • System bifurcation as a function of #robots/#sticks Geometric Probabilities

Aa = surface of the whole arena

ps = As / Aa

pr = Ar / Aa

pR = pr (N0 −1)

pw = Aw / Aa

pg1 = ps

pg2 = Rg ps From Reality to Abstraction

Markov Chain (PFSM) Deterministic Interaction Probabilistic agent’s robot’s flowchart modeling flowchart Full Macroscopic Model For instance, for the average number of robots in searching mode:

Ns (k + 1) = Ns (k)− [∆ g1 (k)+ ∆ g 2 (k)+ pw + pR ]Ns (k) + ∆ g1 (k − Tcga )Γ(k;Ta )Ns (k − Tcga )

+ ∆g 2 (k − Tca )Ns (k − Tca ) + ∆g 2 (k − Tcda )Ns (k − Tcda ) + pw Ns (k − Ta ) + pR Ns (k − Tia ) with time-varying coefficients (nonlinear coupling):

∆ g1(k) = pg1[M 0 − N g (k) − N d (k)]

∆ g 2 (k) = pg 2 N g (k)

k−TSL Γ(k;TSL ) = ∏[1− pg 2 N s ( j)] j=k−Tg −TSL

• 6 states: 5 DE + 1 cons. EQ

• Ti,Ta,Td,Tc ≠ 0; Τxyz = Τx + Τy + Τz • TSL= Shift Left duration • [Martinoli et al., IJRR, 2004] Swarm Performance Metric

Collaboration rate: # of sticks per time unit

C(k) = pg2Ns(k-Tca)Ng(k-Tca) : mean # of collaborations at

Te iteration k ∑C(k) k=0 Ct (k) = : mean collaboration rate Te over Te Sample Results

Webots (10 runs), microscopic (100 runs), macroscopic model (1 run) Simplified Macroscopic Model (1)

Τi,Τa,Τd,Τc << Τg →Τi=Τa=Τd=Τc=0 Simplified Macroscopic Model (2) successful Nonlinear DE coupling through unit-to-unit interaction (in this Search Grip case through the stick)! unsuccessful

Ns(k+1) = Ns(k) – pg1[M0 –Ng(k)]Ns(k) + pg2Ng(k)Ns(k)

+ pg1[M0 –Ng(k-Τg)]Γ(k;0)Ns(k-Tg)

N (k+1) = N – N (k+1) g 0 s Ns = average # robots in searching mode k Ng= average # robots in gripping mode N = # robots used in the experiment Γ(k;0) = ∏[1− pg 2 Ns ( j)] 0 j=k−Tg M0 = # sticks used in the experiment Γ = fraction of robots that abandon pulling Initial conditions and causality Te = maximal number of iterations N (0) = N , N (0) = 0 s 0 g k = 0,1, …Te (iteration index) Ns(k) = Ng(k) = 0 for all k<0 Steady State Analysis (Simplified Model)

• Steady-state analysis → It can be demonstrated that:

opt N0 2 ∃ Tg for ≤ M 0 1+ Rg

with N0 = number of robots and M0= number of sticks, Rg∝ approaching angle for collaboration

approaching angle for collaboration

• Counterintuitive conclusion: an optimal Tg can exist also in scenarios with more robots than sticks if the collaboration is

very difficult (i.e. Rg very small)! Verification of Analysis Conclusions (Full Model)

20 robots and 16 sticks

(optimal Tg)

~ 1 Example: R = R (collaboration very difficult) g 10 g Optimal Gripping Time

opt • Steady-state analysis → Tg canβ be computed analytically in the simplified model (numerically approximated value): β 1− (1+ R ) 1 g β 2 T opt = ln 2 for ≤ β = g N c 1+ R ln(1− p R 0 ) 1− g g1 g 2 2 with β = N0/M0 = ratio robots-to-sticks

opt •Tg can be computed numerically by integrating the full model ODEs or solving the full model steady-state equations

[Lerman et al, Alife Journal, 2001], [Martinoli et al, IJRR, 2004] Journal Publications

