000			03510nam a22005535i 4500
001			978-1-4471-5049-7
003			DE-He213
005			20200421112224.0
007			cr nn 008mamaa
008			130419s2013 xxk| s |||| 0|eng d
020			_a9781447150497 _9978-1-4471-5049-7
024	7		_a10.1007/978-1-4471-5049-7 _2doi
050		4	_aTJ212-225
072		7	_aTJFM _2bicssc
072		7	_aTEC004000 _2bisacsh
082	0	4	_a629.8 _223
100	1		_aTahirovic, Adnan. _eauthor.
245	1	0	_aPassivity-Based Model Predictive Control for Mobile Vehicle Motion Planning _h[electronic resource] / _cby Adnan Tahirovic, Gianantonio Magnani.
264		1	_aLondon : _bSpringer London : _bImprint: Springer, _c2013.
300			_aXI, 56 p. 20 illus., 17 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		_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
505	0		_aIntroduction -- PB/MPC Navigation Planner -- PB/MPC-RT Planner For Rough Terrains -- Conclusion.
520			_aPassivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: • how to use an MPC optimization framework for the mobile vehicle navigation approach; • how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and  • what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful.
650		0	_aEngineering.
650		0	_aAutomotive engineering.
650		0	_aAerospace engineering.
650		0	_aAstronautics.
650		0	_aControl engineering.
650		0	_aRobotics.
650		0	_aAutomation.
650	1	4	_aEngineering.
650	2	4	_aControl.
650	2	4	_aRobotics and Automation.
650	2	4	_aAutomotive Engineering.
650	2	4	_aAerospace Technology and Astronautics.
700	1		_aMagnani, Gianantonio. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781447150480
830		0	_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4471-5049-7
912			_aZDB-2-ENG
942			_cEBK
999			_c57583 _d57583