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Autonomous Agents and Multi-Agent Systems, 4:187–231.",{"doi":745},"10.1023\u002FA:1011443827037",{"id":22,"text":747,"url":22,"identifiers":748},"Simmons, R. 1994. Structured control for autonomous robots. IEEE Transactions on Robotics and Automation, 10(1):34–43.",{"doi":749},"10.1109\u002F70.285583",{"id":22,"text":751,"url":22,"identifiers":752},"Simmons, R. 2004. Inter process communication (IPC). http:\u002F\u002Fwww-2.cs.cmu.edu\u002Fafs\u002Fcs.cmu.edu\u002Fproject\u002FTCA\u002Fwww\u002Fipc\u002F.",{},{"id":22,"text":754,"url":22,"identifiers":755},"Simmons, R., Apfelbaum, D., Fox, D., Goldmann, R., Haigh, K., and Musliner, D. 2000. Coordinated deployment of multiple heterogeneous robots. In Proceedings of the IEEE\u002FRSJ International Conference on Intelligent Robots and Systems (IROS).",{},{"id":22,"text":757,"url":22,"identifiers":758},"Simmons, R., Goldberg, D., Goode, A., Montemerlo, M., Roy, N., and Sellner, B. 2003. GRACE: an autonomous robot for the AAAI robot challenge. AI Mag., 24(2):51–72.",{},{"id":22,"text":760,"url":22,"identifiers":761},"Simplified Wrapper and Interface Generator. 2004. http:\u002F\u002Fwww.swig.org\u002F.",{},{"id":22,"text":763,"url":22,"identifiers":764},"Singh, R. and Sycara, K. 2004. Securing Multi Agent Societies (Tech. Rep. No. CMU-RI-TR-04-02). Robotics Institute, Carnegie Mellon.",{},{"id":22,"text":766,"url":22,"identifiers":767},"Skubic, M. and and Volz, R. A. 1998. Learning force-based assembly skills from human demonstration for execution in unstructured environments. In Proceedings of International Conference on Robotics and Automation (ICRA98), pp. 1281–1288.",{},{"id":22,"text":769,"url":22,"identifiers":770},"Sloman, A. 1998. What's an AI toolkit for? In B. Logan and J. Baxter (eds.), Proceedings of the AAAI-98 Workshop on Software Tools for Developing Agents, pp. 1–10.",{},{"id":22,"text":772,"url":22,"identifiers":773},"Sloman, A. 2002. Help Poprulebase.",{},{"id":22,"text":775,"url":22,"identifiers":776},"Sloman, A. and Scheutz, M. 2002. A framework for comparing agent architectures. In Proceedings of UK Workshop on Computational Intelligence, pp. 169–176.",{},{"id":22,"text":778,"url":22,"identifiers":779},"SOAP version 1.2. 2003. http:\u002F\u002Fwww.w3.org\u002FTR\u002Fsoap12\u002F. W3C XML Protocol Working Group.",{},{"id":22,"text":781,"url":22,"identifiers":782},"Sprouse, J. 2005. Nomadic.sourceforge.net. http:\u002F\u002Fnomadic.sourceforge.net\u002F.",{},{"id":22,"text":784,"url":22,"identifiers":785},"Steinfeld, A. 2004. Interface lessons for fully and semi-autonomous mobile robots. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA) 2004, Vol. 3, pp. 2752–2757.",{},{"id":22,"text":787,"url":22,"identifiers":788},"Stentz, A. 2002. CD*: A real-time resolution optimal re-planner for globally constrained problems. In Proceedings of AAAI 2002, p. 605.",{},{"id":22,"text":790,"url":22,"identifiers":791},"Sycara, K., Paolucci, M., Velsen, M.V., and Giampapa, J. 2003. The RETSINA MAS infrastructure. Autonomous Agents and Multi-Agent Systems, 7(1):29–48.",{"doi":792},"10.1023\u002FA:1024172719965",{"id":22,"text":794,"url":22,"identifiers":795},"Sycara, K.P. and Zeng, D. 1996. Coordination of multiple intelligent software agents. International Journal of Cooperative Information Systems, 5(2\u002F3):181–212.",{},{"id":22,"text":797,"url":22,"identifiers":798},"Tews, A., Matarić, M., and Sukhatme, G. 2003. A scalable approach to human-robot interaction. In IEEE International Conference on Robotics and Automation, Taipei, Taiwan, pp. 1665–1670.",{},{"id":22,"text":800,"url":22,"identifiers":801},"Thrun, S. 2003. Robotic mapping: A survey. In G. Lakemeyer and B. Nebel (Eds.), Exploring Artificial Intelligence in the New Millennium. Morgan Kaufmann, San Francisco, CA, USA, pp. 1–35.",{},{"id":22,"text":803,"url":22,"identifiers":804},"Thrun, S., Fox, D., Burgard, W., and Dellaert, F. 2000. Robust monte carlo localization for mobile robots. Artificial Intelligence, 128(1–2):99–141.",{},{"id":22,"text":806,"url":22,"identifiers":807},"Utz, H., Kraetzschmar, G., Mayer, G., and Palm, G. 2005. Hierarchical behavior organization. In Proceedings of IROS 2005. Edmonton, Canada.",{"doi":808},"10.1109\u002FIROS.2005.1545581",{"id":22,"text":810,"url":22,"identifiers":811},"Utz, H., Sablatnög, S., Enderle, S., and Kraetzschmar, G. 2002. Miro—middleware for mobile robot applications. IEEE Transactions on Robotics and Automation, Special Issue on Object-Oriented Distributed Control Architectures, 18(4):493–497.",{"doi":812},"10.1109\u002FTRA.2002.802930",{"id":22,"text":814,"url":22,"identifiers":815},"Utz, H., Stulp, F., and Mühlenfeld, A. 2004. Sharing belief in teams of heterogeneous robots. In D. Nardi, M. Riedmiller, and C. Sammut (Eds.), RoboCup-2004: The eighth RoboCup Competitions and Conferences, Springer Verlag.",{"doi":816},"10.1007\u002F978-3-540-32256-6_46",{"id":22,"text":818,"url":22,"identifiers":819},"Valin, J. and Létourneau, D. 2004. Flowdesigner. http:\u002F\u002Fflowdesigner.sourceforge.net\u002F.",{"doi":820},"10.1155\u002FS1110865704408142",{"id":22,"text":822,"url":22,"identifiers":823},"Varakantham, P., Gangwani, S., and Karlapalem, K. 2002. On handling component and transaction failures in multi agent systems. SIGecom Exch, 3(1):32–43.",{"doi":824},"10.1145\u002F844331.844336",{"id":22,"text":826,"url":22,"identifiers":827},"Vaughan, R., Gerkey, B., and Howard, A. 2003. On device abstractions for portable, resuable robot code. In Proceedings of IROS 2003, Las Vegas, Nevada, pp. 2121–2427.",{},{"id":22,"text":829,"url":22,"identifiers":830},"Vijayakumar, S., D'souza, A., Shibata, T., Conradt, J., and Schaal, S. 2002. Statistical learning for humanoid robots. Autonomous Robots, 12(1):55–69.",{"doi":831},"10.1023\u002FA:1013258808932",{"id":22,"text":833,"url":22,"identifiers":834},"Volpe, R., Nesnas, I., Estlin, T., Mutz, D., Petras, R., and Das, H. 2001. The CLARAty architecture for robotic autonomy. In Proceedings of the 2001 IEEE Aerospace Conference.",{},{"id":22,"text":836,"url":22,"identifiers":837},"Walters, D. 2003. Open automation project (OAP). http:\u002F\u002Foap.sourceforge.net\u002F.",{},{"id":22,"text":839,"url":22,"identifiers":840},"Webots 5. 2005. http:\u002F\u002Fwww.cyberbotics.com\u002F. Cyberbotics.",{},{"id":22,"text":842,"url":22,"identifiers":843},"White box robotics. 2005. http:\u002F\u002Fwhiteboxrobotics.com\u002F. White Box Robotics.",{},{"id":22,"text":845,"url":22,"identifiers":846},"Wolf, D. and Sukhatme, G. 2005. Mobile robot simultaneous localization and mapping in dynamic environments. Autonomous Robots, 19(1):53–65.",{"doi":847},"10.1007\u002Fs10514-005-0606-4",false,{"id":850,"createTime":851,"updateTime":852,"relativeEntities":853,"slug":854,"properties":855,"entityType":190,"verifyStatus":191,"verifyTime":866,"verifyNote":193,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":867,"fullTextUrl":22,"authors":868,"publicationType":235,"publisherRelationship":902,"citationCount":23,"citationInfo":950,"publishDate":953,"publishYear":951,"citationAnalyzeStatus":21,"lastCitationAnalyze":954,"indexDatabases":955,"openAccess":22,"references":22,"isForceReanalyzing":848},"d7632c1f-de9c-4386-813b-39797d4bfab3","2023-12-28T09:21:02.626+00:00","2026-07-27T05:40:13.488+00:00",[],"Seafloor-map-generation-for-autonomous-underwater-vehicle-navigation",{"abstract":856,"title":858,"gsPaper":860,"references":862,"doi":864},{"EN":857},"Elevation map generation is an essential component of any autonomous underwater vehicle designed to navigate close to the seafloor because elevation maps are used for obstacle avoidance, path planning and self localization. We present an algorithm for the reconstruction of elevation maps of the seafloor from side-scan sonar backscatter images and sparse bathymetric points co-registered within the image. Given the trajectory for the underwater vehicle, the reconstruction is corrected for the attitude of the side-scan sonar during the image generation process. To perform reconstruction, an arbitrary but computable scattering model is assumed for the seafloor backscatter. The algorithm uses the sparse bathymetric data to generate an initial estimate for the elevation map which is then iteratively refined to fit the backscatter image by minimizing a global error functional. Concurrently, the parameters of the scattering model are determined on a coarse grid in the image by fitting the assumed scattering model to the backscatter data. The reconstruction is corrected for the movement of the sensor by initially doing local reconstructions in sensor coordinates and then transforming the local reconstructions to a global coordinate system using vehicle attitude and performing the reconstruction again. We demonstrate the effectiveness of our algorithm on synthetic and real data sets. Our algorithm is shown to decrease the average elevation error when compared to real bathymetry from 4.6 meters for the initial surface estimate to 1.6 meters for the final surface estimate from a survey taken of the Juan de Fuca Ridge.",{"EN":859},"Seafloor map generation for autonomous underwater vehicle navigation",{"VOID":861},"[\"3400263923677009420\"]",{"VOID":863},"Aleksandrov, A.D., Kolmogorov, A.N., and Lavrent'ev, M.A. 1964. Mathematics: Its Content, Methods and Meaning, MIT Press: Cambridge, MA.