Stick Pulling

• Li, Martinoli, Abu-Mostafa, Adaptive Behavior, 2004 -> learning + micro • Martinoli, Easton, Agassounon, Int. J. of Robotics Res., 2004 -> real + realistic + micro + macro • Lerman, Galstyan, Martinoli, Ijspeert, Artificial Life, 2001 -> realistic + macro • Ijspeert, Martinoli, Billard, and Gambardella, Auton. Robots, 2001 -> real + realistic + micro

Object Aggregation

• Agassounon, Martinoli, Easton, Autonomous Robots, 2004 -> realistic + macro + activity regulation • Martinoli, Ijspeert, Mondada, Robotics and Autonomous Systems -> real + realistic + micro Some Limitations of the current Methods Model Calibration - Practice Bin distribution of interaction time Ta (mean Ta= 25 *50 ms = 1.25 s)

# of collisions Micro model, time-out option Micro model, const P option

Realistic, distal controller Realistic, proximal controller Collision time Model Calibration - Practice

Encountering probability pa: example of transition in space from search to obstacle avoidance (1 moving Alice, 1 dummy Alice, Webots measurements, egocentric)

Distal controller Proximal controller (rule-based) (Braitenberg, linear) Stochastic vs. Deterministic Models

Webots (10 runs), microscopic (100 runs), macroscopic model (1 run) Spatial vs. Nonspatial Models [Correll & Martinoli, DARS-04, ISER-04, ICRA-05, DARS-06, ISER-06, SYROCO-06] Boundary coverage problem (case study turbine inspection)

Spatial models required because: • environmental template • fast performance metrics (e.g. time to completion) • clustered dropping point for robots Unfolded turbine, • networking connectivity blade geometry • algorithms with enhanced navigation reproduced faithfully

2000 Time to Completion

1500

1000

500

0 4 6 8 10 12 14 16 18 20 Number of robots Machine-Learning-Based Approach

(main focus: synthesis) Automatic Design and Optimization

• Evaluative & unsupervised learning: multi-agent (GA, PSO) or single-agent (In Line Adaptive Search, RL) • Targeted to embedded control or system (e.g., hw-sw co- design, multi-objective) • Enhanced noise-resistance (e.g., aggregation criteria, statistical tests) • Customization for distributed platforms (off-line and on- line learning; solutions to the credit assignment problem) • Combined with one or more levels of simulation Rationale for Combined Methods • Application of machine-learning method to the target system (“hardware in the loop”) might be expensive or not always feasible • Any level of modeling allow us to consider certain parameters and leave others; models, as expression of reality abstraction, can be considered as “filters” • Machine-learning techniques will explore the design parameters explicitly represented at a given level of abstraction • Depending on the features of the hyperspace to be searched (size, continuity, noise, etc.), appropriate machine-learning techniques should be used (e.g., single-agent hill-climbing techniques vs. multi-agent techniques) Learning to Avoid Obstacles by Shaping a Neural Network Controller using Genetic Algorithms Evolving a Neural Controller

S3 S4 S S Oi output 2 5 S 1 S6 neuron N with sigmoid f(x ) Ni i transfer function f(x) M1 M2

Oi = f (xi ) wij synaptic 2 weight f (x) = −1 1+ e−x Ij S S m 8 7 input xi = ∑ wij I j + I0 inhibitory conn. j=1 excitatory conn.

Note: In our case we evolve synaptic weigths but Hebbian rules for dynamic change of the weights, transfer function parameters, … can also be evolved Evolving Obstacle Avoidance (Floreano and Mondada 1996)

Defining performance (fitness function):

Φ =V (1− ∆V )(1− i)

• V = mean speed of wheels, 0 ≤ V ≤ 1 • ∆v = absolute algebraic difference between wheel speeds, 0 ≤∆v ≤ 1 • i = activation value of the sensor with the highest activity, 0 ≤ i ≤ 1

Note: Fitness accumulated during evaluation span, normalized over number of control loops (actions). Evolving Robot Controllers

Note: Controller architecture can be of any type but worth using GA/PSO if the number of parameters to be tuned is important Evolving Obstacle Avoidance

