Swarm robotics. Ivana Budinská Ústav informatiky SAV
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1 Swarm robotics Ivana Budinská Ústav informatiky SAV Seminár z UI,
2 Obsah prezentácie Motivácia Definície problémov Zaujímavé projekty Algoritmy Sumarizácia a nové úlohy Modifikovaný PSO algoritmus pre kooperatívne prehľadávanie (M.Masár)
3 Motivácia Lacnejšie riešenie robotické vysávače Vzdialené centrálne riadenie správa na Mars ide 15 min Sťažená komunikácia s centrálou prehľadávanie území po požiaroch, zemetraseniach a iných prírodných katastrofách Autonómna navigácia, Samoopravovanie
4 Obrázky prevzaté z internetu Inšpirácie z prírody
5 Definície problémov Koordinované prehľadávanie neznámych priestorov napr. pri odmíňovaní, vyhľadávaní zranených po katastrofách, upratovanie veľkých priestorov, zber úrody, monitorovanie priestorov Zhlukovanie a transport Koordinácia pri premiestňovaní predmetov box pushing, záchrana zranenených Rekonfigurácia modulárne robotické systémy, ktoré vytvoria zložitejšie systémy
6 Vlastnosti Množstvo jednoduchých robotov Homogénne zloženie Individuálne schopnosti Jednoduché pravidlá správania Lokálne informácie Globálny cieľ Technologické vlastnosti: Robustnosť Škálovateľnosť Flexibilita Samoorganizácia Nízka cena
7 Zaujímavé projekty COMSTAR ( ), C-MANTIC Lab Automatické rozpoznávanie cieľa MASSES ( , ) Multi-robotický systém pre skúmanie povrchu mesiaca a Marsu COMRADES ( ) Multi-robotický systém pre utomatickú detekciu pozemných mín SWARMANOID ( ) 7RP FET Asi 60 autonómnych robotov, tri typy: eye-bots,handbots, and foot-bots.
8 Problémy a algoritmy Koordinované prehľadávanie neznámych priestorov Zhlukovanie a transport Rekonfigurácia modulárne robotické systémy, ktoré vytvoria zložitejšie systémy
9 Problémy a algoritmy Koordinované prehľadávanie neznámych priestorov Zhlukovanie a transport Rekonfigurácia modulárne robotické systémy, ktoré vytvoria zložitejšie systémy
10 Koordinované prehľadávanie Súvisiace problémy: Mapovanie a lokalizácia Určovanie hraníc Hľadanie cieľa priestorov Vlastnosti algoritmov: Jednoduché Škálovateľné Distribuované Využívanie lokálnych interakcií
11 Definícia problému Určitý počet robotov, každý má akčný rádius pohyb v ďalšom kroku, komunikačný rádius a rádius pokrytia senzormi. Definujeme funkciu pokrytia ako transformáciu akcie robota v čase v určitom mieste na určitú priestorovú jednotku f: a t r x l t r c t r Snažíme sa minimalizovať prekrývanie a maximalizovať pokrytie (Zjednotenie a prienik priestorových jednotiek c t r )
12 Agentový prístup Každý robot koodinuje svoj postup s robotmi v okolí, aby minimalizoval prekrývanie a maximalizoval pokrytie Ak sa v okolí robota nenachádza iný robot vykonaj náhodný pohyb Ak sa v okolí pokrytia nenachádza robot, ale v komunikačnom okolí áno použije niektorý algoritmus na určenie nasledujúceho kroku, aby sa nevzdialil z komunikačného rádia všetkých robotov (maximalizácia pokrytia) Ak sa v okolí pokrytia aj v komunikačnom nachádzajú roboty vypočítava váženú vzdialenosť od robotv a akciu určuje s ohľadom na minimalizáciu prekrývania.
13 Detekcia hraníc, ohraničení J. McLurkin: irobot projekt BoundaryDetection.php Rovnomerná disperzia (udržiavanie vzdialenosti medzi jednotlivými robotmi) Detekcia hraníc robot sa môže nachádzať v stave Wall node, Frontier node, Interior node generuje akciu. Wall node a Frontier node sú pevné.