\nBaeck, T. and Schwefel, H.P. 1993. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1):1–10.\nBlake, A. and Zisserman, A. 1987. Visual Reconstruction, MIT Press: Cambridge, MA.\nCaruthers, J.W. and Novarini, J.C. 1993. Modeling bistatic bottom scattering strength including a forward scatter lobe. IEEE J. Oceanic Engineering, 18(2):100–107.\nCervenka, P. and de Moustier, C. 1993. Sidescan sonar image processing techniques. IEEE J. Oceanic Engineering, 18(2):108–122.\nClarke, J.H. 1994. Toward remote seafloor classification using the angular response of acoustic backscatter: A case study from multiple overlapping GLORIA data. IEEE J. Oceanic Engineering, 19(1):112–127.\nCobra, D.T., Oppenheim, A.V., and Jaffe, J.S. 1992. Geometric distortions in side-scan sonar images: A procedure for their estimation and correction. IEEE J. Oceanic Engineering, 17(3):252–268.\nCuschieri, J.M. and Hebert, M. 1990. Three-dimensional map generation from side-scan sonar images. L. Energy Resources Technology, 112:96–102.\nDenbigh, P.N. 1989. Swath bathymetry: Principles of operation and analysis of errors. IEEE J. Oceanic Engineering, 14(4):289–298.\nde Moustier, C. and Alexandrou, D. 1991. Angular dependence of 12-kHz seafloor acoustic backscatter. J. Acoustical Society of America, 90:522–531.\nElfes, A. 1987. Sonar-based real world mapping and navigation. IEEE J. Robotics and Automation, RA-3(3):249–265.\nGensane, M. 1989. A statistical study of acoustic signals backscattered from the sea bottom. IEEE J. Oceanic Engineering, 14(1):84–93.\nHebert, M. 1989. Terrain modeling for autonomous underwater navigation. In Proc. Unmanned Untethered Submersible Technology Conf., pp. 502–511.\nHorn, B.K.P. 1986. Robot Vision. MIT Press: Cambridge, MA.\nHorn, B.K.P. and Brooks, M.J. 1988. The variational approach to shape from shading. Computer Vision, Graphics and Image Processing, 33(2):174–208.\nJackson, D.R., Winebrenner, D.P., and Ishimaru, A. 1986. Application of the composite roughness model to high-frequency bottom backscattering. J. Acoustical Society of America, 79:1410–1422.\nJohnson, A.E. 1993. Incorporating different reflection models into surface reconstruction. In Proc. Unmanned Untethered Submersible Technology Conf., pp. 446–459.\nLanger, D. and Hebert, M. 1991. Building qualitative elevation maps from underwater sonar data for autonomous underwater navigation. In Proc. IEEE Int. Conf. Robotics and Automation, pp. 2478–2483.\nLeonard, J.J. and Durrant-Whyte, H.F. 1992. Directed Sonar Sensing for Mobile Robot Navigation, Kluwer Academic: Norwell, MA.\nMalik, S. 1991. Quantitative seafloor backscatter characterization using an interferometric sidescan sonar. Master's Thesis, U. Virginia.\nMatsumoto, H., Dziak, R.P., and Fox, C.G. 1993. Estimation of seafloor microtopographic roughness through modeling of acoustic backscatter data recorded by multibeam sonar systems. J. Acoustical Society of America, 94:2776–2787.\nMazel, C. 1985. Side Scan Sonar Record Interpretation, Klein Associates: Salem, NH.\nMichalopoulou, Z., Alexandrou, D., and de Moustier, C. 1994. Application of a maximum likelihood processor to acoustic backscatter for the estimation of seafloor roughness parameters. J. Acoustical Society of America, 95:2467–2477.\nMitchell, N.C. and Somers, M.L. 1989. Quantitative backscatter measurements with a long-range side-scan sonar. IEEE J. Oceanic Engineering, 14(4):368–374.\nMourad, P.D. and Jackson, D.R. High frequency sonar equation models for bottom backscatter and forward loss. In Proc. IEEE Oceans 89 Conf., pp. 1163–1175.\nOren, M. and Nayar, S.K. 1992. Diffuse scattering model for rough surfaces. Dept. of Computer Science Technical Report 057–92, Columbia University, New York, NY.\nPress, W.H., Flannery, B.P., Teukolsky, S.A., and Vetterling, W.T. 1988. Numerical Recipes in C, Cambridge University Press: New York, NY.\nRigaud, V. and Marcé, L. 1990. Absolute location of underwater robotic vehicles by acoustic data fusion. In Proc. IEEE Int. Conf. Robotics and Automation, pp. 1310–1315.\nStanton, T.K. 1984. Sonar estimates of seafloor microroughness. J. Acoustical Society of America, 74:809–818.\nStewart, W.K., Marra, M., and Jiang, M. 1992. A hierarchical approach to seafloor classification using neural networks. IEEE Oceans 92 Conf., pp. 109–113.\nStewart, W.K. 1989. Three-dimensional modeling of seafloor backscatter from sidescan sonar for autonomous classification and navigation. In Proc. Unmanned Untethered Submersible Technology Conf., pp. 372–392.\nStewart, W.K., Chu, D., Malik, S., Lerner, S., and Singh, H. 1994. Quantitative seafloor characterization using a bathymetric sidescan sonar. IEEE J. Oceanic Engineering, 19(4):599–610.\nTorrance, K.E. and Sparrow, E.M. Theory for off-specular scattering omroughened surfaces, J. Optical Society of America, 57:1105–1114.\nUrick, R.J. 1983. Principles of Underwater Sound, McGraw-Hill: New York, NY.\nvon Alt, C. 1989. A 200kHz deep sea interferometric side scan sonar system. In Proc. IEEE Oceans 89 Conf., pp. 1136–1141.",{"VOID":865},"10.1007\u002FBF00141152","2024-08-30T21:42:15.912+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002FBF00141152",[869,887],{"id":870,"sortIndex":23,"researcher":22,"roles":871,"affiliations":873,"properties":882,"displayName":884,"givenName":22,"familyName":22},"087971e0-15cd-44f4-8569-ad670312f576",[872],"AUTHOR",[874],{"id":875,"sortIndex":23,"affiliation":876,"properties":22},"93831975-8042-4721-b1f3-bb96f84604b4",{"id":875,"createTime":22,"updateTime":22,"relativeEntities":877,"slug":22,"properties":878,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":881,"statistic":22},[],{"title":879},{"VI":880},"The Robotics Institute, Carnegie Mellon University, Pittsburgh",[],{"title":883,"gsAuthor":885},{"VI":884},"Andrew E. Johnson",{"VOID":886},"[\"M0KuaSwAAAAJ\"]",{"id":888,"sortIndex":217,"researcher":22,"roles":889,"affiliations":890,"properties":897,"displayName":899,"givenName":22,"familyName":22},"4fe07214-8b1e-400e-92bb-2a6e9e23b0ff",[872],[891],{"id":875,"sortIndex":23,"affiliation":892,"properties":22},{"id":875,"createTime":22,"updateTime":22,"relativeEntities":893,"slug":22,"properties":894,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":896,"statistic":22},[],{"title":895},{"VI":880},[],{"title":898,"gsAuthor":900},{"VI":899},"Martial