Evolved path

Fitness evolution Evolved Obstacle Avoidance Behavior

Generation 100, on-line, off-board (PC-hosted) evolution

Note: Direction of motion NOT encoded in the fitness function: GA automatically discovers asymmetry in the sensory system configuration (6 proximity sensors in the front and 2 in the back) From Single to Multi-Unit Systems: Co-Learning in a Shared World Evolution in Collective Scenarios

• Collective: fitness become noisy due to partial perception, independent parallel actions Credit Assignment Problem

With limited communication, no communication at all, or partial perception: Co-Learning Collaborative Behavior Three orthogonal axes to consider (extremities or balanced solutions are possible): • Individual and group fitness • Private (non-sharing of parameters) and public (parameter sharing) policies • Homogeneous vs. heterogeneous systems

Example with binary encoding of candidate solutions Co-Learning Competitive Behavior

fitness f1 ≠ fitness f2 Learning to Avoid Obstacle using Noise-Resistant Algorithms

(Example 1 of the Combined Method, realistic level with GA and PSO) Noisy Optimization

• Multiple evaluations at the same point in the search space yield different results • Depending on the optimization problem the evaluation of a candidate solution can be more or less expensive in terms of time • Causes decreased convergence speed and residual error • Little exploration of noisy optimization in evolutionary algorithms, and very little in PSO Key Ideas

• Better information about candidate solution can be obtained by combining multiple noisy evaluations • We could evaluate systematically each candidate solution for a fixed number of times → not smart from computational point of view • In particular for long evaluation spans, we want to dedicate more computational power/time to evaluate promising solutions and eliminate as quickly as possible the “lucky” ones → each candidate solution might have been evaluated a different number of times when compared • In GA good and robust candidate solutions survive over generations; in PSO they survive in the individual memory •Use aggregation functions for multiple evaluations: ex. minimum and average GA PSO A Systematic Study on Obstacle Avoidance – 3 Different Scenarios

PSO, 50 iterations, scenario 3 • Scenario 1: One robot learning obstacle avoidance • Scenario 2: One robot learning obstacle avoidance, one robot running pre-evolved obstacle avoidance • Scenario 3: Two robots co-learning obstacle avoidance

Idea: more robots more noise (as perceived from an individual robot); no “standard” com between the robots but in scenario 3 information sharing through the population manager! Scenario 3 Three orthogonal axes to consider (extremities or balanced solutions are possible): • Individual and group fitness • Private (non-sharing of parameters) and public (parameter sharing) policies • Homogeneous vs. heterogeneous systems

Example with binary encoding of candidate solutions Results – Best Controllers Fair test: same number of evaluations of candidate solutions for all algorithms (i.e. n generations/ iterations of standard versions compared with n/2 of the noise-resistant ones) Results – Average of Final Population

Fair test: idem as previous slide Learning to Pull Sticks

(Example 2 of the Combined Method, microscopic level with in-line adaptive search) Not Always a big Artillery such a GA/PSO is the Most Appropriate Solution…

• Simple individual learning rules combined with collective flexibility can achieve extremely interesting results • Simplicity and low computational cost means possible embedding on simple, real robots In-Line Adaptive Learning (Li, Martinoli, Abu-Mostafa, 2001)

•GTP:Gripping Time Parameter • ∆d: learning step •d:direction • Underlying low-pass filter for measuring the performance In-Line Adaptive Learning

Differences with gradient descent methods: • Fixed rules for calculating step increase/decrease → limited descent speed → no gradient computation → more conservative but more stable • Randomness for getting out from local minima (no momentum) • Underlying low-pass filter is part of the algorithm Differences with Reinforcement Learning: • No learning history considered (only previous step)

Differences with basic In-Line Learning: • Step adaptive → faster and more stability at convergence Enforcing Homogeneity Three orthogonal axes to consider (extremities or balanced solutions are possible): • Individual and group fitness • Private (non-sharing of parameters) and public (parameter sharing) policies • Homogeneous vs. heterogeneous systems

Example with binary encoding of candidate solutions Sample Results – Homogeneous System

Short averaging window Long averaging window (filter cut-off f high) (filter cut-off f low)

1.4 1.4 6 robots 6 robots

1.2 1.2

1 1 5 robots 5 robots

0.8 0.8

0.6 4 robots 0.6 4 robots

0.4 0.4 3 robots

Stick−pulling rate (1/min) 3 robots Stick−pulling rate (1/min)

