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 http://swis.epfl.ch/ [email protected] Tutorial at ANTS-06, Bruxelles, September 4, 2006 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 Background: Mobile robotics An Example of Mobile Robot: 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 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) Swarm-Intelligent Systems: Features • Bio-inspirationBeyond bio-inspiration: combine natural principles– social insect with societiesengineering 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 • 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) Multi-Level Modeling Methodology dN (t) n ′ ′ ′ = ∑∑W (n | n ,t)Nn (t) − W (n | n,t)Nn (t) dt nn′′ S S s a Macroscopic: rate equations, mean field approach, whole swarm Ss Sa Microscopic: multi-agent models, Ss Sa Ss Sa only relevant robot feature captured, 1 agent = 1 robot Abstraction Common metrics 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 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