14 Boundary algoritmus riadená disperzia (vedie roboty do nepreskúmanej oblasti) geometriu lokálnej siete každý robot tvorí vrchol siete, rekonfiguráciou siete sa približuje k hraniciam
15 Multi-robot mapping and localization algoritm Rothermich et al. rieši problém s nepresnou odometriou alokalizáciou Mobilné roboty slúžia ko mobilné majáky, ich úloha v rámci roja sa mení Všetky roboty musia používať referenčné súradnice globálneho prostredia Výhody mapa sa vytvára z rôznych pozícií uhly a hranice sú zachytené presnejšie V neznámom prostredí je lokalizácia jednoho robota veľmi obtiažna
16 Problémy a algoritmy Koordinované prehľadávanie neznámych priestorov Zhlukovanie a transport Rekonfigurácia modulárne robotické systémy, ktoré vytvoria zložitejšie systémy
17 Mobilné formácie - zhluky Hetiarachchi and Spears založené na príťažlivosti a odpudivosti simuláciou ukázali vhodnosť použitia Leonard-Jonsonových potenciálov Udržiavanie formácie pri prekonávaní prekážok
18 Mobilné formácie - zhluky
19 e-puck cluster
20 Obchádzanie dier Kooperatívne obchádzanie dier trakčný senzor na zistenie, kam chce ísť susedný robot Evolučné algoritmy, učenie Nevýhody nepoužiteľné v praxi
21 Reťazenie Nouyan et al., 2009 Stavový automat search, explore,chain,finish Farebná signalizácia: Blue, Green, Yellow
22 Reťazenie a transport Stavový diagram 4 nové stavy: Assemble, Transport target, Transport blind, Reset
23
24 Príklad: Vyhľadaj a zachráň
25 Koordinovaný transport Typická cesta dvoch nekomunikujúcich robotov. Roboty nesynchronizovali svoju prácu. V prvom prípade obidva roboty opustili box a išli za svetlom, v druhom prípade jeden robot tlačil box ale mimo územia a druhý šiel za svetlom, v treťom prípade oba roboty tlačili box úspešne do cieľa, v štvrtom prípade jeden robot prestal tlačiť box a prekrížil cestu druhého robota. - box pushing
26 Problémy a algoritmy Koordinované prehľadávanie neznámych priestorov Zhlukovanie a transport Rekonfigurácia modulárne robotické systémy, ktoré vytvoria zložitejšie systémy
27 Rekonfigurovateľné roboty Modulárne robotické systémy, ktoré vytvárajú vyššie organizmy s cieľom vykonávať rôzne úlohy Inšpirácie: bunky (cellular automata) zhluky, roje, atď. (swarm intelligence) Rekonfigurovateľné roboty typu reťazec Rekonfigurovateľné roboty typu mriežka Mobilné rekonfigurovateľné roboty Iné typy rekonfigurovateľných robotov
28 Kooperácia mobilných jednotiek pri prekonávaní prekážok Swarm bots - Marco Dorigo
29 Atron rekonfigurovateľné robotické moduly D.J.Christensen USD modular robotics research lab, modular.mmmi.sdk.dk
30 Atron príklad rekonfigurovateľného systému
31 Atron reconfigurable robotic modules
32 Atron reconfigurable robotic modules
33 Atron reconfigurable robotic modules
34 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
35 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
36 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
37 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
38 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
39 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
40 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
41 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
42 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
43 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
44 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
45 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
46 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
47 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
48 Rekonfigurovateľné moduly - Lego Desmond Innovation, LLC
49 Záver Swarm inteligencia Kľúčové vlastnosti a techonologické motivácie Aplikačné domény Vytváranie koalícií pri homogénnych skupinách a pri nehomogénnych skupinách Problémy pri realizácii a nasadzovaní technológií do praxe Vývoj senzorov a aktuátorov
50 Referencie Prezentácia Swarm Robotics by Roderich Gross D. Miner: Swarm robotics algorithms: A survay, 2007 J. Alexander Fax, Richard M. Murray: Information Flow and Cooperative Control of Vehicle Formation, IEEE T. Automatic Control, 2003, murray/papers/2003g_fm03-tac.html A.Farinelli, L. Iocchi, D. Nardi: Multi robot systems: A classification focused on coordination, IEEE Transactions on Man Systems and Cybernetics, par B, pp , 2004 Lynne E. Parker: Current State of the Art in Distributed Autonomous Mobile Robotics Yan Meng: Q-Learning Afjusted Bio-Inspired Multi-Robot Coordination, In Recent Advances in Multi Robot systems, Book edited by Aleksandar Lazinica, ISBN Pp 326, May 2008, itech Education and Publishing, Vienna, Austria. W. Liu. Design and modelling of Adaptive Foraging in Swarm Robotic Systems. PhD thesis, Univeristy of the West of England, Bristol, UK D. Antonic, Ž. Ban, M. Žagar: Deming robots - requirements and constraints, 2008 Rothermich, J., Ecemis, M., and Gaudiano, P. Distributed localization and mapping with a robotic swarm. Swarm Robotics, Springer-Verlag (2004), Trianni, V., Nolfi, S., and Dorigo, M. Cooperative hole avoidance in a swarm-bot. Robotics and Autonomous Systems 54, 2 (2006), Hettiarachchi, S., and Spears, W. Moving swarm formations through obstacle fields. International Conference on Artificial Intelligence (2005).
51 Videá, odkazy
52 Modifikovaný PSO algoritmus pre kooperatívne prehľadávnie priestoru (M. Masár) Modré pole označuje oblasť videnia, zelené pole je vyhodnocovná oblasť fitnes funkcia. Každá častica vyhodnocuje, aký nepreskúmaný priestor sa nachádza v jej okolí. Sociálny aspekt častice sú priťahované k tým časticiam, ktoré majú viac nepreskúmaného priestoru v svojom okolí. - particle
53 Výpočet rýchlosti w váha obmedzjúca rýchlosť častice, s časom klesá p id - vektor označujúci pozíciu častice, ktorá má najvyššiu fitnes zo všetkých pozícií, kde sa častica nachádzala p gd vektor najlepších p id zo všetkých častíc Fitnes funkcia ohodnocuje množstvo nepreskúmaného priestoru okolo častice čím viac, tým lepšie
54 Výsledky simulácií v Matlabe
55 Výsledky simulácií v Matlabe
56 Modifikácia PSO II Rozšírenie rovnice o člen označujúci ťažisko nepreskúmaného priestoru. Častica je priťahovaná k tomuto priestoru Metóda nehľadá najlepšie riešenie v čase, ale v priestore
57 Záver Algoritmus predstavuje jednoduché pravidlá v správaní častíc, ktoré vedú ku komplexnému správaniu sa celku Metóda je modifikovaná pre potreby kooperatívneho prehľadávania priestoru pohyb častíc je modifikovaný v nadväznosti na tvar nepreskúmaného územia a častice sú priťahované k nepreskúmaným oblastiam.
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