Hebert",{"VOID":901},"[\"0ytii2EAAAAJ\"]",{"url":867,"publisher":903,"properties":945},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":904,"slug":10,"properties":905,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":909,"manageAffiliations":914,"indexDatabases":925,"url":22,"thumbnailPath":22,"statistic":940,"gsStatistic":22,"type":168,"analyzePriority":22},[],{"issn":906,"title":907,"eissn":908},{"VOID":15},{"EN":17},{"VOID":13},[910],{"id":26,"createTime":22,"updateTime":22,"relativeEntities":911,"label":912,"description":913,"parentId":22,"standard":22,"scholarHubFieldId":22},[],{"EN":29},{},[915,920],{"id":33,"createTime":22,"updateTime":22,"relativeEntities":916,"slug":22,"properties":917,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":919,"statistic":22},[],{"title":918},{"EN":37},[],{"id":40,"createTime":22,"updateTime":22,"relativeEntities":921,"slug":22,"properties":922,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":924,"statistic":22},[],{"title":923},{"EN":44},[46],[926,933],{"id":49,"indexDatabase":927,"url":62,"indexYears":22,"academicFieldIds":932,"indexDatabaseRanking":22},{"id":51,"createTime":22,"updateTime":22,"relativeEntities":928,"label":929,"description":930,"key":58,"publicationTags":931,"standard":22},[],{"EN":54,"VI":54},{"EN":56,"VI":57},[60,61],[64,65],{"id":67,"indexDatabase":934,"url":78,"indexYears":79,"academicFieldIds":939,"indexDatabaseRanking":82},{"id":69,"createTime":22,"updateTime":22,"relativeEntities":935,"label":936,"description":937,"key":75,"publicationTags":938,"standard":22},[],{"EN":72,"VI":72},{"EN":72,"VI":74},[77],[81],{"impactFactor":23,"impactFactorByYear":941,"i10Index":97,"i10IndexLast5Year":98,"totalPublication":99,"totalPublicationByYear":942,"totalCitation":123,"totalCitationByYear":943,"totalCitationPerPublication":145,"totalCitationPerPublicationByYear":944,"hindexLast5Year":167,"hindex":167},{"2012":85,"2013":86,"2014":87,"2015":88,"2016":89,"2017":90,"2018":91,"2019":92,"2020":93,"2021":94,"2022":95,"2023":96},{"1994":101,"1995":102,"1996":103,"1997":104,"1998":105,"1999":106,"2000":106,"2001":107,"2002":106,"2003":108,"2004":98,"2005":109,"2006":110,"2007":111,"2008":112,"2009":113,"2010":114,"2011":109,"2012":114,"2013":107,"2014":115,"2015":116,"2016":107,"2017":110,"2018":117,"2019":118,"2020":119,"2021":120,"2022":111,"2023":121,"2024":122},{"1994":125,"1995":126,"2004":127,"2005":128,"2006":129,"2007":130,"2008":131,"2009":132,"2010":133,"2011":134,"2012":135,"2013":136,"2014":137,"2015":138,"2016":139,"2017":140,"2018":141,"2019":142,"2020":143,"2021":97,"2023":144},{"1994":147,"1995":148,"2004":149,"2005":150,"2006":151,"2007":152,"2008":153,"2009":154,"2010":155,"2011":156,"2012":106,"2013":157,"2014":158,"2015":159,"2016":160,"2017":161,"2018":162,"2019":163,"2020":164,"2021":165,"2023":166},{"pages":946,"volume":948},{"VOID":947},"145-168",{"VOID":949},"3",{"total":23,"publishYear":951,"statisticByYear":952},1996,{},"1996-06-01","2026-07-27T05:40:13.487+00:00",[82,60],{"id":957,"createTime":958,"updateTime":959,"relativeEntities":960,"slug":961,"properties":962,"entityType":190,"verifyStatus":191,"verifyTime":973,"verifyNote":193,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":974,"fullTextUrl":22,"authors":975,"publicationType":235,"publisherRelationship":1035,"citationCount":1083,"citationInfo":1084,"publishDate":1090,"publishYear":1085,"citationAnalyzeStatus":300,"lastCitationAnalyze":1091,"indexDatabases":1092,"openAccess":22,"references":22,"isForceReanalyzing":848},"192cf03a-e949-49fc-8122-a8659013fadd","2023-12-13T20:19:13.867+00:00","2026-07-20T16:41:31.827+00:00",[],"Steerable-miniature-jumping-robot",{"abstract":963,"title":965,"gsPaper":967,"references":969,"doi":971},{"EN":964},"Jumping is used in nature by many small animals to locomote in cluttered environments or in rough terrain. It offers small systems the benefit of overcoming relatively large obstacles at a low energetic cost. In order to be able to perform repetitive jumps in a given direction, it is important to be able to upright after landing, steer and jump again. In this article, we review and evaluate the uprighting and steering principles of existing jumping robots and present a novel spherical robot with a mass of 14 g and a size of 18 cm that can jump up to 62 cm at a take-off angle of 75°, recover passively after landing, orient itself, and jump again. We describe its design details and fabrication methods, characterize its jumping performance, and demonstrate the remote controlled prototype repetitively moving over an obstacle course where it has to climb stairs and go through a window. (See videos 1–4 in the electronic supplementary material.)",{"EN":966},"Steerable miniature jumping robot",{"VOID":968},"[\"7962275779589733410\"]",{"VOID":970},"Alexander, R. M. (1988). Elastic mechanisms in animal movement. Cambridge: Cambridge University Press.\nAlexander, R. M. (2003). Principles of animal locomotion. Princeton: Princeton University Press.\nArmour, R., Paskins, K., Bowyer, A., Vincent, J. F. V., & Megill, W. (2007). Jumping robots: a biomimetic solution to locomotion across rough terrain. Bioinspiration and Biomimetics Journal, 2, 65–82.\nBennet-Clark, H. C. (1975). The energetics of the jump of the locust Schistocerca gregaria. Journal of Experimental Biology, 63(1), 53–83.\nBrackenbury, J., & Hunt, H. (1993). Jumping in springtails: mechanism and dynamics. Journal of Zoology, 229, 217–236.\nBurdick, J., & Fiorini, P. (2003). Minimalist jumping robot for celestial exploration. The International Journal of Robotics Research, 22(7), 653–674.\nBurrows, M. (2003). Biomechanics: Froghopper insects leap to new heights. Nature, 424(6948), 509.\nCard, G., & Dickinson, M. (2008). Performance trade-offs in the flight initiation of drosophila. Journal of Experimental Biology, 211(3), 341.\nFaisal, A. (2001). Coordinated righting behaviour in locusts. Journal of Experimental Biology, 204(4), 637–648.\nFrantsevich, L. (2004). Righting kinematics in beetles (insecta: Coleoptera). Arthropod Structure and Development, 33(3), 221–235.\nGronenberg, W. (1996). Fast actions in small animals: springs and click mechanisms. Journal of Comparative Physiology A: Sensory, Neural, and Behavioral Physiology, 178(6), 727–734.\nHollander, M., & Wolfe, D. A. (1999). Nonparametric statistical methods. New York: Wiley.\nKaspari, M., & Weiser, M. D. (1999). The sizegrain hypothesis and interspecific scaling in ants. Functional Ecology, 13(4), 530–538.\nKovac, M., Guignard, A., Nicoud, J. D., Zufferey, J. C., & Floreano, D. (2007). A 1.5 g sma-actuated microglider looking for the light. In IEEE international conference on robotics and automation, pp. 367–372.\nKovac, M., Fuchs, M., Guignard, A., Zufferey, J., & Floreano, D. (2008). A miniature 7 g jumping robot. In IEEE international conference on robotics and automation, pp. 373–378.\nKovac, M., Schlegel, M., Zufferey, J. C., & Floreano, D. (2009a). A miniature jumping robot with self-recovery capabilities. In IEEE\u002FRSJ international conference on robotics and automation, pp. 583–588.\nKovac, M., Zufferey, J., & Floreano, D. (2009b). Towards a self-deploying and gliding robot. In Floreano, D., Zufferey, J. C., Srinivasan, M. V., & Ellington, C. (Eds.), Flying insects and robots Berlin: Springer, Chap. 19.\nLambrecht, B. G. A., Horchler, A. D., & Quinn, R. D. (2005). A small, insect-inspired robot that runs and jumps. In IEEE\u002FRSJ international conference on robotics and automation, pp. 1240–1245.\nRoberts, T. J., & Marsh, R. L. (2003). Probing the limits to muscle-powered accelerations: lessons from jumping bullfrogs. Journal of Experimental Biology, 206(15), 2567–2580.\nScarfogliero, U., Stefanini, C., & Dario, P. (2007). Design and development of the long-jumping “grillo” mini robot. In IEEE international conference on robotics and automation, pp. 467–472.\nStoeter, S. A., Rybski, P. E., & Papanikolopoulos, N. (2002). Autonomous stair-hopping with scout robots. In IEEE\u002FRSJ international conference on intelligent robots and systems, Vol. 1, pp. 721–726.\nSugiyama, Y., Yamanaka, M., & Hirai, S. (2005). Circular\u002Fspherical robots for crawling and jumping. In IEEE international conference on robotics and automation, pp. 3595–3600.\nTsukagoshi, H., Sasaki, M., Kitagawa, A., & Tanaka, T. (2005). Design of a higher jumping rescue robot with the optimized pneumatic drive. In IEEE international conference on robotics and automation, pp. 1276–1283.\nUllman, D. G. (2002). The mechanical design process. New York: McGraw-Hill.\nWeiss, P. (2001). Hop… hop… hopbots!: designers of small, mobile robots take cues from grasshoppers and frogs. Science News, 159, 88.\nYim, M., Roufas, K., Duff, D., Zhang, Y., Eldershaw, C., & Homans, S. (2003). Modular reconfigurable robots in space applications. Autonomous Robots, 14(2), 225–237.\nZufferey, J. C., Klaptocz, A., Beyeler, A., Nicoud, J. D., & Floreano, D. (2007). A 10-gram vision-based flying robot. 