1. =
absolute positioning cam.pdf
Achim Lilienthal and Tom Duckett, An Absolute Positioning System for 100 Euros, In Proceedings of the 1st IEEE International Workshop
on Robotic Sensing (ROSE 2003), Orebro, Sweden, 2003
- 2D triangulation
- Fixed web-cameras
- Image processing
- Colored shapes recognition
2. =
location-relative-distributed.pdf - GOOD
Manika Sethia, Priti Mahale, Sonal Sheth, Location determination Algorithms for Distributed Wireless Sensor Networks,
Engineering and Computer Science Department, the University of Texas at Dallas, http://citeseer.ist.psu.edu/689308.html
- Known landmarks
- Cooperation
- Information exchange among neighbors
- Average Distance Estimation
3. =
Sensors and Methods for Mobile Robot Positioning.pdf – GOOD USE
J. Borenstein , H. R. Everett , and L. Feng, “Sensors and Methods for Mobile Robot Positioning”, 1996 Edition of the "Where am I" Report,
University of Michigan, April 1996
http://www.automation.hut.fi/edu/as84145/robotpositioning.pdf
- Sensors and Methods for Mobile Robot Positioning
4. =
UCAM-CL-TR-696.pdf
Oliver J. Woodman, An introduction to inertial navigation, Technical report UCAM-CL-TR-696 (ISSN 1476-2986), University of
Cambridge, Computer Laboratory, August 2007
- INS
- all MEMS sensors
5. =
Navigation GPS IMU.pdf (GPS INS 3D.pdf) – BEST
Iyad Abuhadrous, “Localisation absolue par fusion multi capteurs : GPS – Inertiel – Odométrie” , Thesis, Ecole des Mines de Paris, Jan 2005
Iyad Abuhadrous, “Système embarqué temps réel de localisation et de modélisation 3D par fusion multi-capteur”, Thesis,
Ecole des Mines de Paris, le 14 Janvier 2005
- Kalman for GPS/INS/Odo Fusion
- Unscented Kalman Filter
- Reference frames: body, navigation NED, Earth centered inertial ECI, Earth centered earth fixed ECEF
- World Geodetic System (WGS 84)
- Filters: KF, EKF, UKF
- Conversion de coordonnées géodésiques en coordonnées planes
6.
=
Projection cartographique conique conforme de Lambert, Institut Géographique National, 1ère Ed., Paris 1995.
- Conversion de coordonnées géodésiques en coordonnées planes
7. =
Understanding Map Projections.pdf – BEST
Melita Kennedy, Steve Kopp, Understanding Map Projections, Environmental Systems Research Institute, Inc. 2000, ISBN 1-58948-003-1
8. =
Vector Gravimetry.pdf – BEST
Fathi Y. Dwaik, “INS, GPS And Photogrammetry Integration For Vector Gravimetry Estimation”, Thesis, The Ohio State University, 1998
- Kalman for GPS/INS Fusion
- Stochastic error models: Random constant, Random walk, Markov model
9.
=
Jekeli, C. , “Balloon gravimetry using GPS and INS”, Aerospace and Electronic Systems Magazine, IEEE
Volume 7, Issue 6, Jun 1992 Page(s):9 – 15
- GPS, INS
10. =
KINEMATICS OF MOVING FRAMES.pdf – GOOD USE
“Kinematics of moving frames”, http://ocw.mit.edu/NR/rdonlyres/Mechanical-Engineering/2-154Fall-2004/E62A6DD1-EA3A-4BEA-B414C2741E3E1DFD/0/lec1.pdf
- DCM
- Small rotations
- Rate of change of Euler angles
11. =
GPS dictionary.pdf – USE
“The GPS Dictionary”, http://www.u-blox.com, 8. March 2001
- All gps terminology
12. =
GPS transform.pdf – BEST USE
“Datum Transformations of GPS Positions”, http://www.u-blox.com, 5th July 1999
- Conversion between ECEF, LLA and Local Tangential Plane
- Radius corrections
13. =
GPS basics.pdf – BEST
Jean-Marie Zogg, “GPS Basics”, http://www.u-blox.com, 26/03/2002
- All about GPS
- Position calculation !!!
- Trilateration Errors and DOP explanation !!!
14. =
Global Positioning Systems, Inertial Navigation and Integration.pdf – GOOD
Mohinder S. Grewal, Lawrence R. Weill, Angus P. Andrews, “Global Positioning Systems, Inertial Navigation, and Integration ”, John
Wiley & Sons, Inc. 2001, ISBN: 0-471-20071-9
- GPS, INS, Kalman
- Multipath discussion
- Navigation: equations, errors
15. =
Decca-navigator.pdf
The Decca Navigator - Principles and Performance of the System, the Decca Navigator Company Limited, July 1976
- DECCA navigator
- TDOA, hyperbolic positioning
- Radio signal, Phase difference
16. #
GPS errors.pdf - GOOD
- GPS errors
17. =
INS kalman.pdf – USE
Olivier Bonnet Torres, “Filtrage De Kalman Applique A La Navigation Inertielle”, Thesis, Onera-DCSD, 15 décembre 2003
http://www.cert.fr/dcsd/THESES/obonnet/refdoc/kalmanNav.pdf
-
Kalman, INS
18. =
INS.pdf
Avionique Premiere, Guy Mercier, “La Navigation Inertielle”, http://www2.ac-lille.fr/ciras/CAEA/Cours_CAEA/NAV_INERTIELLE.pdf
- INS functionning and dictionary
19. =
DRONE NAVIGATION.doc – VERY GOOD
Joan Solà, Reconstitution de l'état d'un micro drone par fusion de données, Rapport de DEA, ENSICA 2003
- Puissance de bruit échantillonné
- Capteurs
- Model cinématique
- INS/GPS/Kalman
- Model de mesure
20. =
Fltrage particulaire.pdf
Karim Dahia, "Nouvelles méthodes en filtrage particulaire. Application au recalage de navigation inertielle par mesures altimetriques",
Thesis, Université Joseph Fourier, 04 Janvier 2005
- Kalman INS/GPS/MAG
- Unscented Kalman Filter
- Puissance du bruit echantillonne
- Extension du vecteur d’etat avec les incertitudes des capteurs
21. =
INTEGRATED INS-GPS NAVIGATION.pdf (INS errors.htm)
Mensur Omerbashich, Integrated Ins/Gps Navigation From A Popular Perspective, Journal of Air Transportation Vol. 7, No. 1 – 2002
- INS/GPS integration
- INS error values
22. =
submarine.pdf - GOOD
Laure Fournet, "Positionement d'engins autonomes grand fonds”, Thesis, Ecole Superieur de Geromes et Topographes, le 10 juillet 2002
- Kalman
- submarine location sensors: Sonar, Dopler, INS
23. =
path.pdf
Jay Farrell, Matthew Barth, “Integration of GPS/INS and Magnetic Markers for Advanced Vehicle Control”, University of California,
Berkeley, 2001, http://repositories.cdlib.org/its/path/reports/UCB-ITS-PRR-2001-38
-
GPS/INS/markers
vehicule
24. =
Kalman GPS Motion Sensors.pdf - GOOD
Jon Kronander,”Robust Automotive Positioning: Integration of GPS and Relative Motion Sensors”, Thesis, Linköping University, 2004
- Kalman filter
- GPS
- Vehicule dynamics
