Springer Tracts in Advanced Robotics Volume 68 Editors:BrunoSiciliano·OussamaKhatib·FransGroen David Hsu, Volkan Isler, Jean-Claude Latombe, Ming C. Lin (Eds.) Algorithmic Foundations of Robotics IX Selected Contributions of the Ninth International Workshop on the Algorithmic Foundations of Robotics ABC ProfessorBrunoSiciliano,DipartimentodiInformaticaeSistemistica,UniversitàdiNapoliFedericoII, ViaClaudio21,80125Napoli,Italy,E-mail:[email protected] ProfessorOussamaKhatib,ArtificialIntelligenceLaboratory,DepartmentofComputerScience, StanfordUniversity,Stanford,CA94305-9010,USA,E-mail:[email protected] ProfessorFransGroen,DepartmentofComputerScience,UniversiteitvanAmsterdam,Kruislaan403, 1098SJAmsterdam,TheNetherlands,E-mail:[email protected] Editors Dr.DavidHsu Dr.Jean-ClaudeLatombe NationalUniversityofSingapore StanfordUniversity SchoolofComputing ComputerScienceDepartment 13ComputingDrive Stanford,CA94305-9010,USA Singapore117417 [email protected] [email protected] http://robotics.stanford.edu/∼latombe/ http://www.comp.nus.edu.sg/∼dyhsu Dr.MingC.Lin Dr.VolkanIsler UniversityofNorthCarolina,ChapelHill UniversityofMinnesota DepartmentofComputerScience DepartmentofComputerScience 254BrooksBuilding 200UnionStreetSE ChapelHill,NC27599,USA Minneapolis,MN55455,USA [email protected] [email protected] http://www.cs.unc.edu/∼lin/ http://www-users.cs.umn.edu/∼isler/ ISBN978-3-642-17451-3 e-ISBN978-3-642-17452-0 DOI 10.1007/978-3-642-17452-0 SpringerTractsinAdvancedRobotics ISSN1610-7438 (cid:2)c 2010Springer-VerlagBerlinHeidelberg Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis concerned,specificallytherightsoftranslation,reprinting,reuseofillustrations,recitation,broadcasting, reproductiononmicrofilmorinanyotherway,andstorageindatabanks.Duplicationofthispublication orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember 9, 1965,initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violations areliableforprosecutionundertheGermanCopyrightLaw. 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Printedonacid-freepaper 543210 springer.com EditorialAdvisoryBoard OliverBrock,TUBerlin,Germany HermanBruyninckx,KULeuven,Belgium RajaChatila,LAAS,France HenrikChristensen,GeorgiaTech,USA PeterCorke,QueenslandUniv.Technology,Australia PaoloDario,ScuolaS.AnnaPisa,Italy RüdigerDillmann,Univ.Karlsruhe,Germany KenGoldberg,UCBerkeley,USA JohnHollerbach,Univ.Utah,USA MakotoKaneko,OsakaUniv.,Japan LydiaKavraki,RiceUniv.,USA VijayKumar,Univ.Pennsylvania,USA SukhanLee,SungkyunkwanUniv.,Korea FrankPark,SeoulNationalUniv.,Korea TimSalcudean,Univ.BritishColumbia,Canada RolandSiegwart,ETHZurich,Switzerland GauravSukhatme,Univ.SouthernCalifornia,USA SebastianThrun,StanfordUniv.,USA YangshengXu,ChineseUniv.HongKong,PRC Shin’ichiYuta,TsukubaUniv.,Japan N SdeTrAtRhe(aSupsrpinicgeesrTofraEcUtsRinOANd(vEaunrcoepdeRanobRootbicost)ichsasRbeeseeanrpcrhoNmeottwedorukn)- RERuersoOepaeracBnhROO************TICS NetworkU E Foreword By the dawn of the new millennium, robotics has undergone a major transforma- tion in scope and dimensions. This expansion has been brought about by the ma- turity of the field and the advances in its related technologies. From a largely dominant industrial focus, robotics has been rapidly expanding into the challenges of the human world (human-centered and life-like robotics). The new generation of robots is expected to safely and dependably interact and work with humans in homes, workplaces, and communities providing support in services, entertainment, education, exploration, healthcare, manufacturing, and assistance. Beyond its impact on physical robots, the body of knowledge that robotics has produced is revealing a much wider range of applications reaching across diverse research areas and scientific disciplines, such as: biomechanics, haptics, neurosci- ences, and virtual simulation, animation, surgery, and sensor networks among oth- ers. In return, the challenges of the new emerging areas are proving an abundant source of stimulation and insights for the field of robotics. It is indeed at the inter- section of disciplines where the most striking advances happen. The Springer Tracts in Advanced Robotics (STAR) is devoted to bringing to the research community the latest advances in the robotics field on the basis of their significance and