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study the following multi-robot coordination problem: given a graph, where each edge is weighted by the probability of surviving while traversing it, find a set of paths for K robots that maximizes the expected number of nodes collectively visited, subject to constraints on the probabilities that each robot survives to its destination. We call this the Team Surviving Orienteers (TSO) problem, which is motivated by scenarios where a team of robots must traverse a dangerous environment, such as aid delivery after disasters. We present the TSO problem formally along with several variants, which represent “survivability-aware” counterparts for a wide range of multi-robot coordination problems such as vehicle routing, patrolling, and informative path planning. We propose an approximate greedy approach for selecting paths, and prove that the value of its output is within a factor \n                  \n                    \n                  \n                  $$1-e^{-p_s\u002F\\lambda }$$\n                  \n                    \n                  \n                 of the optimum where \n                  \n                    \n                  \n                  $$p_s$$\n                  \n                    \n                  \n                 is the per-robot survival probability threshold, and \n                  \n                    \n                  \n                  $$1\u002F\\lambda \\le 1$$\n                  \n                    \n                  \n                 is the approximation factor of an oracle routine for the well-known orienteering problem. We also formalize an on-line update version of the TSO problem, and a generalization to heterogeneous teams where both robot types and paths are selected. We provide numerical simulations which verify our theoretical findings, apply our approach to real-world scenarios, and demonstrate its effectiveness in large-scale problems with the aid of a heuristic for the orienteering problem.",{"EN":1103},"The Team Surviving Orienteers problem: routing teams of robots in uncertain environments with survival constraints",{"VOID":1105},"[]",{"VOID":1107},"Atanasov, N., Le Ny, J., Daniilidis, K., & Pappas, G. J. (2015). Decentralized active information aquisition: Theory and application to multi-robot SLAM. In Proceedings of the IEEE conference on robotics and automation.\nCampbell, A. M., Gendreau, M., & Thomas, B. W. (2011). The orienteering problem with stochastic travel and service times. Annals of Operations Research, 186(1), 61–81.\nChao, I. M., Golden, B. L., & Wasil, E. A. (1996). The team orienteering problem. European Journal of Operational Research, 88(3), 464–474.\nChekuri, C., Korula, N., & Pál, M. (2012). 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The orienteering problem. Naval Research Logistics, 34(3), 307–318.\nGolden, B. L., & Yee, J. R. (1979). A framework for probabilistic vehicle routing. AIIE Transactions, 11(2), 109–112.\nGolovin, D., & Krause, A. (2011). Adaptive submodularity: Theory and applications in active learning and stochastic optimization. Journal of Artificial Intelligence Research, 42, 427–486.\nGunawan, A., Lau, H. C., & Vansteenwegen, P. (2016). Orienteering problem: A survey of recent variants, solution approaches and applications. European Journal of Operational Research, 255(2), 315–332.\nGupta, A., Krishnaswamy, R., Nagarajan, V., & Ravi, R. (2012). Approximation algorithms for stochastic orienteering. In ACM-SIAM symposium on discrete algorithms.\nHaldane, J. B. S. (1932). A note on inverse probability. Mathematical Proceedings of the Cambridge Philosophical Society, 28(1), 55–61.\nHollinger, G. A., & Sukhatme, G. S. (2014). Sampling-based robotic information gathering algorithms. 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(1997). Distributed algorithms (1st ed.). Los Altos: Morgan Kaufmann.\nNemhauser, G. L., Wolsey, L. A., & Fisher, M. L. (1978). An analysis of approximations for maximizing submodular set functions-I. Mathematical Programming, 14(1), 265–294.\nNOAA National Weather Service Radar Operations Center. (1991). NOAA next generation radar (NEXRAD) level II base data.\nPillac, V., Gendreau, M., Guéret, C., & Medaglia, A. L. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225(1), 1–11.\nPsaraftis, H. N., Wen, M., & Kontovas, C. A. (2016). Dynamic vehicle routing problems: Three decades and counting. Networks, 67(1), 3–31.\nSingh, A., Krause, A., Guestrin, C., & Kaiser, W. J. (2009). Efficient informative sensing using multiple robots. Journal of Artificial Intelligence Research, 34, 707–755.\nSmith, R. N., Schwager, M., Smith, S. L., Jones, B. H., Rus, D., & Sukhatme, G. S. (2011). Persistent ocean monitoring with underwater gliders: Adapting sampling resolution. Journal of Field Robotics, 28(5), 714–741.\nStewart, W. R., & Golden, B. L. (1983). Stochastic vehicle routing: A comprehensive approach. European Journal of Operational Research, 14(4), 371–385.\nVaněk, O., Jakob, M., Hrstka, O., & Pěchouček, M. (2013). Agent-based model of maritime traffic in piracy affected waters. Transportation Research Part C: Emerging Technologies, 36, 157–176.\nVansteenwegen, P., Souffriau, W., Berghe, G. V., & Van Oudheusden, D. (2009). Iterated local search for the team orienteering problem with time windows. Computers and Operations Research, 36(12), 3281–3290.\nVansteenwegen, P., Souffriau, W., & Van Oudheusden, D. (2011). The orienteering problem: A survey. European Journal of Operational Research, 209(1), 1–10.\nVarakantham, P., & Kumar, A. (2013). Optimization approaches for solving chance constrained stocahstic orienteering problems. In Proceedings of the international conference on algorithmic decision theory.\nWagner, S., & Affenzeller, M. (2005). HeuristicLab: A generic and extensible optimization environment. In B. Ribeiro, R. F. Albrecht, A. Dobnikar, D. W. Pearson & N. C. Steele (Eds.), Adaptive and natural computing algorithms. Berlin: Springer.\nWei, K., Iyer, R. K., & Bilmes, J. A. (2014). Fast multi-stage submodular maximization. In International conference on machine learning.\nZhang, B., Tang, L., & Roemer, M. (2017). Probabilistic planning and risk evaluation based on ensemble weather forecasting. IEEE Transactions on Automation Sciences and Engineering, PP(99), 1–11.\nZhang, H., & Vorobeychik, Y. (2016). Submodular optimization with routing constraints. 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Time-domain correlation image sensor: FirstCmosrealization of demodulator pixels array. In Proc. 1999 IEEE Workshop on Charge-Coupled Devices and Advanced Image Sensors, Karuizawa, Japan, pp. 33–36.",{"id":22,"text":1334,"url":22,"identifiers":22},"Hoshino, K. Mura, F., and Shimoyama, I. 2000. Design and performance of a micro-sized biomorphic compound eye with a scanning retina. Journal of Microelectromechanical Systems, 9(1):32–37.",{"id":22,"text":1336,"url":22,"identifiers":22},"Kimachi, A., Imaizumi, R., and Ando, S. 1998. Intelligent image sensor with a vibratory mirror mimicking involuntary eye movement. In Technical Digest of the 16th Sensor Symposium, pp. 171–176.",{"id":22,"text":1338,"url":22,"identifiers":22},"Lande, T.S. 1998. Neuromorphic Systems Engineering-Neural Networks in Silicon, Kluwer Academic Publishers: Dordrecht.",{"id":22,"text":1340,"url":22,"identifiers":22},"Landolt, O., Mitros, A., and Koch, C. 2001. Visual sensor with resolution enhancement by mechanical vibrations. In Proc. 2001 Conf. Advanced Research in VLSI, Salt Lake City, Utah, pp. 249–264.",{"id":22,"text":1342,"url":22,"identifiers":22},"Mead, C. 1989. Analog VLSI and Neural Systems, Addison Wesley.",{"id":22,"text":1344,"url":22,"identifiers":22},"Viollet, S. and Franceschini, N. 1999. Visual servo system based on a biologically-inspired scanning sensor. In Sensor Fusion and Decentralized Control in Robotic Systems II, Vol. 3839, SPIE: Bellingham, pp. 144–155.",{"id":1346,"createTime":1347,"updateTime":1348,"relativeEntities":1349,"slug":1350,"properties":1351,"entityType":190,"verifyStatus":191,"verifyTime":1362,"verifyNote":193,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":1363,"fullTextUrl":1364,"authors":1365,"publicationType":235,"publisherRelationship":1431,"citationCount":23,"citationInfo":1480,"publishDate":1483,"publishYear":1481,"citationAnalyzeStatus":1327,"lastCitationAnalyze":1484,"indexDatabases":1485,"openAccess":22,"references":22,"isForceReanalyzing":848},"b3b6ce92-66f3-4319-8118-9771ddd291dc","2024-02-20T09:48:11.120+00:00","2026-07-17T20:18:09.855+00:00",[],"Resilient-distributed-state-estimation-with-mobile-agents-overcoming-Byzantine-adversaries-communication-losses-and-intermittent-measurements",{"abstract":1352,"title":1354,"gsPaper":1356,"references":1358,"doi":1360},{"EN":1353},"Applications