25. =
ucge-reports.pdf
Xiaohong Zhang, ”Integration of GPS with A Medium Accuracy IMU for Metre-Level Positioning”, UCGE Reports, Number 20178, June
2003, http://www.geomatics.ucalgary.ca/links/GradTheses.html
- GPS / INS Integration
26. =
Kong2000-PhDThesis.pdf
Xiaoying Kong, Inertial Navigation System Algorithms for Low Cost IMU, Thesis, University Of Sydney, 27 August 2000
- Error models
- GPS/INS/Kalman
27. =
RateSensorAppNote.pdf – GOOD USE
“Theory of Operation of Angular Rate Sensors”, http://www.xbow.com/Support/Support_pdf_files/RateSensorAppNote.pdf
- sensor errors
- MEMS Rate Sensors: Performance of MEMS Rate Sensors, Advantages of MEMS Technology, The Coriolis Force
- Fiber Optic Gyro (FOG) Rate Sensors: The Sagnac Effect, Advantages of FOG Technology
28. =
AngleRandomWalkAppNote.pdf – GOOD USE
“Angle Random Walk”, http://www.xbow.com/Support/Support_pdf_files/AngleRandomWalkAppNote.pdf
- sensor errors
- Converting Angle Random Walk and PSD/FFT Noise Values
29. =
Bias_Stability_Measurement.pdf – GOOD USE
“Bias Stability Measurement: Allan Variance”, http://www.xbow.com/Support/Support_pdf_files/Bias_Stability_Measurement.pdf
- sensor errors
- Allan Variance
30. =
IMUAppNote.pdf – USE
“Measurement of a Vehicle’s Dynamic Motion”, http://www.xbow.com/Support/Support_pdf_files/IMUAppNote.pdf
- INS
- Combine Angular Rate Sensors with Accelerometers
31. =
NAV420AppNote.pdf
Giri Baleri, Sr. Application Engineer, “Datum Transformations of NAV420 Reference Frames”, NAV420CA Application Note, Crossbow
Technology, Inc. http://www.xbow.com/Support/Support_pdf_files/NAV420AppNote.pdf
- Axe transformations
- Reference Frames
32. =
Chapter9.pdf – USE
Henning Umland, A Short Guide to Celestial Navigation, http://www.titulosnauticos.net/astro
- axe transformations
- The Ellipsoid
33. =
krautz.ppt – GOOD USE
Christoph Krautz, Tracking - Overview and Mathematics, http://www14.in.tum.de/konferenzen/Jass04/courses/3/krautz.ppt
- Sensors overview
- Technologies And Mathematics For Object Tracking
- axe transformations
34. =
Attitude Control.pdf – GOOD THESIS
Matthew C. VanDyke, “Decentralized Coordinated Attitude Control of a Formation of Spacecraft”, Thesis, Virginia Polytechnic Institute and
State University, May 21, 2004, Blacksburg, Virginia
-
aerodynamics
axe transformations
equations of motion (INS)
quaternion based control
35. =
ATTITUDE KINEMATICS.pdf – GOOD USE
Phillips, W. F., Hailey, C. E., and Gebert, G. A., A Review of Attitude Kinematics for Aircraft Flight Simulation,
AIAA Simulation and Modelling Conference, Aug. 2000
-
aerodynamics
axe transformations
equations of motion (INS)
Detailed Mathematics for Aircraft Kinematics: rotation matrices and quaternions
36. =
Multi-rate Sensor Fusion for GPS Navigation.pdf – GOOD
David McNeil Mayhew, “Multi-rate Sensor Fusion for GPS Navigation Using Kalman Filtering”, Thesis, Virginia Polytechnic Institute and
State University, Blacksburg, Virginia, May 1999
http://scholar.lib.vt.edu/theses/available/etd-062899-064821/unrestricted/etd.PDF
* error page 22: dynamic equations: dot teta = v*tan(phi)/l
- INS,GPS, kalman filtering
- weight (rule, fuzzy) based fusion
- Rule optimization: manual, hill-climbing, genetic
37. =
maybeck_ch1.pdf – GOOD
Peter S. Maybeck, Stochastic Models, Estimation and Control, volume 1, Academic Press Inc. 1979, ISBN 0-12-480701-1
- Bayesian filtering
38. =
An Introduction to the Kalman Filter.pdf – GOOD
Greg Welch, Gary Bishop, An Introduction to the Kalman Filter, University of North Carolina, 2001,
http://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf
- Kalman filtering
- simple and clear introduction to Kalman filtering
39. #
An introduction to scalar Kalman filters.htm – BEST
The Scalar Kalman Filter
http://www.swarthmore.edu/NatSci/echeeve1/Ref/Kalman/ScalarKalman.html
- simple and clear introduction to Kalman filtering
40. #
The Kalman Filter is Optimal.htm – BEST
Is the Kalman Filter Optimal?
http://www.swarthmore.edu/NatSci/echeeve1/Ref/Kalman/OptSysKalman.html
- simple and clear introduction to Kalman filtering
41. =
connectors-swarmbot-final.pdf
Mondada, F., Bonani, M., Magnenat, S., Guignard, A. and Floreano, D., Physical connections and cooperation in swarm robotics, In
Proceedings of the 8th Conference on Intelligent Autonomous Systems (IAS8), March 10-14, 2004, IOS Press, Amsterdam, NL, pp. 53-60
- cooperative robotics
42. =
cooperative-robots-survey-journal.pdf – GOOD
Y. Uny Cao, Alex S. Fukunaga, Andrew B. Kahng, “Cooperative Mobile Robotics: Antecedents and Directions”, Autonomous Robots, 4, 1–
23 (1997)
http://alexf04.maclisp.org/cooperative-robots-survey-journal.pdf
- cooperative robotics
43. =
iros07.pdf – VERY INTERESTING
Paul M. Maxim, Suranga Hettiarachchi, William M. Spears, Diana Spears, Jerry Hamann, Thomas Kunkel, Fast and Robust Trilateration for
Multi-Robot Tasks, Workshop of Self-Reconfigurable Robots & Systems and Applications, Nov. 2007,
www.cs.uwyo.edu/~wspears/papers/iros07.pdf
-
trilateration
multiple antennas mounted on robot
computation of orientation
44. =
L. E. Parker, Current state of the art in distributed autonomous mobile robotics, in Distributed Autonomous Robotic Systems, vol. 4, pp. 3–
12. Springer, Tokio, 2000
- Cooperative robotics
- Multi-Robot Teams
45. =
Dhillon, B. S., Robot Reliability and Safety. New York: Springer-Verlag, 1991.
- sensor errors
- Robots failures
46. =
hayes02distributed.pdf
Hayes, A. T., Martinoli, A. & Goodman, R. M. (2002) Distributed Odor Source Localisation, Special Issue on Artificial Olfaction, IEEE
Sensors Journal, Vol. 2, No. 3, pp. 260-271
- Distributed search
- Odour source localization
- Odour sensors
47. =
correll04collective.pdf
Correll, N. & Martinoli A. Collective Inspection of Regular Structures using a Swarm of Miniature Robots, Proc. of the Ninth Int. Symp. on
Experimental Robotics ISER-04, Singapore. Springer Tracts in Advanced Robotics 6, Vol. 21, 2006.