0.2 2 robots 0.2 2 robots

0 0 0 100 200 300 400 500 600 0 100 200 300 400 500 600 Initial gripping time parameter (sec) Initial gripping time parameter (sec) Systematic (mean only) Note: 1 parameter for the Learned (mean + std dev) whole group! Allowing Heterogeneity Three orthogonal axes to consider (extremities or balanced solutions are possible): • Individual and group fitness • Private (non-sharing of parameters) and public (parameter sharing) policies • Homogeneous vs. heterogeneous systems Impact of Diversity on Performance (Li, Martinoli, Abu-Mostafa, 2004)

1.3 Notes: 2−caste, Global Heterogeneous, Global • global = group 1.25 Heterogeneous, Local • local = individual

1.2

1.15 Specialized teams

1.1 Stick−pulling rate ratio 1.05 Homogeneous 1 teams (baseline) 2 3 4 5 6 Number of robots Performance ratio between heterogeneous (full and 2- castes) and homogeneous groups AFTER learning Diversity Metrics (Balch 1998) Entropy-based diversity measure introduced in AB-04 could be used for analyzing threshold distributions

Simple entropy: Social entropy:

pi = portion of the agents in cluster i; m cluster in total; h = taxonomic level parameter Specialization Metric

Specialization metric introduced in AB-04 could be used for analyzing specialization arising from a variable-threshold division of labor algorithm

S = specialization; D = social entropy; R = swarm performance

Note: this would be in particular useful when the number of tasks to be solved is not well-defined or it is difficult to assess the task granularity a priori. In such cases the mapping between task granularity and caste granularity might not trivial (one-to-one mapping? How many sub-tasks for a given main task, etc. see the limited performance of a caste-based solution in the stick-pulling experiment) Sample Results in the Standard Sticks • 2 serial grips needed to get the sticks out • 4 sticks, 2-6 robots, 80 cm arena

Relative Performance Diversity Specialization • Spec more important for • Flat curves, difficult to tell • Specialization higher with small teams whether diversity bring global when needed, drop • Local p > global p performance more quickly when not • enforced caste: pay the needed price for odd team sizes • Enforcing caste: low-pass filter Remarks on the Standard Set-Up Results

• When local and global performance are almost aligned (i.e. “by doing well locally I do well globally”), local performance achieve slightly better results since no credit assignment • Nevertheless, global performance less noisy, so part of diversity for increasing performance higher with global performance (“specialization when needed”) From Robots to other Embedded, Distributed, Real- Time Systems Embedded, Real-Time SI-Systems

Symbiotic societies Traffic systems Social

? Networks of S&A

Vertebrates

Multi-robot systems Pedestrians Embedded, Real-Time SI-Systems: Common Features

• Real-world systems (noise, small heterogeneities, …)

• From a few to millions of units (but not 1023!)

• Embodiment, sensors, actuators, often mobility and energy limitations

• Local intelligence, behavioral rules, autonomous units

• Local interaction, communication (unit-to-unit, unit-to-environment) S s S s S s Collaborative DecisioninSensorNetworks S a S a S a models, 1agent=node simulation,multi-agent montecarlo Microscopic 1 field approach,wholenetwork Macroscopic models, 1agent=node simulation,multi-agent montecarlo Microscopic 2 Omnet++ plugin) with reproduced faithfully (Webots communication channel Realistic sensor nodes available Physical reality : intra-nodedetails and : rateequations,mean : spatial2D osail1D : nonspatial : detailed infoon Cac et al.,inpreparation] [Cianci

Experimental time

Abstraction

Common metrics kN Nj jNk p k N j pNkjp Nk Nk jjrs j j )( )(1 )( () () () 1) ( () () 1) ( =+−− − + += S s −++ S pjNkjpjNkj s ev leave leave S s )( )() 1) ( () () jj ⎣⎦ ⎡⎤ http://leurre.ulb.ac.be/ onjoin join Leurre: MixedInsect-RobotSocieties S a S a S − + a 1 1 field approach,wholeswarm Macroscopic description forallnodes agent =1robotorcockroach; similar Microscopic animation cockroaches: body volume+ details reproduced faithfully; environment (e.g., shelter,arena) Realistic behavior measurable externally of cockroaches, individual robots; limited info onphysiology Physical reality : intra-robotdetails, : multi-agentmodels,1 : rateequations,mean [Correll et al., IROS-06; ALife J. inpreparation] J. etal.,IROS-06;ALife [Correll : detailed infoon