in environmental monitoring, surveillance and patrolling typically require a network of mobile agents to collectively gain information regarding the state of a static or dynamical process evolving over a region. However, these networks of mobile agents also introduce various challenges, including intermittent observations of the dynamical process, loss of communication links due to mobility and packet drops, and the potential for malicious or faulty behavior by some of the agents. The main contribution of this paper is the development of resilient, fully-distributed, and provably correct state estimation algorithms that simultaneously account for each of the above considerations, and in turn, offer a general framework for reasoning about state estimation problems in dynamic, failure-prone and adversarial environments. Specifically, we develop a simple switched linear observer for dealing with the issue of time-varying measurement models, and resilient filtering techniques for dealing with worst-case adversarial behavior subject to time-varying communication patterns among the agents. Our approach considers both communication patterns that recur in a deterministic manner, and patterns that are induced by random packet drops. For each scenario, we identify conditions on the dynamical system, the patrols, the nominal communication network topology, and the failure models that guarantee applicability of our proposed techniques. Finally, we complement our theoretical results with detailed simulations that illustrate the efficacy of our algorithms in the presence of the technical challenges described above.",{"EN":1355},"Resilient distributed state estimation with mobile agents: overcoming Byzantine adversaries, communication losses, and intermittent measurements",{"VOID":1357},"[\"2571801664654701360\"]",{"VOID":1359},"Abazeed, M., Faisal, N., Zubair, S., & Ali, A. (2013). Routing protocols for wireless multimedia sensor network: a survey. Journal of Sensors.\ncitation_journal_title=The International Journal of Robotics Research; citation_title=Persistent monitoring in discrete environments: Minimizing the maximum weighted latency between observations; citation_author=S Alamdari, E Fata, SL Smith; citation_volume=33; citation_issue=1; citation_publication_date=2014; citation_pages=138-154; citation_doi=10.1177\u002F0278364913504011; citation_id=CR2\ncitation_journal_title=The Journal of Defense Modeling and Simulation; citation_title=Modeling the Lanchester laws with system dynamics; citation_author=MJ Artelli, RF Deckro; citation_volume=5; citation_issue=1; citation_publication_date=2008; citation_pages=1-20; citation_doi=10.1177\u002F154851290800500101; citation_id=CR3\ncitation_journal_title=Automatica; citation_title=A complete greedy algorithm for infinite-horizon sensor scheduling; citation_author=AB Asghar, ST Jawaid, SL Smith; citation_volume=81; citation_publication_date=2017; citation_pages=335-341; citation_doi=10.1016\u002Fj.automatica.2017.04.018; citation_id=CR4\nAtanasov, N., Le Ny, J., Daniilidis, K., & Pappas, G. J. (2014). Information acquisition with sensing robots: Algorithms and error bounds. In Proceedings of the 2014 IEEE international conference on robotics and automation (ICRA) (pp. 6447–6454).\nAtanasov, N., Le Ny, J., Daniilidis, K., & Pappas, G. J. (2015). Decentralized active information acquisition: Theory and application to multi-robot SLAM. In Proceedings of the 2015 IEEE international conference on robotics and automation (ICRA) (pp. 4775–4782).\ncitation_journal_title=Systems & Control Letters; citation_title=Delayed unknown input observers for discrete-time linear systems with guaranteed performance; citation_author=A Chakrabarty, R Ayoub, SH Żak, S Sundaram; citation_volume=103; citation_publication_date=2017; citation_pages=9-15; citation_doi=10.1016\u002Fj.sysconle.2017.02.005; citation_id=CR7\ncitation_journal_title=Automatica; citation_title=State and unknown input observers for nonlinear systems with delayed measurements; citation_author=A Chakrabarty, E Fridman, SH Żak, GT Buzzard; citation_volume=95; citation_publication_date=2018; citation_pages=246-253; citation_doi=10.1016\u002Fj.automatica.2018.05.036; citation_id=CR8\ncitation_journal_title=IEEE Transactions on Signal Processing; citation_title=Resilient distributed estimation through adversary detection; citation_author=Y Chen, S Kar, JMF Moura; citation_volume=66; citation_issue=9; citation_publication_date=2018; citation_pages=2455-2469; citation_doi=10.1109\u002FTSP.2018.2813330; citation_id=CR9\ncitation_title=Linear system theory and design; citation_publication_date=1998; citation_id=CR10; citation_author=C-T Chen; citation_publisher=Oxford University Press\nChong, M. S., Wakaiki, M., & Hespanha, J. P. (2015). Observability of linear systems under adversarial attacks. In Proceedings of the American control conference (pp. 2439–2444).\ncitation_journal_title=Proceedings of the National Academy of Sciences; citation_title=The heat kernel as the pagerank of a graph; citation_author=F Chung; citation_volume=104; citation_issue=50; citation_publication_date=2007; citation_pages=19735-19740; citation_doi=10.1073\u002Fpnas.0708838104; citation_id=CR12\ncitation_journal_title=Mathematical Geology; citation_title=The origins of kriging; citation_author=N Cressie; citation_volume=22; citation_issue=3; citation_publication_date=1990; citation_pages=239-252; citation_doi=10.1007\u002FBF00889887; citation_id=CR13\nDeghat, M., Ugrinovskii, V., Shames, I., & Langbort, C. (2016). Detection of biasing attacks on distributed estimation networks. In Proceedings of the IEEE conference on decision and control (pp. 2134–2139).\ncitation_journal_title=IFAC-PapersOnLine; citation_title=Distributed consensus-based Kalman filtering considering subspace decomposition; citation_author=AR Nozal, L Orihuela, P Milláan; citation_volume=50; citation_issue=1; citation_publication_date=2017; citation_pages=2494-2499; citation_doi=10.1016\u002Fj.ifacol.2017.08.443; citation_id=CR15\ncitation_journal_title=Automatica; citation_title=Resilient consensus of second-order agent networks: Asynchronous update rules with delays; citation_author=SM Dibaji, H Ishii; citation_volume=81; citation_publication_date=2017; citation_pages=123-132; citation_doi=10.1016\u002Fj.automatica.2017.03.008; citation_id=CR16\ncitation_journal_title=Journal of the ACM (JACM); citation_title=Reaching approximate agreement in the presence of faults; citation_author=D Dolev, NA Lynch, SS Pinter, EW Stark, WE Weihl; citation_volume=33; citation_issue=3; citation_publication_date=1986; citation_pages=499-516; citation_doi=10.1145\u002F5925.5931; citation_id=CR17\ncitation_journal_title=IEEE Journal of Selected Topics in Signal Processing; citation_title=On the genericity properties in distributed estimation: Topology design and sensor placement; citation_author=M Doostmohammadian, UA Khan; citation_volume=7; citation_issue=2; citation_publication_date=2013; citation_pages=195-204; citation_doi=10.1109\u002FJSTSP.2013.2246135; citation_id=CR18\nDunbabin, M., Roberts, J. M., Usher, K., & Corke, P. (2004). A new robot for environmental monitoring on the Great Barrier Reef. In Proceedings of the 2004 Australasian conference on robotics & automation. Australian Robotics & Automation Association\ncitation_journal_title=Systems & Control Letters; citation_title=Remote stabilization over fading channels; citation_author=N Elia; citation_volume=54; citation_issue=3; citation_publication_date=2005; citation_pages=237-249; citation_doi=10.1016\u002Fj.sysconle.2004.08.009; citation_id=CR20\ncitation_journal_title=IEEE Transactions on Automatic Control; citation_title=Secure estimation and control for cyber-physical systems under adversarial attacks; citation_author=H Fawzi, P Tabuada, S Diggavi; citation_volume=59; citation_issue=6; citation_publication_date=2014; citation_pages=1454-1467; citation_doi=10.1109\u002FTAC.2014.2303233; citation_id=CR21\nGandin, L. S. (1963). Objective analysis of meteorological fields. Israel Program for Scientific Translations, 242.\nGoodin, D. (2016). 