- Distributed coverage
- swarm, cooperative robots
- swarm
48. =
Cooperative Localization and Control.pdf - GOOD
J. Spletzer, A. K. Das, R. Fierro, C. J. Taylor,V. Kumar, and J. P. Ostrowski, “Cooperative Localization and Control for Multi-Robot
Manipulation”, Proceedings of the 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems 2001, Volume 2, Issue , 2001
Page(s):631 - 636 vol.2
http://www.cis.upenn.edu/~cjtaylor/publications/IROS2001.pdf
- cooperative robotics
- cooperative localization
- Visual imagery
- Formation keeping
49. =
Multi-Robot Collaboration.pdf – GOOD
Rekleitis, I.M.; Dudek, G.; Milios, E.E. “Multi-robot collaboration for robust exploration”, In Proceedings of the IEEE International
Conference on Robotics and Automation 2000 (ICRA00), Volume 4, 2000, Page(s):3164 - 3169
- cooperative robotics
- cooperative localization
- Reduction of position errors by cooperation (sensing redundancy)
50. =
JP_icra06.pdf
Jim Pugh and Alcherio Martinoli, Localization and Communication Module for Small-Scale Multi-Robot Systems, In Proceedings on 2006
IEEE International Conference on Robotics and Automation, ICRA 2006, pp. 188-193, 15-19 May 2006, Oriando, FL
- cooperative robotics
- cooperative localization
- infrared sensors
- infrared relative localization/communication by signal strength
- approximatied model for signal measurements
51. =
tinos01fault.pdf - GOOD
Renato Tinós, Luis E. Navarro-Serment and Christiaan J.J. Paredis, “Fault Tolerant Localization for Teams of Distributed Robots”, In
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol. 2, pp. 1061-1066, Maui, Hawaii, USA, Oct.
29 - Nov. 03, 2001
- cooperative robotics
- cooperative localization
- distributed robot teams
- ultrasonic distance sensor
- ranging measurement errors
52. =
angular positioning by ultrasonic.pdf - GOOD
Shraga Shoval and Johann Borenstein, “Measurement Of Angular Position Of A Mobile Robot Using Ultrasonic Sensors”, In ANS
Conference on Robotics and Remote Systems, Pittsburgh, PA, April 26-28, 1999.
- Ultrasonic sensors
- Odometry correction by orientation
- phase difference of an ultrasonic wave by two receivers
53. =
IROS2003lamp.pdf – BEST
Bisson, J.; Michaud, F.; Letourneau, D. Relative positioning of mobile robots using ultrasounds, In Proceedings on 2003 IEEE/RSJ
International Conference on Intelligent Robots and Systems, 2003. (IROS 2003). Volume 2, Issue , 27-31 Oct. 2003 Page(s): 1783 - 1788
- cooperative localization
- TDOA, Ultrasound
- LAMP
54. =
lamp.pdf
Frédéric RIVARD , “Localisation relative de robots mobiles opérant en groupe", Mémoire de maîtrise ès sciences appliquées, Sherbrooke
(Québec) Canada Mars 2005
- cooperative localization
- Triangulation, TDOA, Ultrasound
- LAMP
55. =
Determination-of-a-position.pdf
W. Murphy and W. Hereman, Determination of a Position in Three Dimensions Using Trilateration and Approximate Distances, tech. report
MCS-95-07, Colorado School of Mines 1995
- Trilateration
- Multiple beacons TOA
- LSM, Pseudo-inverse
- Only measurement errors, non symmetric system !!!
56. =
Manolakis, D.E, Efficient solution and performance analysis of 3-D position estimation by trilateration, IEEE Transactions on Aerospace
and Electronic Systems, Volume 32, Issue 4, Oct 1996 Page(s):1239 – 1248
- trilateration
- TOF by quadratic equation
- Error analysis
57. =
Manolakis, D.E.; Cox, M.E., “Effect in range difference position estimation due to stations position errors”, IEEE Transactions on Aerospace
and Electronic Systems, Volume 34, Issue 1, Jan 1998 Page(s):329 – 334
- TDOA by iteration
- Error analysis
- effects of beacon errors
58. =
urn_nbn_se_liu_diva.pdf - GOOD
Martin Alkeryd , Evaluation of Position Sensing Techniques for an Unmanned Aerial Vehicle, tech. report LITH-ISY-EX--06/3790--SE,
Department of Electrical Engineering, Linköpings university, Sweden, 2006
-
Trilateration for in-door by ultrasound
Very detailed distance measurement expression
GDOP
59. =
ECMR07_0073.pdf
Alexander Bahr, John J. Leonard, Minimizing Trilateration Errors in the Presence of Uncertain Landmark Positions, In Proceedings of the
3rd European Conference on Mobile Robots , Freiburg, Germany, September 2007
- Localisation under-water
- TOF
- Error analysis by error-propagation
- Beacon errors
60. =
roumeliotis03analysis.pdf
S. I. Roumeliotis and I. Rekleitis, “Analysis of multirobot localization uncertainty propagation,” in Proc. IEEE Int. Workshop on Intelligent
Robots and Systems, Las Vegas, NV, USA, 2003, pp. 1763–1770.
- cooperative localization for the case of large groups of mobile robots
- odometric measure of linear and rotational velocity
- proprioceptive and exteroceptive sensing capabilities
- Gaussian errors
61. =
A. Easton and S. Cameron, A gaussian error model for triangulation based pose estimation using noisy landmarks, in Proc. IEEE
International Conference on Robotics, Automation and Mechatronics, Bangkok, Thailand, 2006
- Trilateration
- Gaussian error model for beacon positions !!!
- As multiple robot approaches to localization become more prevalent, existing triangulation methods involving fixed location landmarks are
inadequate to accurately determine a robot's pose. We present an error model for a robot's pose based on triangulation from three landmarks.
The model represents each landmark position as a Gaussian distribution and, consequently, factors landmark positional uncertainty into robot
pose error. We demonstrate the performance and accuracy of this model through a series of experiments and use the results to explain some
of the inconsistencies in earlier results. We also present four metrics for analyzing the output of any Gaussian-based localization error model,
demonstrating the metrics' particular applicability to multiple robot localization problems
62. =
Sci10-r8-tra-trilateracio.pdf
Federico Thomas and Lluís Ros, Revisiting Trilateration for Robot Localization, IEEE Transactions on Robotics, Volume 21, Issue 1, Feb.
2005 Page(s): 93 – 101
- TOF
- Error analysis
- CAYLEY-MENGER DETERMINANT for computation of dilution of precision
63. =
Jon Dattorro, Convex Optimization & Euclidean Distance Geometry, Meboo Publishing USA 2005, ISBN 0976401304
-
CAYLEY-MENGER DETERMINANT
Semi definite programming
64. =
hybrid_trilateration.pdf
Kong-Woo Lee, Jae-Byung Park and Beom-Hee Lee, Dynamic localization with hybrid trilateration for mobile robots in intelligent space,
Intelligent Service Robotics, Springer Berlin / Heidelberg, Volume 1, Number 3 / July, 2008, Pages: 221-235
-
Trilateration
Hybrid navigation
CAYLEY-MENGER DETERMINANT
In this paper, we propose a new localization algorithm based on a hybrid trilateration algorithm for obtaining an accurate position
of a robot in intelligent space. The proposed algorithm is also able to estimate a position of the moving robot by using the
extended Kalman filter, taking into consideration time synchronization and velocity of the robot. For realizing the localization
system, we employ several smart sensors as beacons on the ceiling in intelligent space and as a listener attached to the robot.
