000 03851nam a22004935i 4500
001 978-1-4471-2957-8
003 DE-He213
005 20140220083236.0
007 cr nn 008mamaa
008 120330s2012 xxk| s |||| 0|eng d
020 _a9781447129578
_9978-1-4471-2957-8
024 7 _a10.1007/978-1-4471-2957-8
_2doi
050 4 _aTJ212-225
072 7 _aTJFM
_2bicssc
072 7 _aTEC004000
_2bisacsh
082 0 4 _a629.8
_223
100 1 _aWang, Yue.
_eauthor.
245 1 0 _aSearch and Classification Using Multiple Autonomous Vehicles
_h[electronic resource] :
_bDecision-Making and Sensor Management /
_cby Yue Wang, Islam I. Hussein.
264 1 _aLondon :
_bSpringer London,
_c2012.
300 _aXVI, 160p. 51 illus., 46 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 _aLecture Notes in Control and Information Sciences,
_x0170-8643 ;
_v427
505 0 _aCoverage Control -- Awareness-based Decision-making Strategy -- Bayesian-based Decision-making Strategy -- Risk-based Sequential Decision-making Strategy -- Risk-based Sensor Management for Integrated Detection and Estimation -- Conclusion and Future Work.
520 _aSearch and Classification Using Multiple Autonomous Vehicles provides a comprehensive study of decision-making strategies for domain search and object classification using multiple autonomous vehicles (MAV) under both deterministic and probabilistic frameworks. It serves as a first discussion of the problem of effective resource allocation using MAV with sensing limitations, i.e., for search and classification missions over large-scale domains, or when there are far more objects to be found and classified than there are autonomous vehicles available. Under such scenarios, search and classification compete for limited sensing resources. This is because search requires vehicle mobility while classification restricts the vehicles to the vicinity of any objects found. The authors develop decision-making strategies to choose between these competing tasks and vehicle-motion-control laws to achieve the proposed management scheme. Deterministic Lyapunov-based, probabilistic Bayesian-based, and risk-based decision-making strategies and sensor-management schemes are created in sequence. Modeling and analysis include rigorous mathematical proofs of the proposed theorems and the practical consideration of limited sensing resources and observation costs. A survey of the well-developed coverage control problem is also provided as a foundation of search algorithms within the overall decision-making strategies. Applications in both underwater sampling and space-situational awareness are investigated in detail. The control strategies proposed in each chapter are followed by illustrative simulation results and analysis. Academic researchers and graduate students from aerospace, robotics, mechanical or electrical engineering backgrounds interested in multi-agent coordination and control, in detection and estimation or in Bayes filtration will find this text of interest.
650 0 _aEngineering.
650 0 _aSystems theory.
650 0 _aAstronautics.
650 1 4 _aEngineering.
650 2 4 _aControl.
650 2 4 _aSystems Theory, Control.
650 2 4 _aRobotics and Automation.
650 2 4 _aAerospace Technology and Astronautics.
700 1 _aHussein, Islam I.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447129561
830 0 _aLecture Notes in Control and Information Sciences,
_x0170-8643 ;
_v427
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-2957-8
912 _aZDB-2-ENG
999 _c100738
_d100738