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point descriptors have been traditionally used for visual loop closure detection. However, in low-textured environments, it is usually difficult to find enough point features and, hence, the performance of such algorithms degrade. Under this context, this paper proposes a loop closure detection method that combines lines and learned points to work, particularly, in scenarios where hand-crafted points fail. To index previous images, we adopt separate incremental binary Bag-of-Words (BoW) schemes for points and lines. Moreover, we adopt a binarization procedure for features’ descriptors to benefit from the advantages of learned features into a binary BoW model. Furthermore, image candidates from each BoW instance are merged using a novel query-adaptive late fusion approach. Finally, a spatial verification stage, which integrates appearance and geometry perspectives, allows us to enhance the global performance of the method. Our approach is validated using several public datasets, outperforming other state-of-the-art solutions in most cases, especially in low-textured scenarios.",{"EN":1496},"Appearance-based loop closure detection combining lines and learned points for low-textured environments",{"VOID":1498},"[\"11897275558232658248\"]",{"VOID":1500},"Angeli, A., Filliat, D., Doncieux, S., & Meyer, J. A. (2008). A fast and incremental method for loop-closure detection using bags of visual words. IEEE Transactions on Robotics, 24(5), 1027–1037.\nArroyo, R., Alcantarilla, P. F., Bergasa, L. M., Romera, E. (2016). Fusion and binarization of CNN features for robust topological localization across seasons. In: IEEE\u002FRSJ International Conference on Intelligent Robots and Systems, pp. 4656–4663.\nArroyo, R., Alcantarilla, P. F., Bergasa, L. M., Yebes, J. J., Bronte, S. (2014). Fast and effective visual place recognition using binary codes and disparity information. In: IEEE\u002FRSJ International Conference on Intelligent Robots and Systems, pp. 3089–3094.\nBampis, L., Amanatiadis, A., & Gasteratos, A. (2018). Fast loop-closure detection using visual-word-vectors from image sequences. International Journal of Robotics Research, 37(1), 62–82.\nBay, H., Tuytelaars, T., Van Gool, L. (2006). Surf: Speeded up robust features. In: European Conference on Computer Vision, pp. 404–417.\nBhowmik, N., González, R., Gouet-Brunet, V., Pedrini, H., Bloch, G. (2014). Efficient fusion of multidimensional descriptors for image retrieval. In: IEEE International Conference on Image Processing, pp. 5766–5770.\nBian, J., Lin, W., Matsushita, Y., Yeung, S., Nguyen, T., Cheng, M. (2017). Gms: Grid-based motion statistics for fast, ultra-robust feature correspondence. In: International Conference on Computer Vision and Pattern Recognition, pp. 2828–2837.\nBlanco, J. L., Moreno, F. A., & Gonzalez, J. (2009). 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(2018). iBoW-LCD: An appearance-based loop-closure detection approach using incremental bags of binary words. IEEE Robotics and Automation Letters, 3(4), 3051–3057.\nGarg, S., Milford, M. (2020). Fast, compact and highly scalable visual place recognition through sequence-based matching of overloaded representations. In: IEEE International Conference on Robotics and Automation, pp. 3341–3348.\nGarg, S., Milford, M. (2021). Seqnet: Learning descriptors for sequence-based hierarchical place recognition. IEEE Robotics and Automation Letters, 4305–4312.\nGehrig, M., Stumm, E., Hinzmann, T., Siegwart, R. (2017). Visual place recognition with probabilistic voting. In: IEEE International Conference on Robotics and Automation, pp. 3192–3199.\nGeiger, A., Lenz, P., Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. In: International Conference on Computer Vision and Pattern Recognition, pp. 3354–3361.\nGomez-Ojeda, R., Moreno, F., Zuñiga-Noël, D., Scaramuzza, D., & Gonzalez-Jimenez, J. (2019). PL-SLAM: A stereo SLAM system through the combination of points and line segments. IEEE Transactions on Robotics, 35(3), 734–746.\nGrompone von Gioi, R., Jakubowicz, J., Morel, J., Randall, G. (2010). LSD: A fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence 32(4), 722–732.\nHan, J., Dong, R., Kan, J. (2021). A novel loop closure detection method with the combination of points and lines based on information entropy. Journal of Field Robotics, 38, 386–401.\nHausler, S., Milford, M. (2020). Hierarchical multi-process fusion for visual place recognition. arXiv preprint arXiv:2002.03895\nKenshimov, C., Bampis, L., Amirgaliyev, B., Arslanov, M., & Gasteratos, A. (2017). Deep learning features exception for cross-season visual place recognition. Pattern Recognition Letters, 100, 124–130.\nKhan, S., Wollherr, D. (2015). IBuILD: Incremental bag of binary words for appearance based loop closure detection. In: IEEE International Conference on Robotics and Automation, pp. 5441–5447.\nLabbé, M., & Michaud, F. (2013). Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Transactions on Robotics, 29(3), 734–745.\nLin, K., Lu, J., Chen, C., Zhou, J. (2016). Learning compact binary descriptors with unsupervised deep neural networks. In: International Conference on Computer Vision and Pattern Recognition, pp. 1183–1192.\nLin, K., Lu, J., Chen, C., Zhou, J., & Sun, M. (2019). Unsupervised deep learning of compact binary descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(6), 1501–1514.\nLopez-Antequera, M., Gomez-Ojeda, R., Petkov, N., & Gonzalez-Jimenez, J. (2017). 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In: IEEE\u002FRSJ International Conference on Intelligent Robots and Systems, pp. 1775–1782.",{"VOID":1502},"10.1007\u002Fs10514-021-10032-7","2024-06-26T03:26:50.213+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10514-021-10032-7",[1506,1523,1538],{"id":1507,"sortIndex":23,"researcher":22,"roles":1508,"affiliations":1509,"properties":1518,"displayName":1520,"givenName":22,"familyName":22},"8b1fe8e8-353a-47ea-bbaf-f10313d14d0e",[872],[1510],{"id":1511,"sortIndex":23,"affiliation":1512,"properties":22},"479464e8-fb2e-4d30-af4b-35d9f4c347fb",{"id":1511,"createTime":22,"updateTime":22,"relativeEntities":1513,"slug":22,"properties":1514,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":1517,"statistic":22},[],{"title":1515},{"VI":1516},"Department of Mathematics and Computer Science, University of the Balearic Islands, and IDISBA (Institut d’Investigacio Sanitaria de les Illes Balears), Palma de Mallorca, Spain",[],{"title":1519,"gsAuthor":1521},{"VI":1520},"Joan P. 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engineering approaches to controlling a hexapod walker typically involve a central control instance that implements an abstract optimal gait pattern and relies on additional optimization criteria to generate reference signals for servocontrollers at all the joints. In contrast, the gait of the slow-walking stick insect apparently emerges from an extremely decentralized architecture with separate step pattern generators for each leg, a strong dependence on sensory feedback, and multiple, in part redundant, primarily local interactions among the step pattern generators. Thus, stepping and step coordination do not reflect an explicit specification based on a global optimization using a representation of the system and its environment; instead they emerge from a distributed system and from the complex interaction with the environment. A similarly decentralized control at the level of single leg joints also may explain the control of leg dynamics. Simulations show that negative feedback for control of body height and walking direction combined with positive feedback for generation of propulsion produce a simple, extremely decentralized system that can handle a wide variety of changes in the walking system and its environment. Thus, there is no need for a central controller implementing global optimization. Furthermore, physiological results indicate that the nervous system uses approximate algorithms to achieve the desired behavioral output rather than an explicit, exact solution of the problem. Simulations and implementation of these design principles are being used to test their utility for controlling six-legged walking machines.",{"EN":1617},"Control of Walking in the Stick Insect: From Behavior and Physiology to Modeling",{"VOID":1105},{"VOID":1620},"Arbib, M. (Ed.) 1995. The Handbook of Brain Theory and Neural Networks, Bradford Books\u002FMIT Press: Cambridge, MA.