Finally, simulation results show the feasibility and effectiveness of the proposed localization algorithm compared with existing
trilateration algorithms.
65. =
iros02pdf.pdf
R. Kurazume, S. Hirose, and S. Nagata, Study on cooperative positioning system, in Proc. IEEE Int. Conf. Robotics and Automation,
Minneapolis, MN, USA, 1996, pp. 1421–1426.
- Multi-robot cooperation for localization
- Triangulation by camera, vision
66. =
10.1.1.40.3857.pdf
Wolfram Burgard, Dieter Fox, Mark Moors, Reid Simmons, and Sebastian Thrun. Collaborative multirobot exploration. In
Proc. of the IEEE Int. Conf. on Robotics & Automation, pages 476-481, 2000
-
cooperative robotics
exploration, map building
67. =
grabowski01localization.pdf
Grabowski, R.; Khosla, P. , Localization techniques for a team of small robots, Proceedings of the 2001 IEEE/RSJ International Conference
on Intelligent Robots and Systems Volume 2, Issue , 2001 Page(s):1067 - 1072 vol.2
- cooperative localization
- Maximum likelihood estimation
- Using multiple robots to enhance the precision – measurement of the range between robot pairs by array of ultrasonic sensors mounted on
each robot
- one emission- multiple receptions !!!
- detection of ultrasonic burst by threshold
- a lot of computations, but the system can detect outliners !!
68. =
jensfelt99active.pdf
Jensfelt, P.; Kristensen, S. , Active global localization for a mobile robot using multiple hypothesis tracking, IEEE Transactions on Robotics
and Automation, Volume 17, Issue 5, Oct 2001 Page(s):748 – 760
- Multi-Hypothesis Kalman Filter – each possible robot position is regarded as a hypothesis characterized by a Gaussian
- Building a World Model – each observed feature is changing the hypothesis evidences
- Odometry + Vision
69. =
aatique.pdf - GOOD THESIS
Muhammad Aatique , Evaluation of TDOA techniques for position location in CDMA systems, Thesis, Virginia Polytechnic Institute,
September 1997, Blacksburg, Virginia
http://scholar.lib.vt.edu/theses/available/etd-82597-03345/unrestricted/aatique.pdf
Foy, W.H. “Position-Location Solutions by Taylor-Series Estimation”, IEEE Transactions on Aerospace and Electronic Systems, Volume
AES-12, Issue 2, March 1976 Page(s):187 – 194
- Taylor series ITERATION solution DTOA, TOF
- CDMA - Code Division Multiple Access
- TDOA
- Absolute positioning
70. =
55_5.pdf - GOOD
Xiuzhen Cheng; Thaeler, A.; Guoliang Xue; Dechang Chen, TPS: A Time-Based Positioning Scheme for Outdoor Wireless Sensor
Networks, INFOCOM 2004. Twenty-third Annual Joint Conference of the IEEE Computer and Communications Societies, Volume 4, Issue ,
7-11 March 2004 Page(s): 2685 - 2696
- TDOA (TPS), RF signal => range differences are averaged over multiple beacon intervals before they are combined to estimate the
sensor location through trilateration
- Wireless sensor networks
- Thousands of collaborative sensors
- Without linearization => trough quadratic equation
- Error analysis
71. =
oskarsson98accurate.pdf
M. Oskarsson and K. Astrom, Accurate and automatic surveying of beacon positions for a laser guided vehicle, In Proc. Symposium on
Image Analysis, Uppsala, Sweden, 1998.
- autonomous vehicule
- laser scanner, bearing measurements, odometry
- reflection beacons
- maximum likelihood estimation
- building a beacon map
72. =
tercero99continuous.pdf
Salido, J., Paredis, C. J. J., and Khosla, P. K. 1999, Continuous Probabilistic Mapping by Autonomous Robots, the Sixth International
Symposium on Experimental Robotics 1999, Pages: 275 – 286
- continuous probabilistic mapping (CPM)
- map building by cooperating robots
- range sensing data from multiple robots
- fast and simple, designed for small robots
- grid map
- Bayesian rules for updating map cells by sensing and occupancy estimation
73. =
Fusion.pdf
Rao, Nageswara S. V. Xiaochun Xu, Sartaj Sahni, “A computational geometry method for DTOA triangulation”, 10th International
Conference on Information Fusion, 2007 , 9-12 July 2007, On page(s): 1-7
-
Geometrical TDOA
Only 3 beacons
74. =
Pages-zamora02evaluation.pdf - GOOD
Pages-Zamora, A.; Vidal, J., Evaluation of the improvement in the position estimate accuracy of UMTS mobiles with hybrid positioning
techniques, IEEE 55th Vehicular Technology Conference 2002, Volume 4, 2002, Page(s): 1631 – 1635
- Root Mean Square (RMS) analysis!!!
- Best Linear Unbiased Estimate (BLUE) = LSM
- Hybrid system: ToA/AoA
75. =
borenstein97mobile.pdf
J. Borenstein, H.R. Everett, L. Feng, and D. Wehe, Mobile Robot Positioning: Sensors and Techniques, Invited paper for the Journal of
Robotic Systems, Special Issue on Mobile Robots. Vol. 14 No. 4, pp. 231 – 249.
