An Introduction to Swarm Robotics

Transcription

1 An Introduction to Swarm Robotics 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 Tutorial at ANTS-06, Bruxelles, September 4, 2006

2 Outline Background Mobile robotics Swarm Intelligence Swarm 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

3 Background: Mobile robotics

4 An Example of Mobile Robot: Khepera (Mondada et al., 1993) actuators sensors microcontrollers batteries 5.5 cm Strengths: size and modularity!

5 Perception-to-Action Loop sensors Reactive (e.g., linear or nonlinear transform) Reactive + memory (e.g. filter, state variable) Deliberative (e.g. planning) actuators Perception Computation Action Environment

6 Autonomy in Mobile Robotics Task Complexity Human-Guided Robotics Different levels/degrees of autonomy: Energetic level Sensory, actuatorial, and computational level Decisional level Swarm Robotics? Autonomous Robotics Autonomy Research Industry

7 Background: Swarm-Intelligent Systems

8 Swarm Intelligence Definitions Beni and Wang (1990): Used the term in the context of cellular automata (based on cellular robots 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 insect 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)

9 Swarm-Intelligent Systems: Features Bio-inspiration Beyond bio-inspiration: combine natural principles social insect with societies engineering knowledge and flocking, 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 (stigmergy) Scalability Robustness vs. efficiency trade-off redundancy balance exploitation/exploration individual simplicity System cost effectiveness individual simplicity mass production

10 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

11 Background: Swarm Robotics

12 First Swarm-Robotics Demonstration Using Real Robots (Beckers, Holland, and Deneubourg, 1994)

13 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?

14 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

15 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.

16 SWIS Mobile Robotic Fleet Moorebot II PC 104, XScale processor, Linux, WLAN ; available robots: # 4 Khepera III XScale processor, Linux, WLAN , Bluetooth; #20 24 cm 11 cm Size & modularity! 6 cm Standards, com, and batt. changing! 2 cm E-puck dspic, PICos, WLAN , Bluetooth; #100 size Alice II PIC, no OS, WLAN , IR com; #40

17 SWIS Research Thrusts System engineering & integration (single node) Multi-level modeling, model-based methods Automatic (machinelearning-based) design & optimization

18 Model-Based Approach (main focus: analysis)

19 Multi-Level Modeling Methodology S s S s S s S s S a S a S a S a dnn( t) = dt W ( n n, t) N ( t) n n n W ( n n, t) N n ( t) Macroscopic: rate equations, mean field approach, whole swarm Microscopic: multi-agent models, only relevant robot feature captured, 1 agent = 1 robot Realistic: intra-robot (e.g., S&A) and environment (e.g., physics) details reproduced faithfully Physical reality: Info on controller, S&A, morphology and environmental features Experimental time Abstraction Common metrics

20 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

21 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)

22 Microscopic Level S s S a R 11 S e S d R n1 S s S a R 12 S s S a R 1l S e S i S d R nm Caste 1 Coupling (e.g., manipulation, sensing) Caste n Robotic System (N PFSM; N = total # agents) S i S a S b S a S b O 11 O 1p O q1 O qr Environment (Q PFSM; Q = total # objects)

23 Macroscopic Level (1) Robotic (PFSM) S s S a S e Coupling S i S d Caste1 Caste n average quantities central tendency prediction (1 run) continuous quantities: +1 ODE per state for all robotic castes and object types (metric/task dependent!) -1 ODEif substituted with conservation equations (e.g., total # of robots, total # of objects of type q, ) S a S b Type 1 Environment (PFSM) Type q

24 Macroscopic Level (2) If Markov properties are fulfilled, this is what we are looking for: dnn( t) dt = W ( n n, t) N ( t) n n n inflow ' W ( n n, t) N outflow n, n = states of the agents N n = average # of robots in state n at time t W = transition rates (linear, nonlinear) n ( t) Rate Equation (time-continuous) N n (( k + 1) T ) = N ( kt) + TW ( n n, kt) N ' ( kt) n n n n TW ( n n, kt) N n ( kt) Time-discrete version. k = iteration index, T time step (often left out)

25 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)

26 Linear Example: Wandering and Obstacle Avoidance

27 A Simple Linear Model Example: search (moving forwards) and obstacle avoidance Nikolaus Correll 2006

28 A simple Example Start Start p s Search Avoidance Search Avoid., τ a p s S s S a τ a Obstacle? Obstacle? N Y 1-p a p a a Deterministic robot s flowchart Nonspatiality & microscopic characterization PFSM (Markov Chain) Probabilistic agent s flowchart

