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Published on January 3, 2008

Author: Arundel0

Source: authorstream.com

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Multiagent Control of Modular Self-Reconfigurable Robots:  Multiagent Control of Modular Self-Reconfigurable Robots Tad Hogg HP Labs Hristo Bojinov Jeremy Kubica Arancha Casal PARC’s modular robotics group topics:  topics modular robots multi-agent control results modular robots:  modular robots collections of modules each module is a robot self-reconfigurable modules can change connections so overall robot changes shape “modular self-reconfigurable” robots MSR why change shape?:  why change shape? adjust shape to task e.g., locomotion wheel, spider, snake, … e.g., manipulation match “finger” size to object size topics:  topics modular robots Proteo Prismatic future possibilities multi-agent control results Proteo:  Proteo rhombic dodecahedron space filling Proteo:  Proteo modules move over neighbors each edge of cube is a diagonal of RD face topics:  topics modular robots Proteo Prismatic future possibilities multi-agent control results prismatic MSR robots:  prismatic MSR robots modules connect via arms extending arms moves neighbors examples Crystalline robot (Dartmouth) moves in 2 dimensions TeleCube (PARC) moves in 3 dimensions TeleCube:  TeleCube cubes 6 independent arms 2:1 length ratio neighbors cooperate to move:  neighbors cooperate to move topics:  topics modular robots Proteo Prismatic future possibilities multi-agent control results devices for “smart matter”:  devices for “smart matter” micro-electomechanical (MEMS) bacteria molecular quantum sensor + computer + actuator micromachines (MEMS):  micromachines (MEMS) made with photolithography e.g., programmable force fields (open loop) hard to assemble biological machines:  biological machines biotechnology: program bacteria e.g., T. Knight, R. Weiss at MIT AI Lab limited abilities programs for bacteria:  programs for bacteria gene regulatory networks engineered changes give some program control over behavior molecular machines:  molecular machines ribosomes: make proteins in cells protein motors move material in cells ATP synthase rotor size: 10nm DNA mRNA protein See Nature, 386, 299 (1997) molecular machines:  molecular machines carbon nanotubes and buckyballs strong, light, flexible, electronic devices easy to make hard to arrange molecular machines:  molecular machines complex molecules for robot parts currently: only theory hard to make hard to assemble potential: cheap, fast, strong parts example medical applications: R. Freitas, Jr., Nanomedicine, 1999 example designs: E. Drexler, R. Merkle, A. Globus quantum computers:  quantum computers potential: much faster algorithms e.g., factoring very difficult to build amplitudes while solving a 10-variable 3-SAT instance with 3 solutions quantum search heuristic Java demo: www.hpl.hp.com/shl/projects/quantum/demo quantum machines:  quantum machines potential: detail control over materials e.g., interfere two ways to absorb light => transparent very difficult to build See T. Hogg and G. Chase, Quantum smart matter, 1996 www.arxiv.org/abs/quant-ph/9611021 S. Lloyd and L. Viola, Control of open quantum systems dynamics, 2000 www.arxiv.org/abs/quant-ph/0008101 quantum machines:  quantum machines example: coin weighing puzzle quantum sensor finds bad coin in single try See B. Terhal, J. Smolin, Single quantum querying of a database, 1997 www.arxiv.org/abs/quant-ph/9705041 devices: summary:  devices: summary smaller devices harder to make harder to connect, assemble greater potential capability but need many, cheap devices statistical or systems view challenge: How to build?:  challenge: How to build? physical/engineering constraints unreliable parts misconnected limits early technology economics build cheaply challenge: How to use?:  challenge: How to use? information/computational constraints limited, changing info from environment computational complexity e.g., planning optimal device use limits even mature technology topics:  topics modular robots multi-agent control results control challenge:  control challenge coordinate many modules sensor & actuator errors decompose programming task to only need local info (small scale) high-level task description (large scale) e.g., grasp object of unspecified shape cf., H. Simon: nearly decomposable systems control before hardware?:  control before hardware? many, small modules don’t yet exist hence, hardware details unknown but can study general issues control may simplify hardware design e.g., manage in spite of defects identify compute/communicate tradeoffs sensor + computer + actuator physics vs. size:  physics vs. size gravity friction Brownian motion thermal noise decoherence faster smaller harder to build MEMS molecular quantum multi-agent control:  multi-agent control matches control to physics different agents for each scale matches control to available info rapid response to local info manager agents: overall coordination without need for details motivation: biology:  motivation: biology social insects, multicellular organisms, ecology reliable behavior from unreliable parts cf. incentive issues noncooperative agents economics, common law, … examples termite mounds embryo growth motivation: teams:  motivation: teams robot soccer insect-like robot teams e.g., foraging MSR robots have tighter physical constraints direct access to neighbor locations e.g., no need for vision to find neighbors topics:  topics modular robots multi-agent control results computational ecology Proteo Telecube computational ecology:  computational ecology dynamical behavior of simple agents asynchronous, local decisions delays, imperfect information “mean-field” statistical theory see B. Huberman, The Ecology of Computation, 1988 apply to actual robot behaviors see K. Lerman et al. in Artificial Life, 2001 techniques:  techniques finite-state machine for each module simple script, some randomness local communication create gradients through structure “scents” topics:  topics modular robots multi-agent control results computational ecology Proteo Telecube example: growing a chain:  example: growing a chain modes: SLEEP, SEARCH(red), SEED(yellow), FINAL(white) initially: all in SLEEP, randomly pick one SEED seed: picks growth direction emits scent attracts modules growing a chain:  growing a chain SLEEP SEARCH SEED FINAL detect scent descend gradient + propagate scent emit scent=0 if neighbor is seed propagate scent if neighbor became seed scent:  scent set S=min(neighbors)+1 move around neighbor until lower value found if seed found: become new seed SEED S = 0 S-1 S S+1 structures:  structures recursive branching multilevel arms grow around object using contact sensors See H. Bojinov et al., Multiagent Control of Self-reconfigurable Robots, 2000 www.arxiv.org/abs/cs.RO/0006030 topics:  topics modular robots multi-agent control results computational ecology Proteo Telecube locomotion:  locomotion make snake shape move toward goal barrier follow wall find gap higher-level control: general direction building on low-level agent behavior see: Kubica et al, Proc. ICRA 2001 object manipulation:  object manipulation exert forces to move object based on contact with object “scent” recruits other modules modules on surface form rigid shell summary:  summary simple agents perform basic tasks reconfiguration locomotion manipulate objects apply to different hardware types Proteo: surface motions TeleCube: internal motions future directions:  future directions quantify capabilities design more complex behaviors implement on hardware quantify capabilities:  quantify capabilities examples of agent-based control are only specific instances quantify how robust? how accurate? what cost? e.g., power use agent design:  agent design combine with higher-level agents e.g., switch among low-level behaviors automate agent design e.g., genetic algorithms (FXPAL) test on hardware:  test on hardware various existing robots few, fairly large modules large number of tiny modules don’t yet exist wait for hardware vs. simulate? understand likely hardware capabilities e.g., MEMS, … conclusions:  conclusions agent-based control for MSR robots gives robust low-level behaviors simplifies higher-level task control biological system models suggest module rules useful even if not biologically accurate www.hpl.hp.com/shl/people/tad

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