- sensors for mobile robots
76. =
MEMS_GPS_IMU.pdf
Xiaoji Niu and Naser El-Sheimy, Improving MEMS IMU/GPS Systems for Accurate Land-Based Navigation Applications, The Institute of
Navigation National Technical Meeting (ION NTM 2006), Monterey, California, USA, January 18-20, pp. 523-529
- Hardware implementation
- INS/GPS/Kalman
77. =
Localization for Wireless Sensor.pdf
G. S. Paschos, E. D. Vagenas and S. A. Kotsopoulos, Real-time Localization for Wireless Sensor Networks with multiple beacon
transmissions, Fifth International Network Conference 2005 - Samos, July 2005
- Trilateration by Power levels
78. =
bucher.pdf – GOOD
RALPH BUCHER and D. MISRA , A Synthesizable VHDL Model of the Exact Solution for Three-dimensional Hyperbolic Positioning
System, VLSI Design, 2002 Vol. 15 (2), pp. 507–520
- DTOA
- Hardware implementation
79. =
FMorgado_IAICSIC_WAC2004_ISIAC073_6pages.pdf
Morgado, F. Jimenez, A.R. Seco, F., Ultrasound-based 3d-coordinate measuring system for localization of findings in paleoarchacological excavations, World Automation Congress, 2004. Proceedings, Volume 16, Issue , 28 June - 1 July 2004 Page(s): 216 - 222
- ultrasound TOF
- radio synchronization
80. #
absolute_20localization_1_.pdf
A.R. Jimenez, F. Seco, R. Ceres and L. Calderon, Absolute Localization using Active Beacons: A survey and IAI-CSIC contributions,
- absolute localization
- TDOA by LSM !!! => singularities
81. #
Robot Localization.pdf - GOOD REPORT
Robot Localization using Ultra-Sonic and Radio Frequency Signals
- TOF, radio synchronic
- Analogue design: Amplifier with integrated clamper, Band pass filter, Full wave rectifier
- FPGA/VHDL implementation
82. =
reza00data.pdf - GOOD THESIS
Rahman I. Reza , Data Fusion For Improved TOA/TDOA Position Determination in Wireless Systems, Thesis, Virginia Polytechnic Institute
and State University, July 2000, Blacksburg, Virginia
- TOF, TDOA
- Normal errors
- Circular Error Probability (CEP)
- GDOP
- data fusion
83. =
kleeman92optimal.pdf
Kleeman, L., Optimal estimation of position and heading for mobile robots using ultrasonic beacons and dead-reckoning, IEEE International
Conference on Robotics and Automation, 12-14 May 1992, vol.3, Page(s):2582 – 2587
- Kalman for dead-reckoning odometry + active ultrasonic beacons
84. =
RK4_02.pdf
M. Schwing, Méthode de Runge-Kutta RK4, IUFM de Lorraine, www.ac-nancy-metz.fr/enseign/physique/divers/MethodNum/Schwdoc/RK4_02.pdf
- Runge-Kutta
85. =
WGS84.pdf
David T. Sandwell, Reference Earth Model - WGS84, 2002, http://topex.ucsd.edu/geodynamics/14gravity1_2.pdf
- Earth model WGS84
86. =
determinant.pdf
Brent M. Dingle, Calculating Determinants of Symbolic and Numeric Matrices, Technical Report, Texas A&M University, May 2004,
November 2005
- Determinants
87. =
cartesian_to_geodetic.pdf – BEST USE
Robert Burtch, A Comparison of Methods Used In Rectangular To Geodetic Coordinate Transformations, ACSM Annual Conference and
Technology Exhibition, Orlando, FL, April 21-26, 2006
- Earth model
- Geodetic conversions
88. =
acoustic-ranging.pdf - GOOD
Lewis Girod, Development and Characterization of an Acoustic Range Finder, In submission to the 2000 International Symposium on
Wearable Computers, 31 March 2000
- Acoustic time of flight
- RF synchronization
- Trilateration
- Spread Spectrum Modulation
- Modulations: Frequency Sweep, Frequency Hopping, and Direct Sequence.
- D/A Converter >> Amplifier >> Emitter >> Receiver >> Preamp >> A/D Converter.
- Adaptive noise threshold
- low-cross correlation sequences
89. =
paper35.pdf
Borenstein J., Control and kinematic design of multi-degree-of-freedom mobile robot with compliant linkage,
IEEE Transactions on Robotics and Automation, Vol. 11, No. 1, February 1995, pp. 21-35.
- Multi-degree-of-freedom vehicles
- Compliant linkage
- Cooperation
90. #
Ultrasound.pdf
Ultrasound in Diagnostic And Therapy, http://www.anst.uu.se/hanslund/Med_Tekn/ultrasound.pdf
- ultrasound
91. #
acoustics.pdf
Overview of Underwater Acoustics, http://ocw.mit.edu/NR/rdonlyres/Earth--Atmospheric--and-Planetary-Sciences/12-097January--IAP-2006/CB9129FF-EC92-4975-84F0-62ECA735FBFC/0/acoustics.pdf
92. #
The speed and attenuation of sound 2_4_1.htm
The speed and attenuation of sound, http://www.kayelaby.npl.co.uk/general_physics/2_4/2_4_1.html
93. =
li03.pdf - GOOD
Dan Li and Yu Hen Hu, Energy Based Collaborative Source Localization Using Acoustic Micro-Sensor Array, J. EUROSIP Applied Signal
Processing, vol. 2003, no. 4, pp. 321-337, 2003.
- collaborative source localization based on acoustic signatures
- To enable acoustic source localization, two approaches have been well developed: 1) For a coherent, narrow band source, the phase
difference measured at receiving sensors can be exploited to estimate the bearing direction of the source. For broadband source, time-delayed
estimation has been quite popular. 2) In free space, acoustic energy decays at a rate that is inversely proportional to the distance from the
source. Therefore, if we take simultaneous acoustic energy measurements emitted from an omni-directional acoustic source at different,
known sensor locations.
94. #
SoundAttenuation.pdf
Silex innovations inc., “Sound Attenuation”, 4/5/2000, http://silex.com/pdfs/Sound%20Attenuation.pdf
- general notions in sound
- sound absorption
95. #
b.pdf
- Sound absorption
96. =
D. R. Yoerger, M. Jakuba, A. M. Bradley, B. Bingham, Techniques for Deep Sea Near Bottom Survey Using an Autonomous Underwater
Vehicule, International Journal Of Robotics Research Vol. 26, No. 1, Jan 2007, pp. 41-54
- long baseline acoustic
- ultrasound resonator anchors
97. #
ISR - IS Robotics, Inc., RR-1/BS-1 System for Communications and Positioning. Preliminary
Data Sheet, IS Robotics, Twin City Office Center, Suite 6, 22 McGrath Highway, Somerville,
MA 02143, 617-629-0055
98. =
Kim, J.; Jee, G.-I.; Lee, J.G. , A complete GPS/INS integration technique using GPS carrier phasemeasurements, Position Location and
Navigation Symposium, IEEE 1998, 20-23 Apr 1998 Page(s):526 – 533
-
GPS/INS
For its synergistic relationship, the integrated GPS/INS system has been adopted in many navigation systems. In this paper, a
complete GPS/INS integration technique utilizing GPS carrier phase measurements is proposed. A measurement model of doubledifferenced GPS carrier phase measurements is newly derived in order to be used with the INS error model. Also, an algorithm is
suggested to resolve the integer ambiguities of GPS carrier phase measurements by employing MS information. From simulation
results, performance of the suggested technique is verified
99. =
K. Ohno, T. Tsubouchi, B. Shigematsu, S. Yuta, Differential GPS and odometry-based outdoor navigation of a mobile robot, Advanced
Robotics, Volume 18, Issue - 6, Page(s): 611- 635
-
odometry/ D-GPS
outdoor
This paper demonstrates a reliable navigation of a mobile robot in outdoor environment. We fuse differential GPS and odometry
data using the framework of extended Kalman filter to localize a mobile robot. And also, we propose an algorithm to detect curbs
through the laser range finder. An important feature of road environment is the existence of curbs. The mobile robot builds the
map of the curbs of roads and the map is used for tracking and localization. The navigation system for the mobile robot consists
of a mobile robot and a control station. The mobile robot sends the image data from a camera to the control station. The control
station receives and displays the image data and the teleoperator commands the mobile robot based on the image data. Since the
image data does not contain enough data for reliable navigation, a hybrid strategy for reliable mobile robot in outdoor
environment is suggested. When the mobile robot is faced with unexpected obstacles or the situation that, if it follows the