29 Linear Model Constant P Option p s =1/T a Search p a Avoidance, T a N s (k+1) = N s (k) - p a N s (k) + p s N a (k) N a (k+1) = N 0 N s (k+1) N s (0) = N 0 ;N a (0) = 0 T a = mean obstacle avoidance duration p a = probability of moving to obstacle av. p s = probability of resuming search N s = average # robots in search N a = average # robots in obstacle avoidance N 0 = # robots used in the experiment k = 0,1, (iteration index)

30 Linear Model Time Out Option 1 Search p a Avoidance, T a N s (k+1) = N s (k) - p a N s (k) + p a N s (k-t a ) N a (k+1) = N 0 N s (k+1)! N s (k) = N a (k) = 0 for all k<0! N s (0) = N 0 ;N a (0) = 0 T a = mean obstacle avoidance duration p a = probability moving to obstacle avoidance N s = average # robots in search N a = average # robots in obstacle avoidance N 0 = # robots used in the experiment k = 0,1, (iteration index)

31 Linear Model Sample Results N a */N 0 Realistic to micro comparison (different controllers, dynamic/static scenarios, allocentric/egocentric measures) Micro to macro comparison (same robot density but wall surface become smaller with bigger arenas)

32 Nonlinear Example Stick-Pulling

33 A Case Study: Stick-Pulling Physical Set-Up Collaboration via indirect communication 2-6 robots 4 sticks 40 cm radius arena IR reflective band Proximity sensors Arm elevation sensor

34 Systematic Experiments Real robots Realistic simulation [Martinoli and Mondada, ISER, 1995] [Ijspeert et al., AR, 2001]

35 Experimental and Realistic Simulation Results N robots > N sticks N robots N sticks Real robots (3 runs) and realistic simulations (10 runs) System bifurcation as a function of #robots/#sticks

36 Geometric Probabilities s g g s g a w w r R a r r a s s p R p p p A A p N p p A A p A A p = = = = = = / 1) ( / / A a = surface of the whole arena

37 From Reality to Abstraction Deterministic robot s flowchart Interaction modeling Markov Chain (PFSM) Probabilistic agent s flowchart

38 Full Macroscopic Model 6 states: 5 DE + 1 cons. EQ T i,t a,t d,t c 0; Τ xyz = Τ x + Τ y + Τ z T SL = Shift Left duration [Martinoli et al., IJRR, 2004] ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ; ( ) ( ) ( ] ) ( ) ( [ ) ( 1) ( ia s R a s w cda s cda g ca s ca g cga s a cga g s R w g g s s T k N p T k N p T k N T k T k N T k T k N T k T k k N p p k k k N k N Γ = + For instance, for the average number of robots in searching mode: = = Γ = = SL SL g T k T T k j s g SL g g g d g g g j N p T k k N p k k N k N M p k )] ( [1 ) ; ( ) ( ) ( )] ( ) ( [ ) ( with time-varying coefficients (nonlinear coupling):

39 Swarm Performance Metric C(k) = p g2 N s (k-t ca )N g (k-t ca ) C t Collaboration rate: # of sticks per time unit (k) T e = k= 0 C( k) T e : mean # of collaborations at iteration k : mean collaboration rate over T e

40 Sample Results Webots (10 runs), microscopic (100 runs), macroscopic model (1 run)

41 Simplified Macroscopic Model (1) Τ i,τ a,τ d,τ c << Τ g Τ i =Τ a =Τ d =Τ c =0

42 Simplified Macroscopic Model (2) Nonlinear DE coupling through unit-to-unit interaction (in this case through the stick)! Search successful unsuccessful Grip N s (k+1) = N s (k) p g1 [M 0 N g (k)]n s (k) + p g2 N g (k)n s (k) + p g1 [M 0 N g (k-τ g )]Γ(k;0)N s (k-t g ) N g (k+1) = Γ k ( ;0) = [1 pg 2 j= k T g N 0 N s (k+1) k N ( j)] Initial conditions and causality N s (0) = N 0, N g (0) = 0 N s (k) = N g (k) = 0 for all k<0 s N s = average # robots in searching mode N g = average # robots in gripping mode N 0 = # robots used in the experiment M 0 = # sticks used in the experiment Γ = fraction of robots that abandon pulling T e = maximal number of iterations k = 0,1, T e (iteration index)

43 Steady State Analysis (Simplified Model) Steady-state analysis It can be demonstrated that: T opt g for N M R with N 0 = number of robots and M 0 = number of sticks, approaching angle for collaboration R g g approaching angle for collaboration Counterintuitive conclusion: an optimal T g can exist also in scenarios with more robots than sticks if the collaboration is very difficult (i.e. R g very small)!