Experimental time

Abstraction

Common metrics Supra-Molecular Chemical System [Mermoud et al., 2006, in preparation]

Macroscopic 1: Chemical equilibrium is completely defined by equilibrium constants K of each reaction (law of mass action) Macroscopic 2: Reactions kinetics describes how a reaction occurs and at which speed (differential equations) Common metrics Ss Sa Ss Sa Microscopic 1: Agent-Based model, molecules

S S Abstraction s a geometry abstracted, 1 agent = 1 aggregate

Microscopic 2: Agent-Based model, molecules 2D- and 3D geometry captured, 1 agent = 1 aggregate

Physical reality: microscopic

(e.g., crystallography) and macroscopic Experimental time measurements (chemical reaction) TBD SAILS: 3DSelf-Assembling Blimps http://www.mascarillons.org field approach,wholeswarm? Macroscopic Webots maintained, visualizationwith agent =1blimp;trajectory Microscopic realistic fluiddynamics yet) environment simplified (no Realistic robots Physical reality : intra-robotdetails, : multi-agentmodels,1 : rateequations,mean : detailed infoon Nmrn et al.,IEEE-SIS,GA,2005] [Nembrini

Experimental time

Abstraction

Common metrics Conclusions Lessons Learned over 10 Years 1. Stress methodological effort with computer & mathematical tools; exploit synergies among the three main research thrusts 2. Keep closing the loop between theory and experiments with simulation 3. Formally proof claims using simple models and show experimental excellence with realistic conditions → seek for system dependability 4. Choose case studies that are relevant for applications 5. Focus on system design and use off-the-shelf components and platforms Lessons Learned over 10 Years 6. Leverage all the technologies you can from other markets (OS, wireless com, S&A, batteries) and go beyond bio-inspiration 7. Team-up with other research specialists and companies for specific problems and applications 8. Push towards miniaturization; probably key for non-military applications in swarm robotics 9. Consider other forms of coordination other than self-organization (swarm intelligence just one form of distributed intelligence) 10. Consider other artificial/natural platforms (e.g. static S&A networks, mixed societies, chemical systems, intelligent vehicles, 3D moving units) Some Pointers for Swarm Robotics (1) • Events: in additions to ANTS, ICRA, IROS: – IEEE SIS (2003, 2005, 2006, 2007) – DARS (1992 - , biannual) – Swarm Robotics Workshop at SAB (2002, 2004) • Books: – “Swarm Intelligence: From Natural to Artificial Systems", E. Bonabeau, M. Dorigo, and G. Theraulaz, Santa Fe Studies in the Sciences of Complexity, Oxford University Press, 1999. – Balch T. and Parker L. E. (Eds.), “Robot teams: From diversity to polymorphism”, Natick, MA: A K Peters, 2002. • Journal special issues: – Robotics, 2001, Annuals of Mathematics and Artificial Intelligence – Swarm Robotics, 2004, Autonomous Robots Some Pointers for Swarm Robotics (2) • Projects and further pointers in addition to SWIS activities: – SwarmBot (next tutorial): http://www.swarm-bots.org/ –I-Swarm: http://www.i-swarm.org/ – Leurre: http://leurre.ulb.ac.be/index2.html – BORG group at Georgia Tech: http://borg.cc.gatech.edu/ – Rus robotics group at MIT: http://groups.csail.mit.edu/drl/ – RESL at USC: http://www-robotics.usc.edu/~embedded/ –IASL at UWE: http://www.ias.uwe.ac.uk/ – Robotics at Essex: http://cswww.essex.ac.uk/essexrobotics/ – Race at Uni Tokyo: http://www.race.u-tokyo.ac.jp/index_e.html – Fukuda’s laboratory: http://www.mein.nagoya-u.ac.jp/ – Swarm robotics we page (by E. Sahin): http://swarm-robotics.org/