command, it can happen to collide, it sends a warning message to the teleoperator and changes the mode from teleoperated to
autonomous to avoid the obstacles by itself. After avoiding the obstacles or the collision situation, the mode of the mobile robot is
returned to teleoperated mode. We have been able to confirm that the appropriate change of navigation mode can help the
teleoperator perform reliable navigation in outdoor environment through experiments in the road
100. =
Stella, E.; Lovergine, F.P.; Caponetti, L.; Distante, A., Mobile robot navigation using vision and odometry, Proceedings of the Intelligent
Vehicles '94 Symposium, 24-26 Oct. 1994 Page(s): 417 – 422
-
vision/odometry
A cooperation strategy between odometry and a novel visual self-location method for indoor autonomous robot navigation is
described. The strategy exploits the a-priori knowledge about the path that the vehicle is going to run, whether to model the
odometer uncertainity in order to a-priori define the odometer re-calibration position or to verify the experimental uncertainity
following the one modelled in order to update the re-calibration position. The visual self-location method capitalizes on the
excellent angular resolution of CCD TV cameras in order to estimate the camera position and orientation and, therefore, the
vehicle position
101. =
D. C. Montgomery, G. C. Runger, Applied Statistics and Probability for Engineers, Third Edition, John Wiley & Sons, Inc. 2003, ISBN 0471-20454-4
102. @
T.T. Soong, Fundamentals Of Probability And Statistics For Engineers, John Wiley & Sons, Inc. 2004, ISBN 0-470-86814-7
103. =
Charles M. Grinstead, J. Laurie Snell, Introduction to Probability, 2ed, American Mathematical Society 1997, ISBN 0821807498
104. =
inertial_navigation_introduction.pdf
A. D. King, Inertial Navigation – Forty Years of Evolution, GEC REVIEW, VOL. 13, NO. 3, 1998,
http://www.imar-navigation.de/download/inertial_navigation_introduction.pdf
105. =
tgfsr3.pdf
Makoto Matsumoto , Yoshiharu Kurita, Twisted GFSR generators, ACM Transactions on Modeling and Computer Simulation (TOMACS),
v.2 n.3, p.179-194, July 1992
- GFSR
106. =
random.pdf
Richard Saucier, "Computer Generation of Statistical Distributions", Army Research Laboratory, ARL-TR-2168, March 2000
http://ftp.arl.mil/random/random.pdf
107. =
G. E. P. Box and Mervin E. Muller, “A Note on the Generation of Random Normal Deviates”, The Annals of Mathematical Statistics (1958),
Vol. 29, No. 2 pp. 610-611
108. =
boxmuller.pdf
Takashi Shinzato, "Box Muller Method", January 27, 2007
http://www.sp.dis.titech.ac.jp/~shinzato/boxmuller.pdf
109. @
Robert G. Brown, Patrick Y.C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, second edition, ISBN 0471-52573-1,
John Wiley & Sons, 1992
110. =
K. V. Ramachandra, Kalman Filtering Techniques for Radar Tracking, Marcel Dekker, 2000, ISBN: 0-8247-9322-6
111. =
Jackie Neider, Tom Davis, and Mason Woo, Redbook - The Official Guide to Learning OpenGL, second edition, ISBN 0-201-63274-8,
Silicon Graphics, Inc, 1994
112. =
OpenGL Architecture Review Board, “OpenGL Reference Manual”, ISBN 0-201-63276-4, Silicon Graphics, Inc, 1994
113. #
GPS - NMEA sentence information.html
GPS - NMEA sentence information, http://aprs.gids.nl/nmea/
114. #
nmea data.html
NMEA data, http://www.gpsinformation.org/dale/nmea.htm
115. =
E.D. Kaplan. Understanding GPS: Principles and Applications. Artech House, 1996, ISBN 0-89006-793-7
- GPS
116. =
arrasTRerror.pdf
Kai Oliver Arras, An Introduction To Error Propagation: Derivation, Meaning and Examples, Technical Report Nº EPFL-ASL-TR-98-01 R3
of the Autonomous Systems Lab, Institute of Robotic Systems, Swiss Federal Institute of Technology Lausanne (EPFL), September 1998
http://www.informatik.uni-freiburg.de/~arras/papers/arrasTRerror.pdf
- error propagation
- When is the Approximation a Good One
117. =
200412423.pdf – BEST ULTRASOUND, GOOD TESIS
Esko Dijk, Kees van Berkel, Ronald Aarts and Evert van Loenen ,Ultrasonic 3D Position Estimation Using a Single Base Station, Lecture
Notes in Computer Science, Ambient Intelligence Volume 2875/2003, Pages 133-148
-
ultrasonic TOF
indoor localization
acoustics => reflections, attenuation, speed
acoustic reflections as beacons
118. =
Millibots.pdf
Navarro-Serment, L.E.; Grabowski, R.; Paredis, C.J.J.; Khosla, P.K., Millibots, IEEE Robotics & Automation Magazine, Volume 9, Issue 4,
Dec 2002 Page(s): 31 – 40
-Multi-robot cooperation
-Acoustic TOF to static robots used as beacons => Leap-Frogging
119. #
triang.pdf
Eduardo Z. Casanova, Salvador D. Quijada, Jaime G. Garcia-Bermejo, Jose R. Peran Gonzalez, A New Beacon-based System for the
Localization of Moving Objects, http:// citeseer.ist.psu.edu/607313.html
- Laser triangulation
- Hardware implementation
120. =
http://mathworld.wolfram.com/
Mathworld, http://mathworld.wolfram.com/
121. =
http://www.wikipedia.org/
Wikipedia, http://www.wikipedia.org/
122. #
http://hyperphysics.phy-astr.gsu.edu/hbase/hframe.html
C. R. Nave, “HyperPhysics Concepts”, 2005, http://hyperphysics.phy-astr.gsu.edu/hbase/hframe.html
123. =
Numerical Optimization.pdf - GOOD
Jorge Nocedal, Stephen J. Wright, Numerical Optimization, 1999 Springer-Verlag New York, Inc. , ISBN 0-387-98793-2
- optimization
124. =
Numerical Optimization - Theoretical and Practical Aspects.pdf
J. Frédéric Bonnans, J. Charles Gilbert, Claude Lemaréchal, Claudia A. Sagastizábal, Numerical Optimization - Theoretical and Practical
Aspects, Second Edition, 2006 Springer-Verlag Berlin Heidelberg New York, ISBN: 3-540-35445-X
- optimization
125. =
Practical Methods of Optimization, Vol.1.djvu
R. Fletcher, Practical Methods of Optimization - Unconstrained Optimization, Vol 1, John Wiley & Sons 1980, ISBN 0 471 27711 8
- optimization
126. =
Practical Methods of Optimization, Vol.2.djvu
R. Fletcher, Practical Methods of Optimization - Constrained Optimization, Vol 2, John Wiley & Sons 1981, ISBN 0 471 27828 9
- optimization
127. =
Linear programming.pdf
Robert J. Vanderbei, Linear Programming: Foundations and Extensions, Springer 1996, ISBN 0792398041
- optimization
- linear programming
- simplex method
128. #
Practical Analog and Digital Filter Design.pdf
Les Thede, Practical Analog and Digital Filter Design, Artech House, Inc. 2004, ISBN 1-580-53915-9
- filtering
129. #
digital_filter_design.pdf
Steve Winder, Analog And Digital Filter Design, Second Edition, 2002, Elsevier Science (USA), ISBN 0-7506-7547-0
- filtering
130. =
asymptot_elliptic.pdf
D. Karp and S.M. Sitnik, Asymptotic approximations for the first incomplete elliptic integral near logarithmic singularity, Journal of
Computational and Applied Mathematics, vol. 205, no. 1, pp. 186-206
- asymptotic approximation for uncomplete elliptic integrals
=> very complex
131. =
Whittaker, E. T. and Watson, G. N. A Course in Modern Analysis, 4th ed., Cambridge University Press, 1996, ISBN 0-5215-8807-3
- elliptic functions
132. =
David Kincaid, Cheney Ward, Numerical Analysis (3rd edition). Brooks/Cole 2002, ISBN 0-534-38905-8
- interpolation
133. =
Michelle Schatzman. Numerical Analysis: A Mathematical Introduction. Clarendon Press 2002, Oxford, ISBN 0-19-850279-6
- interpolation
134. =
Motion planning.pdf –GOOD MOTION PLANNING
Vladimir L. Lumelsky, Sensing, intelligence motion: how robots and humans move in an unstructured world, John Wiley & Sons, Inc, 2006,
ISBN 0-471-70740-6
- motion planning and collision avoidance is perhaps the most universal robotic problem. It is also the most “robotic” robotic problem:
Whereas other issues and techniques are common to other areas of sciences and engineering, collision avoidance—especially its branch that
deals with partial input information (such as from sensors)—is the monopoly of robotics.