44 Verification of Analysis Conclusions (Full Model) 20 robots and 16 sticks (optimal T g ) Example: ~ 1 R g = R g 10 (collaboration very difficult)

45 Optimal Gripping Time opt T g Steady-state analysis can be computed analytically in the simplified model (numerically approximated value): T opt g = ln(1 p 1 g1 R g N 2 1 ln ) β (1 + R 2 β 1 2 with β = N 0 /M 0 = ratio robots-to-sticks 0 g ) for β β c = 2 1+ R g opt T g 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]

46 Journal Publications Stick Pulling Li, Martinoli, Abu-Mostafa, Adaptive Behavior, > learning + micro Martinoli, Easton, Agassounon, Int. J. of Robotics Res., > real + realistic + micro + macro Lerman, Galstyan, Martinoli, Ijspeert, Artificial Life, > realistic + macro Ijspeert, Martinoli, Billard, and Gambardella, Auton. Robots, > real + realistic + micro Object Aggregation Agassounon, Martinoli, Easton, Autonomous Robots, > realistic + macro + activity regulation Martinoli, Ijspeert, Mondada, Robotics and Autonomous Systems -> real + realistic + micro

47 Some Limitations of the current Methods

48 Model Calibration - Practice Bin distribution of interaction time T a (mean T a = 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

49 Model Calibration - Practice Encountering probability p a : example of transition in space from search to obstacle avoidance (1 moving Alice, 1 dummy Alice, Webots measurements, egocentric) Distal controller (rule-based) Proximal controller (Braitenberg, linear)

50 Stochastic vs. Deterministic Models Webots (10 runs), microscopic (100 runs), macroscopic model (1 run)

51 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) Unfolded turbine, blade geometry reproduced faithfully Spatial models required because: environmental template fast performance metrics (e.g. time to completion) clustered dropping point for robots networking connectivity algorithms with enhanced navigation 2000 Time to Completion Number of robots

52 Machine-Learning-Based Approach (main focus: synthesis)

53 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 codesign, multi-objective) Enhanced noise-resistance (e.g., aggregation criteria, statistical tests) Customization for distributed platforms (off-line and online learning; solutions to the credit assignment problem) Combined with one or more levels of simulation

54 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)

55 Learning to Avoid Obstacles by Shaping a Neural Network Controller using Genetic Algorithms

56 Evolving a Neural Controller N i f(x i ) w ij i synaptic weight f ( x) 2 1 I j input O i output neuron N with sigmoid transfer function f(x) O = x i = = x m f j= 1 ( x i 1+ e w ij I ) j + I 0 S 1 S 2 S 3 S 4 S 5 S 8 S 7 S 6 M 1 M 2 inhibitory conn. 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

57 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).

58 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

59 Evolving Obstacle Avoidance Evolved path Fitness evolution

60 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)

61 From Single to Multi-Unit Systems: Co-Learning in a Shared World

62 Evolution in Collective Scenarios Collective: fitness become noisy due to partial perception, independent parallel actions

63 Credit Assignment Problem With limited communication, no communication at all, or partial perception:

64 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

65 Co-Learning Competitive Behavior fitness f 1 fitness f 2

66 Learning to Avoid Obstacle using Noise-Resistant Algorithms (Example 1 of the Combined Method, realistic level with GA and PSO)

67 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

68 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

69 GA PSO

70 A Systematic Study on Obstacle Avoidance 3 Different Scenarios 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 PSO, 50 iterations, scenario 3 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!

71 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

72 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 Best Controllers

73 Results Average of Final Population Fair test: idem as previous slide

74 Learning to Pull Sticks (Example 2 of the Combined Method, microscopic level with in-line adaptive search)

75 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

76 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

77 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

78 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

79 Sample Results Homogeneous System Short averaging window (filter cut-off f high) Long averaging window (filter cut-off f low) robots robots Stick pulling rate (1/min) robots 4 robots 3 robots Stick pulling rate (1/min) robots 4 robots 3 robots robots robots Initial gripping time parameter (sec) Systematic (mean only) Learned (mean + std dev) Initial gripping time parameter (sec) Note: 1 parameter for the whole group!

