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001 978-3-319-27425-6
003 DE-He213
005 20200421112043.0
007 cr nn 008mamaa
008 151218s2016 gw | s |||| 0|eng d
020 _a9783319274256
_9978-3-319-27425-6
024 7 _a10.1007/978-3-319-27425-6
_2doi
050 4 _aTJ210.2-211.495
050 4 _aT59.5
072 7 _aTJFM1
_2bicssc
072 7 _aTEC037000
_2bisacsh
072 7 _aTEC004000
_2bisacsh
082 0 4 _a629.892
_223
100 1 _aOyekan, John.
_eauthor.
245 1 0 _aTracking and Mapping of Spatiotemporal Quantities Using Unicellular Swarm Intelligence
_h[electronic resource] :
_bVisualisation of Invisible Hazardous Substances Using Unicellular Swarm Intelligence /
_cby John Oyekan.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aX, 194 p. 128 illus., 75 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aBiosystems & Biorobotics,
_x2195-3562 ;
_v14
505 0 _aIntroduction -- Literature Review -- Investigative Process -- Developing and Implementing a Source Finding Controller -- Relationship between the Berg-Brown Model and the Keller-Segel Model -- Behaviour Based Coverage Controller -- Improvements and towards RealWorld Applications -- Conclusion.
520 _aThe book discusses new algorithms capable of searching for, tracking, mapping and providing a visualization of invisible substances. It reports on the realization of a bacterium-inspired robotic controller that can be used by an agent to search for any environmental spatial function such as temperature or pollution. Using the parameters of a mathematical model, the book shows that it is possible to control the exploration, exploitation and sensitivity of the agent. This feature sets the work apart from the usual method of applying the bacterium behavior to robotic agents. The book also discusses how a computationally tractable multi-agent robotic controller was developed and used to track as well as provide a visual map of a spatio-temporal distribution of a substance. On the one hand, this book provides biologists and ecologists with a basis to perform simulations related to how individual organisms respond to spatio-temporal factors in their environment as well as predict and analyze the behavior of organisms at a population level. On the other hand, it offers robotic engineers practical and fresh insights into the development of computationally tractable algorithms for spatial exploratory and mapping robots. It also allows a more general audience to gain an understanding of the design of computational intelligence algorithms for autonomous physical systems.
650 0 _aEngineering.
650 0 _aMicrobiology.
650 0 _aSystem theory.
650 0 _aComplexity, Computational.
650 0 _aRobotics.
650 0 _aAutomation.
650 0 _aEnvironmental engineering.
650 0 _aBiotechnology.
650 1 4 _aEngineering.
650 2 4 _aRobotics and Automation.
650 2 4 _aComplexity.
650 2 4 _aEnvironmental Engineering/Biotechnology.
650 2 4 _aMicrobiology.
650 2 4 _aComplex Systems.
650 2 4 _aStatistical Physics and Dynamical Systems.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319274232
830 0 _aBiosystems & Biorobotics,
_x2195-3562 ;
_v14
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-27425-6
912 _aZDB-2-ENG
942 _cEBK
999 _c56749
_d56749