• Motion planning with complete information, also called in literature the Piano Mover’s model or off-line planning approach. Here the path
is computed all at once before the motion starts; in principle, an optimal path can be found in this way.
• Motion planning with incomplete information, also called sensor-based motion planning or on-line motion planning, or path planning with
uncertainty, or the Sensing–Intelligence–Motion (SIM) paradigm. Here the decision-making is done continuously as the robot moves along,
based on on-line information, such as from sensors. By its very nature, an optimal solution is ruled out in this formulation.
- Provable Versus Heuristic Algorithms. Another important distinction between algorithms is between provable (other terms: non-heuristic,
exact, algorithmic) and heuristic approaches. A provable motion planning algorithm is one for which there is a guarantee that if a path
between the starting and target points exist, the algorithm will find one in finite time and without an exhaustive search—or else will conclude
in finite time that there is no path if such is the case. We then say that the algorithm converges. To obtain such a guarantee, people go
through the trouble of proving the algorithm convergence. An algorithm itself should allow such a proof; for example, the so-called
“common sense” strategies—we call them heuristic algorithms—do not allow a proof of convergence and are not likely to be convergent.
- The main difficulty is not in proving existence of algorithms that would guarantee a solution (they obviously exist), but in assessing the
problem complexity and obtaining a computationally efficient procedure. Reaching a solution means either finding a path or concluding in
finite time that no path exists.
- In the configuration space the robot shrinks to a point, whereas the surrounding objects—we call them obstacles—are grown accordingly,
to compensate for the shrinking robot.
- In the algorithms working with polygonial obstacles the computational complexity is proportianal to the number of nodes !!!
- in 1736 Euler proposed and solved a problem that was to become famous under the name Koenigsberg Bridge Problem. This work marked
the beginning of two new mathematical disciplines, graph theory and topology. It also gives an important insight into the robot motion
planning problem…
135. =
J. Schwartz, J. Hopcroft, M. Sharir, eds. Planning, Geometry, and Complexity. Robot Motion Aspects, Ablex Publishing
Corporation, Norwood, NJ, 1986, ISBN: 0893913618
- Motion planing
- A good survey of the work on provable algorithms for the Piano Mover’s problem
136. =
D. Lipski and F. Preparata. Segments, rectangles, contours. Journal of Algorithms, Volume 2, Pages: 63–76, 1981
- Motion planing
- specialized maze search algorithms
137. =
J. Reif. Complexity of the Mover’s Problem and generalizations. In Proceedings, 20th Symposium of the Foundations of
Computer Science, 1979
- Motion planning
- Complexity of Piano Mover’s problem
138. =
J. Schwartz and M. Sharir. On the Piano Mover’s problem. II. General techniques for computing topological properties of
real algebraic manifolds. Advances in Applied Mathematics, Volume 4, Pages: 298–351, 1983
- Motion planning
- Complexity of Piano Mover’s problem
139. @
De-Shuang Huang, Laurent Heutte and Marco Loog, Artificial Potential Field Based Path Planning for Mobile Robots Using
Virtual Water-Flow Method, Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary
Intelligent Computing Techniques, Volume 2, Pages: 588-595, Springer Berlin Heidelberg 2007
- motion planing
- potential field
140. =
corridor.pdf – VERY GOOD MOTION PLANNING
Roland Geraerts and Mark H. Overmars, The Corridor Map Method: Real-Time High-Quality Path Planning, IEEE
International Conference on Robotics and Automation, Roma, Italy, 10-14 April 2007
- motion planing
- The Corridor Map Method
- local minimum of the potential field !!!
- many redundant motions of the PRM!!!
141. =
2004-062.pdf
A. Kamphuis and M. Overmars, Finding paths for coherent groups using clearance, in Eurographics/ ACM SIGGRAPH
Symposium on Computer Animation, 2004, pp. 19-28.
- motion planing
- The Corridor Map Method
- Another widely used technique is grid searching in which the environment is divided into a grid that can be searched for a free path using
A* like approaches. At each grid point, the robot is allowed to move to adjacent grid points as long as the line between them is completely
contained within Cfree.
- A* (pronounced "A star") incrementally searches all routes leading from the starting point until it finds the shortest path to a goal.
142. =
Hart, P. E.; Nilsson, N. J.; Raphael, B. A Formal Basis for the Heuristic Determination of Minimum Cost Paths, IEEE
Transactions on Systems Science and Cybernetics SSC4 (2): 100–107, 1968
- A* path finding algorithms
143. =
10.1.1.89.3090.pdf
Dechter, Rina; Judea Pearl, Generalized best-first search strategies and the optimality of A*, Journal of the ACM, Volume
32, Pages: 505–536, 1985
- A* path finding algorithms
144. @
E. Rimon and D. Koditschek, Exact robot navigation using artificial potential fields, IEEE Transactions on Robotics and
Automation, vol. 8, pp. 501.518, 1992
- motion planing
- potential field
145. =
O. Khatib, Real-time obstacle avoidance for manipulators and mobile robots, International Journal of Robotics Research, vol.
5, pp. 90-98, 1986.
- motion planing
- potential field
146.
phd-bell05.pdf
Graeme Bell, Forward Chaining for Potential Field Based Navigation, thesis, UNIVERSITY OF ST ANDREWS, September
2005
- motion planing
- potential field
147. =
10.1.1.19.1454.pdf
M. Overmars, A random approach to motion planning, Utrecht University, Tech. Rep. RUU-CS-92-32, 1992.
- motion planing
- Probabilistic Roadmap Method (PRM)
148. =
barraquand1997rand-sample-scheme-journal.pdf
J. Barraquand, L. Kavraki, J.-C. Latombe, T.-Y. Li, R. Motwani, and P. Raghavan, A random sampling scheme for path
planning, International Journal of Robotics Research, vol. 16, pp. 759-744, 1997.