\nBässler, U. 1983. Neural Basis of Elementary Behavior in Stick Insects, Springer-Verlag: Heidelberg.\nBässler, U. 1986. Afferent control of walking movements in the stick insect Cuniculina impigra. II. Reflex reversal and the release of the swing phase in the restrained foreleg. J. Comp. Physiol. 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Rhythmic patterns in the thoracic nerve cord of the stick insect induced by pilocarpine. J. Exp. Biol., 198:435-456.\nBurrows, M. 1996. The Neurobiology of an Insect Brain, Oxford University Press: Oxford.\nChrachri, A. and Clarac, F. 1987. Induction of rhythmic activity in motoneurons of crayfish thoracic ganglia by cholinergic agonists. Neurosci. Letters, 77:49-54.\nChurchland, P.S. and Sejnowski, T.J. 1988. Perspectives on cognitive neuroscience. Science, 242:741-745.\nCohen, A.H. and Boothe, D.L. 1999. Sensorimotor interactions during locomotion: Principles derived from biological systems. Autonomous Robots, 7:239-245.\nCruse, H. 1976a. The function of the legs in the free walking stick insect, Carausius morosus. J. Comp. Physiol., 112:235-262.\nCruse, H. 1976b. The control of body position in the stick insect (Carausius morosus) when walking over uneven surfaces. Biol. Cybern., 24:25-33.\nCruse, H. 1985a. Which parameters control the leg movement of a walking insect? II. The start of the swing phase. J. Exp. Biol., 116:357-362.\nCruse, H. 1985b. Coactivating influences between neighbouring legs in walking insects. J. Exp. Biol., 114:513-519.\nCruse, H. 1990. What mechanisms coordinate leg movement in walking arthropods? Trends in Neurosciences, 13:15-21.\nCruse, H. and Steinkuehler, U. 1993. Solution of the direct and inverse kinematic problems by a common algorithm based on the mean of multiple computations. Biol. Cybern., 69:345-351.\nCruse, H. and Bartling, C. 1995. Movement of joint angles in the legs of a walking insect, Carausius morosus. J. Insect Physiol., 41:761-771.\nCruse, H., Müller-Wilm, U., and Dean, J. 1993. Artificial neural nets for a 6-legged walking system. In From Animals to Animats 2, J.-A. Meyer, H.L. Roitblat, and S.W. Wilson (Eds.), MIT Press: Cambridge, MA, pp. 52-60.\nCruse, H., Kindermann, T., Schumm, M., Dean, J., and Schmitz, J. 1998a. Walknet—A biologically inspired network to control six-legged walking. Neural Networks, 11:1435-1447.\nCruse, H., Dean, J., Kindermann, T., Schmitz, J., and Schumm, M. 1998b. Simulation of complex movements using artificial neural networks. Z. Naturforschung, 53c:628-638.\nDean, J. 1989. Leg coordination in the stick insect, Carausius morosus: Effects of cutting thoracic connectives. J. Exp. Biol., 145:103-131.\nDean, J. 1990. Coding proprioceptive information to control movement to a target: Simulation with a simple neural network. Biol. Cybern., 63:115-120.\nDean, J. 1991a. Effect of load on leg movement and step coordination of the stick insect, Carausius morosus. J. Exp. Biol., 159:449-471.\nDean, J. 1991b. A model of leg coordination in the stick insect, Carausius morosus. II. Description of the kinematic model and simulation of normal step patterns. Biol. Cybern., 64:403-411.\nDean, J. 1992. A model of leg coordination in the stick insect, Carausius morosus. III. Responses to perturbations of normal coordination. Biol. Cybern., 66:335-343.\nDean, J. 1998. Animats and what they can tell us. Trends in Cognitive Sciences, 2:60-67.\nDean, J. and Wendler, G. 1982. Stick insects walking on a wheel: Perturbations induced by obstruction of leg protraction. J. Comp. Physiol., 148:195-207.\nDean, J. and Wendler, G. 1983. Stick insect locomotion on a walking wheel: Interleg coordination of leg position. J. Exp. Biol., 103:75-94.\nDelcomyn, F. 1980. Neural basis of rhythmic behavior in animals. Science, 210:492-498.\nDelcomyn, F. 1985. Factors regulating insect walking. Ann. Rev. Entomol., 30:239-256.\nDelcomyn, F. 1999. Walking robots and the central and peripheral control of locomotion in insects. Autonomous Robots, 7:259-270.\nDonner, M.D. 1987. Real-Time Control of Walking, Birkhäuser: Boston.\nEspenshied, K.S., Quinn, R.D., Chiel, H.J., and Beer, R.D. 1993. Leg coordination mechanisms in stick insect applied to hexapod robot locomotion. Adaptive Behavior, 1:455-468.\nEspenshied, K.S., Quinn, R.D., Chiel, H.J., and Beer, R.D. 1996. Biologically-based distributed control and local reflexes improve rough terrain locomotion in a hexapod robot. Robotics and Autonomous Systems, 18:59-64\nFranceschini, N., Pichon, J.M., and Blanes, C. 1992. From insect vision to robot vision. Philos. Trans. R. Soc. London Series B, 337:283-294.\nFriesen, W.O. 1989. Neuronal control of leech swimming movements. In Neuronal and Cellular Oscillators. Cellular Clocks Series, J.W. Jacklet (Ed.), Marcel Dekker Inc.: NY, Vol. 2, pp. 269-316.\nGraham, D. and Cruse, H. 1981. Coordinated walking of stick insects on a mercury surface. J. Exp. Biol., 92:229-241.\nGraham, D. 1977. Simulation of a model for the coordination of leg movement in free walking insects. Biol. Cybern., 26:187-198.\nGraham, D. 1978. Unusual step pattern in the free walking grasshopper Neoconocephalus robustus. II. A critical test of the leg interactions underlying different models of hexapod co-ordination. J. Exp. Biol., 73:159-172.\nGraham, D. 1979. Effects of circum-oesophageal lesion on the behaviour of the stick insect Carausius morosus. II. Changes in walking co-ordination. Biol. Cybern., 32:147-152.\nGraham, D. 1985. Pattern and control of walking in insects. Adv. Insect Physiol., 18:31-140.\nGrillner, S., Deliagina, T., Ekeberg, Oe., El Manira, A., Hill, R.H., Lansner, A., Orlovsky, G.N., and Wallen, P. 1995. Neural networks that co-ordinate locomotion and body orientation in lamprey. Trends in Neurosciences, 18:270-279.\nHallam, J. 1998. Why mix robotics and biology? IROS'98 Workshop WT1: Defining the future of biomorphic robotics, October 13, 1998, Victoria, B.C., Canada.\nHeiligenberg, W.F. 1991. Neural Nets in Electric Fish, MIT Press: Cambridge, MA.\nJander, J.P. 1985. Mechanical stability in stick insects when walking straight and around curves. In Insect Locomotion, M. Gewecke and G. Wendler (Eds.), Parey: Berlin, pp. 33-42.\nKristan, W.B., Jr., Lockery, S.R., and Lewis, J.E. 1995. Using reflexive behaviors of the medicinal leech to study information processing. J. Neurobiology, 27:380-389.\nMaes, P. 1993. Behavior-based artificial intelligence. In From Animals to Animats 2, J.A. Meyer, H.L. Roitblat, and S.W. Wilson (Eds.), MIT Press: Cambridge, MA, pp. 2-10.\nMaes, P., Mataric, M.J., Meyer, J.-A., Pollack, J., and Wilson, S.W. (Eds.) 1996. From Animals to Animats 4, MIT Press: Cambridge, MA.\nMcGhee, R.B. and Sun, S.-S. 1974. On the problem of selecting a gait for a legged vehicle. In Proceedings of the 6th IFAC Symposium on Automatic Control in Space. IFAC: Pittsburgh and Moscow, pp. 53-62.\nMüller-Wilm, U., Dean, J., Cruse, H., Weidemann, H.J., Eltze, J., and Pfeiffer, F. 1992. Kinematic model of a stick insect as an example of a 6-legged walking system. Adaptive Behavior, 1:155-169.\nPearson, K.G. 1972. Central programming and reflex control of walking in the cockroach. J. Exp. Biol., 56:173-193.\nPearson, K.G. and Franklin, R. 1984. Characteristics of leg movements and patterns of coordination in locusts walking on rough terrain. Int. J. Robotics Res., 3:101-112.\nPearson, K.G. and Iles, J.F. 1973. Nervous mechanisms underlying intersegmental coordination of leg movements during walking in the cockroach. J. Exp. Biol., 58:725-744.\nPfeiffer, F., Eltze, J., and Weidemann, H.-J. 1995. Six-legged technical walking machine considering biological principles. Robotics and Autonomous Systems, 14:223-232.\nRind, R.C. 1997. Collision avoidance: From the locust eye to a seeing machine. In From Living Eyes to Seeing Machines, M.V. Srinivasan and S. Venkatesh (Eds.), Oxford University Press: Oxford, pp. 105-125.\nRixe, A. and Dean, J. 1995. Mechanisms of curve walking in the stick insect Carausius morosus. In Proceedings of the 23th Göttingen Neurobiology Conference, N. Elsner and R. Menzel (Eds.), Stuttgart: Thieme Verlag, p. 217.\nRyckebusch, S. and Laurent, G. 1993. Rhythmic patterns evoked in locust leg motor neurons by the muscarinic agonist pilocarpine. J. Neurophysiol., 69:1583-1595.\nSchmitz, J. and Haßfeld, G. 1989. The treading-on-tarsus reflex in stick insects: Phase-dependence and modifications of the motor output during walking. J. Exp. Biol., 143:373-388.