quality. Through a wide and timely dissemination of critical research developments in robotics, our objective with this series is to promote more exchanges and collaborations among the researchers in the community and contribute to further advancements in this rapidly growing field. Since its inception in 1994, the biennial Workshop Algorithmic Foundations of Robotics (WAFR) has established some of the field's most fundamental and lasting contributions. Since the launching of STAR, WAFR and several other thematic symposia in robotics find an important platform for closer links and extended reach within the robotics community. This volume is the outcome of the WAFR ninth edition and is edited by D. Hsu, V. Isler, J.-C. Latombe and M.C. Lin. The book offers a collection of a wide range of topics in advanced robotics, including motion planning, multiagents, modular and reconfigurable robots, localization and mapping, grasping, and sensing. The contents of the twenty-four contributions represent a cross-section of the current state of research from one particular aspect: algorithms, and how they are inspired by classical disciplines, such as discrete and computational geometry, differential geometry, mechanics, optimization, operations research, computer VIII Foreword science, probability and statistics, and information theory. Validation of algo- rithms, design concepts, or techniques is the common thread running through this focused collection. Rich by topics and authoritative contributors, WAFR culminates with this unique reference on the current developments and new directions in the field of algorithmic foundations. A very fine addition to the series! Naples, Italy Bruno Siciliano October 2010 STAR Editor Preface Robot algorithms are a fundamental build block of robotic systems. They enable robots to perceive, plan, control, and learn, in order to achieve greater autonomy. Today, the design and analysis of robot algorithms are more crucial than ever for at least two reasons: • Robotics is undergoing major transformation. Originally focused on indus- trial manufacturing, it is now rapidly expanding into new domains, such as homes and offices, elderly care, medical surgery, entertainment, ocean and space exploration, and search-and-rescue missions. In these new domains, tasks are less repetitive, environments are less structured, events are more unpredictable, and greater autonomy is required over long periods of time. It is often impossible to anticipate all events explicitly and to program the robots specifically to handle them. New algorithms that are adaptive to environment uncertainties and changes are needed to conquer these challenges. • Robot algorithms are finding new applications beyond robotics, for exam- ple, in designing mechanical assemblies, modeling molecular motion, creat- ing digital characters for video games and computer-generated movies, ar- chitectural simulation, and ergonomic studies. These non-traditional applications of robot algorithms pose new challenges: hundreds or thou- sands of degrees of freedom, large crowds of characters, complex physical constraints, and natural-looking motions. The resulting new algorithms may in turn benefit future robots. Robot algorithms are also rapidly evolving as a result of new technologies, e.g., low-cost parallel computers, cheaper and more diverse sensors, and new interac- tion technologies ranging from haptic to neuroprosthetic devices. Unlike traditional computer algorithms, robot algorithms interact with the physical world. They must operate safely, reliably, and efficiently under tight time constraints in imperfectly known environments. So, it is not surprising that the de- sign and analysis of robot algorithms raise unique combinations of fundamental questions in computer science, electrical engineering, mechanical engineering, and mathematics. For example, minimalist robotics studies the minimal sensing and actuation capabilities required for robots to complete a given task. It addresses not only computational complexity issues, but also “physical” complexity issues. Probabilistic methods are widely used as a modeling tool to handle uncertainties due to sensing and actuation noise, but they are also used as a computational tool
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