- motion planing
- Probabilistic Roadmap Method (PRM)
149. =
10.1.1.97.4526.pdf
S. S. Ge and Y. J. Cui, Dynamic Motion Planning for Mobile Robots Using Potential Field Method, Autonomous
Robots, Volume 13, pages 207-222, 2002
- motion planing
- potential field
150. =
Planning Algorithms.pdf – BEST Motion planning
Steven M. LaValle, Planning Algorithms, Cambridge University Press 2006, ISBN 0-521-86205-1
- Motion planning
151. =
pathPlanning.pdf
Luis Gracia, Josep Tornero, Optimal Trajectory Planning for Wheeled Mobile Robots Based on Kinematics Singularity,
Journal of Intelligent and Robotic Systems, Volume 37, Issue 1 (May 2003), Pages: 1 – 19, ISSN: 0921-0296
- Motion planning
- Cost index
- New optimality criterion for motion planning of wheeled mobile robots based on a cost index that assesses the nearness to singularity of
forward and inverse kinematic models
152. =
Diéguez, A. R., Sanz, R., and López, Deliberative On-Line Local Path Planning for Autonomous Mobile Robots, Journal of
Intelligent and Robotic Systems, Volume 53 , Issue 2 (October 2008), Pages: 145 – 168, ISSN:0921-0296
- Motion planning
- Approach is different to other model-based navigation approaches since it integrates both global and local planning processes in the same
architecture while other methods only combine global path planning with a reactive method to avoid non-modelled obstacles. Our local
planning is only triggered when an unexpected obstacle is found and reactive navigation is not able to regain the initial path. A new
trajectory is then calculated on-line using only proximity sensor information.
153. GPS.pdf
Ronald W Phoebus Jr., GPS and Trilateration, 2003
- GPS basics
- Trilateration basics
- Navigation by means of celestial observation, spherical trigonometry, and hand computation had almost reached its present form by the
time of Captain James Cooke’s 1779 voyage to the Hawaiian Islands.
154. =
Wuk Kim; Lee, J.G.; Jee, G.-I., The interior-point method for an optimal treatment of bias in trilateration location, IEEE Transactions on
Vehicular Technology, Volume 55, Issue 4, July 2006 Page(s):1291 – 1301
- trilateration
- bias
- This paper presents a new position-determination estimator for trilateration location. The proposed estimator takes the measurement bias
into consideration and improves the location accuracy of a mobile location system. In case that a mobile station (MS) utilizes signals from a
set of base stations for its location, the computed location is largely affected by nonline-of-sight (NLOS) error in signal propagation. A
constrained optimization method in a three-stage estimation structure is proposed to estimate and eliminate the measurement bias contained
in each pseudorange and mainly caused by the NLOS error. A linear observation model of the bias is formulated, and the interior-point
optimization technique optimally estimates the bias by introducing a feasible range of the measurement bias. It is demonstrated that the new
three-stage estimator successfully computes an accurate location of an MS in a realistic environment setting. The location accuracy of the
proposed estimator is analyzed and compared with the existing methods through mathematical formulations and simulations. The proposed
estimator efficiently mitigates the effect of a measurement bias and shows that the iterated least square (ILS) accuracy of 118 m [67%
distance root-mean-square (DRMS)] can be improved to about 17 m in a typical urban environment
155. =
ToA_multipath.pdf
Y. Qi, H. Kobayashi, and H. Suda, On time-of-arrival positioning in a multipath environment, IEEE Trans. Vehicular
Technology, vol. 55, no. 5, pp. 1516-1526, Sep. 2006.
-
Multipath
Wireless geolocation in a multipath environment is of particular interest for wideband communications. The
conventional approach makes use of first-arriving signals only. In this paper, we investigate whether and under
what conditions processing multipath delays should enhance the positioning accuracy. The best achievable
positioning accuracy is evaluated in terms of the Cramer-Rao low bound (CRLB) and the generalized-CRLB (GCRLB), depending on whether prior statistics of non-line-sight (NLOS) induced errors are available. We then show
that such prior statics are critical to the accuracy improvement when the multipath delays are processed.
Furthermore, the degree of accuracy enhancement depends on two major factors: the strength of multipath
components and the variance of NLOS induced errors. The corresponding positioning receivers are also discussed.
In previous work (Y. Qi and H. Kobayashi, Proc. 2002 IEEE Int. Conf. on Acoustic Speech and Sig. Proc., pp.
2473-2476, 2002, and Proc. IEEE Vehicle Tech. Conf., pp. 285-288, 2002), we developed an analysis of the timeof-arrival (TOA) positioning method in an NLOS environment, assuming single path propagation. The main results
obtained there are extended and applied to the multipath case in this paper
156. =
Wireless_Position_2007.pdf
Sinan Gezici, A Survey on Wireless Position Estimation, In Wireless Personal Communications, Volume 44, Number 3,
Pages 263-282, Springer Netherlands February, 2008
-
trilateration
Received Signal Strength (RSS), Angle of Arival, TDoA, ToA
Mapping Techniques
non-linear least-squares (only measurement errors)
157. =
ToA_Eval_2006.pdf
Izquierdo, F.; Ciurana, M.; Barcelo, F.; Paradells, J.; Zola, E., Performance evaluation of a TOA-based trilateration method to locate
terminals in WLAN, 1st International Symposium on Wireless Pervasive Computing, 16-18 Jan. 2006 Page(s): 1 - 6
-
trilateration
linear and non-linear LSM
no error analysis
-
ToF synchro
Nowadays, several systems are available for outdoor localization, such as GPS, assisted GPS and other systems working on
cellular networks. However, there is no proper location system for indoor scenarios. Research into designing location systems for
802.11 networks is being carried out, so locating mobile devices on global networks (GSM/cellular + GPS + WLAN) finally
seems feasible. The technique presented in this paper uses existing wireless LAN infrastructure with minor changes to provide an
accurate estimation of the location of mobile devices in indoor environments. This technique is based on round-trip time (RTT)
measurements, which are used to estimate distances between the device to be located and WLAN access points. Each RTT
measurement estimates the time elapsed between the RTS (Request-to-Send) and the CTS (Clear-to-Send) frame of the 802.11
standard. By applying trilateration algorithms, an accurate estimation of the mobile position is calculated
-
158. #
Small Robot Sensors.mht – GOOD USE
- sensors for small mobile robots
159. =
Lesson 11 SONAR 1.ppt – GOOD USE
Robert J. Urick, Principles of Underwater Sound, 3rd Edition, McGraw-Hill Inc 1993, ISBN 0-9321-4662-7
-
Ultrasound
Detailed explanations, formulas
underwater applications
160. #
Task_6.2b_2005_Presentation.ppt – GOOD USE
-
Hybrid navigation
Kalman
Particle filters
161. @
http://www.math.sfu.ca/~cbm/aands/
Abramowitz, Milton; Stegun, Irene A., Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, New York:
Dover 1965, ISBN 0-486-61272-4
162. @
Larry Wasserman, All of Statistics-A Concise Course in Statistical Inference, Springer Texts in Statistics, 1st ed. 2004, ISBN: 978-0-38740272-7
163. @
W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical recipes: The art of scientific computing, Cambridge
University Press 2007, ISBN 0521880688
164. @
Mark Balch, Complete digital design, McGraw-Hill, Inc., 2003, ISBN 0-07-143347-3