\nSchmitz, J., Dean, J., and Kittmann, R. 1991. Central projections of leg sense organs in the stick insect, Carausius morosus (Insecta, Phasmida). Zoomorphology, 111:19-33.\nSchmitz, J., Ernst, S., and Reich, J. 1996. A new intersegmental coordinating influence in the walking system of the stick insect. In Brain and Evolution Proceedings of the 24th Goettingen Neurobiology Conference, N. Elsner and H.-U. Schnitzler (Eds.), Stuttgart: Thieme Verlag, p. 129.\nSchmitz, J. and Ernst, S. Intra-and intersegmental load compensating reactions of walking stick insects, Carausius morosus, in preparation.\nSchmitz, J., Bartling, C., Brunn, D.E., Cruse, H., Dean, J., Kindermann, T., Schumm, M., and Wagner, H. 1995. Adaptive properties of hard-wired neuronal systems. Adaptive Eigenschaften festverdrahteter neuronaler Systeme. Verh. Dtsch. Zool. Ges., 88.2:165-179.\nSelverston, A. 1995. Modulation of circuits underlying rhythmic behaviors. J. Comp. Physiol. A, 176:139-147.\nSrinivasan, M.V., Chah, J.S., Weber, K., Venkatesh, S., Nagle, M.G., and Zhang, S.W. 1999. Robot navigation inspired by principles of insect vision. Robotics and Autonomous Systems, 26:203-216.\nSteinkühler, U. and Cruse, H. 1998. A holistic model for an internal representation to control the movement of a manipulator with redundant degrees of freedom. Biol. Cybernetics, 79:457-466.\nWeidemann, H.-J., Eltze, J., and Pfeiffer, F. 1993. Leg design based on biological principles. In Proc. of the 1993 IEEE Conference on Robotics and Automation, pp. 352-358.\nWendler, G. 1964. Laufen und Stehen der Stabheuschrecke Carausius morosus: Sinnesborstenfelder in den Beingelenken als Glieder von Regelkreisen. Z. vergl. Physiol., 48:198-250.\nWilson, D.M. 1966. Insect walking. Ann. Rev. 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paper describes two humanoid robots developed in the Humanoid Robotics Institute, Waseda University. Hadaly-2 is intended to realize information interaction with humans by integrating environmental recognition with vision, conversation capability (voice recognition, voice synthesis), and gesture behaviors. It also possesses physical interaction functions for direct contact with humans and behaviors that are gentle and safe for humans. WABIAN is a robot with a complete human configuration that is capable of walking on two legs and carrying things as with humans. Furthermore, it has functions for information interactions suite for uses at home.",{"EN":1754},"Humanoid Robots in Waseda University—Hadaly-2 and WABIAN",{"VOID":1756},"[\"8889502330062534062\"]",{"VOID":1758},"Alford, W.A., Rogers, D.M., Wilkes, D.M., and Kawamura, K. 1999. Multi-agent system for a human-friendly robot. In Proc. IEEE Conf. on System, Man and Cybernetics, Vol. 2, pp. 1064–1069.\nBrooks, R. 1999. Technologies for human\u002Fhumanoid natural interaction. In Proc. HURO'99: The Second International Symposium on Humanoid Robots, pp. 135–147.\nGuglielmelli, E., Dario, P., Laschi, C., Fontanelli, R., Susani, M., Verbeeck, P., and Gabus, J.C. 1996. Humans and technologies at home: From friendly appliances to robotic interface. In Proc. of RO-MAN'96, pp. 71–79.\nHashimoto, S. et al. 1997. Humanoid robot-development of an information assistant robot hadaly. In Proceedings of RO-MAN'97, pp. 106–111.\nHirai, P. et al. 1997. Current and future perspective of Honda humanoid robot. In Proc. IROS'97, IEEE\u002FRSJ, pp. 500–509.\nInoue, H. 1998. A platform based humanoid project. In Proc. IARP First Workshop on Humanoid and Human Friendly Robotics, pp. I-1-I-4.\nKato, I. 1993. Conception of living and life support robot. Journal of the Robotics Society of Japan, 11(5):14–17.\nKato, I. et al. 1972. Pneumatically powered artificial legs walking automatically under various circumstances. In Proc. of 4th Int. Symposium in External Control of Human Extremities.\nKato, I. et al. 1987. Wabot-2: Autonomous robot with dexterous finger-arm. Proc. of IEEE Robotics and Automation, 5(2):90–97.\nKobayashi, T. and Haruyama, S. 1997. Partly-hidden Markov and its application to gesture recognition. In Proc. of Intl. Conference on Acoustics, Speech, and Signal Proc. (ICASSP97),Vol. 4, pp. 3081–3084.\nKurata, T., Chang, D., and Hashimoto, S. 1995. Multimedia sensing system for robot. In Proc. of the 4th IEEE Intl.Workshop on Robot and Human Communication, pp. 83–88.\nMorita, T. and Sugano, S. 1997. Development of an anthropomorphic force-controlled manipulatorWAM10. In 8th Intl. Conference on Advanced Robotics, pp. 701–706.\nNoguchi, T. and Hashimoto, S. 1998. Recognition environment by active searching. In Proc. of the conference of the Information Processing Society of Japan.\nSawada, H. and Hashimoto, S. 1997. Gesture recognition using an acceleration sensor and its application to musical performance control. Electronics and Communication in Japan, Part 3, 80(5):452–259.\nShirai, K. 1997. Spoken dialogue in multimodal human interface. In ICSP 97 Proc., Vol. 1, pp. 13–20.\nTakanishi, A., Matsuno, T., and Kato, I. 1997. Development of an anthropomorphic head-eye robot with two eyes-coordinated headeye motion and pursuing motion in the depth direction. In Proceedings IROS97, pp. 799–804.\nTakanobu, H. et al. 1997. Japan-Italy joint research on interaction between humans and remote environments. In Proc of the Conference of the Robotics Society of Japan, pp. 789–790.\nTanie, K. 1999. MITI's humanoid robot project. In Proc. HURO'99: The Second International Symposium on Humanoid Robots, pp. 71–76.\nYamaguchi, J. and Takanishi, A. 1997. Design of bipedwalking robot having antagonistic driven joint using nonlinear spring mechanism. 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In contrast to other computational implementations, the developmental approach aims to acquire sensorimotor competence through growth processes modelled on data and theory from infant psychology. Constraints help shape learning in infancy by limiting the complexity of interactions between the body and environment, and we use this idea to produce efficient, effective learning in autonomous robots. Our architecture is based on current thinking surrounding the gaze mechanism, and experimentally derived models of stereotypical eye–head gaze contributions. It is built using our proven constraint-based field-mapping approach. We identify stages in the development of infant gaze control, and propose a framework of artificial constraints to shape learning on the robot in a similar manner. We demonstrate the impact these constraints have on learning, and the resulting ability of the robot to make controlled gaze shifts.",{"EN":2678},"A biologically constrained architecture for developmental learning of eye–head gaze control on a humanoid robot",{"VOID":2680},"[\"5331977767506816595\"]",{"VOID":2682},"Aslin, R. N. (1987). Visual and auditory development in infancy. In J. D. Osofsky (Ed.), Handbook of infancy (2nd ed.). New York: Wiley.\nBarnes, G. R. (1979). Vestibulo-ocular function during co-ordinated head and eye movements to acquire visual targets. The Journal of Physiology, 287(1), 127–147.\nBerthouze, L., & Kuniyoshi, Y. (1998). Emergence and categorization of coordinated visual behavior through embodied interaction. Machine Learning, 31, 187–200.\nBirnholz, J. (1981). The development of human fetal eye movement patterns. Science, 213(4508), 679–681.\nBizzi, E., Kalil, R. E., & Tagliasco, V. (1971). 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Australian Computer Society, Inc.: Darlinghurst.",{"VOID":2684},"10.1007\u002Fs10514-013-9335-2","2024-05-16T22:32:06.907+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10514-013-9335-2",[2688,2705,2720],{"id":2689,"sortIndex":23,"researcher":22,"roles":2690,"affiliations":2691,"properties":2700,"displayName":2702,"givenName":22,"familyName":22},"346ed310-7c6d-4dd0-b650-80f8c008b880",[872],[2692],{"id":2693,"sortIndex":23,"affiliation":2694,"properties":22},"0cde12c3-1914-4412-983c-c6327db91276",{"id":2693,"createTime":22,"updateTime":22,"relativeEntities":2695,"slug":22,"properties":2696,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":2699,"statistic":22},[],{"title":2697},{"VI":2698},"Department of Computer Science, Aberystwyth University, Aberystwyth, UK",[],{"title":2701,"gsAuthor":2703},{"VI":2702},"James 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