80 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

81 Impact of Diversity on Performance (Li, Martinoli, Abu-Mostafa, 2004) caste, Global Heterogeneous, Global Heterogeneous, Local Notes: global = group local = individual Stick pulling rate ratio Number of robots Specialized teams Homogeneous teams (baseline) Performance ratio between heterogeneous (full and 2- castes) and homogeneous groups AFTER learning

82 Diversity Metrics (Balch 1998) Entropy-based diversity measure introduced in AB-04 could be used for analyzing threshold distributions Simple entropy: Social entropy: p i = portion of the agents in cluster i; m cluster in total; h = taxonomic level parameter

83 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)

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

85 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 )

86 From Robots to other Embedded, Distributed, Real- Time Systems

87 Embedded, Real-Time SI-Systems Symbiotic societies Traffic systems Social insects Networks of S&A? Vertebrates Pedestrians Multi-robot systems

88 Embedded, Real-Time SI-Systems: Common Features Real-world systems (noise, small heterogeneities, ) From a few to millions of units (but not 10 23!) Embodiment, sensors, actuators, often mobility and energy limitations Local intelligence, behavioral rules, autonomous units Local interaction, communication (unit-to-unit, unit-to-environment)

89 S s S s S s Collaborative Decision in Sensor Networks S a S a S a [Cianci et al., in preparation] Macroscopic: rate equations, mean field approach, whole network Microscopic 1: spatial 2D montecarlo simulation, multi-agent models, 1 agent = 1 node Microscopic 2: nonspatial 1D montecarlo simulation, multi-agent models, 1 agent = 1 node Realistic: intra-node details and communication channel reproduced faithfully (Webots with Omnet++ plugin) Physical reality: detailed info on sensor nodes available Experimental time Abstraction Common metrics

90 Leurre: Mixed Insect-Robot Societies [Correll et al., IROS-06; ALife J. in preparation] Nk ( + 1) = Nk () + pnkjp () ( j 1) N () k p () jnk () join join j j r s j 1 j leave leave p () j Nj() k j+ p ( j+ 1) Nj+ 1() k j S s S s S s S a S a S a Macroscopic: rate equations, mean field approach, whole swarm Microscopic: multi-agent models, 1 agent = 1 robot or cockroach; similar description for all nodes Realistic: intra-robot details, environment (e.g., shelter, arena) details reproduced faithfully; cockroaches: body volume + animation Physical reality: detailed info on robots; limited info on physiology of cockroaches, individual behavior measurable externally Experimental time Abstraction Common metrics

91 Supra-Molecular Chemical System [Mermoud et al., 2006, in preparation] S s S s S s S a S a S a 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) Microscopic 1: Agent-Based model, molecules geometry abstracted, 1 agent = 1 aggregate Abstraction Common metrics Microscopic 2: Agent-Based model, molecules 2D- and 3D geometry captured, 1 agent = 1 aggregate Physical reality: microscopic (e.g., crystallography) and macroscopic measurements (chemical reaction) Experimental time

92 SAILS: 3D Self-Assembling Blimps [Nembrini et al., IEEE-SIS, GA, 2005] TBD Macroscopic: rate equations, mean field approach, whole swarm? Microscopic: multi-agent models, 1 agent = 1 blimp; trajectory maintained, visualization with Webots Realistic: intra-robot details, environment simplified (no realistic fluid dynamics yet) Physical reality: detailed info on robots Experimental time Abstraction Common metrics

93 Conclusions

94 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

95 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)

96 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, Balch T. and Parker L. E. (Eds.), Robot teams: From diversity to polymorphism, Natick, MA: A K Peters, Journal special issues: Ant Robotics, 2001, Annuals of Mathematics and Artificial Intelligence Swarm Robotics, 2004, Autonomous Robots

97 Some Pointers for Swarm Robotics (2) Projects and further pointers in addition to SWIS activities: SwarmBot (next tutorial): I-Swarm: Leurre: BORG group at Georgia Tech: Rus robotics group at MIT: RESL at USC: IASL at UWE: Robotics at Essex: Race at Uni Tokyo: Fukuda s laboratory: Swarm robotics we page (by E. Sahin):