Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Identification of probabilistic approaches and map-based navigation in motion planning for mobile robots

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

Robot motion planning (RMP) develops a precise path between start and goal points for mobile robots in an unknown environment. RMP is a complex task when it needs to be planned for a group of robots in a coordinated environment with leader–follower relationship. The planned path might change depending upon the number of robots and the decision made. The decision made by each robot depends on the feedback received based on the subsequent action taken by other robots. In addition, the computational complexities depend upon factors such as communication between robots, the influence of moving obstacles and environment in which they are interacting. In order to explore further in the area of motion planning, it is felt that a comprehensive survey of available literature would support researchers working in RMP and hence the present paper. This paper reviews around 152 articles published in various international journals and conferences with more emphasis on articles published after 1960. In this work, recent activities carried out in the field of path planning for mobile robotics are critically evaluated and problems faced by the researchers are also highlighted. The focus is towards implementation of probabilistic algorithms, including Probabilistic Road Map and Simultaneous Localization and Mapping. Future research prospects in multi-robot path planning based on probabilistic approaches are also discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14

Similar content being viewed by others

References

  1. Nowacki P J 1969 The importance of automatic control. Automatica 5: 541–547

    Article  Google Scholar 

  2. Whiting P D and Hillier J A 1960 A method for finding the shortest route through a road network. Oper. Res. 11(1–2): 37–40

    Article  Google Scholar 

  3. Dreyfus S E 1969 An appraisal of some shortest path algorithms. Oper. Res. 3(17): 395–412

    Article  MATH  Google Scholar 

  4. Pohl I 1970 Heuristic search viewed as path finding in a graph. Artif. Intell. 1: 193–204

    Article  MathSciNet  MATH  Google Scholar 

  5. Fikes R E, Hart P E and Niisson N J 1972 Learning and executing generalized robot plans. Artif. Intell. 3: 251–288

    Article  Google Scholar 

  6. Lee B H and Lee C S G 1987 Collision-free motion planning of two robots. IEEE Trans. Syst. Man Cybern. 17(1): 21–32

    Article  Google Scholar 

  7. Angeles J, Rojas A and Lopez-Cajun C S 1988 Trajectory planning in robotics continuous-path applications. IEEE J. Robot. Autom. 4(4): 380–385

    Article  Google Scholar 

  8. Wu C H and Jou C C 1989 Planning and control of robot orientational path. IEEE Trans. Syst. Man Cybern. 19(5): 1234–1244

    Article  Google Scholar 

  9. Griswold N C 1990 Control for mobile robots in the presence of moving objects. IEEE J. Robot. Autom. 6(7): 263–268

    Article  Google Scholar 

  10. Chien Y and Xue Q 1992 Path planning for two planar robots moving in unknown environment. IEEE Trans. Syst. Man Cybern. 22(2): 307–317

    Article  Google Scholar 

  11. Alexopoulos C and Griffin P M 1992 Path planning for a mobile robot. IEEE Trans. Syst. Man Cybern. 22(2): 318–323

    Article  Google Scholar 

  12. Shan Y and Koren Y 1995 Obstacle accommodation motion planning. IEEE Trans. Robot. Autom. 11(1): 36–49

    Article  Google Scholar 

  13. Kavraki L E, Kolountzakis M N and Latombe J 1998 Analysis of probabilistic roadmaps for path planning. IEEE Trans. Robot. Autom. 14(1): 166–171

    Article  Google Scholar 

  14. Fok K H and Kabuka M R 1992 A flexible multiple mobile robots system. IEEE Trans. Robot. Autom. 8(5): 607–612

    Article  Google Scholar 

  15. Asama H, Arai T, Fukuda T and Hasegawa T 2002 Distributed autonomous robotics systems. Tokoyo: Springer

    Book  Google Scholar 

  16. Sheu P and Xue Q 2002 Intelligent robotic planning systems. In: World Scientific Series in Robotics and Automated Systems, vol. 3

  17. http://engineeronadisk.com/V2/hugh_jack_masters/engineeronadisk-9.html

  18. Dudek G and Jenkin M 2000 Computational principles of mobile robotics. Cambridge University Press, Cambridge, United Kingdom

    MATH  Google Scholar 

  19. Yang W 2008 Autonomous robots research advances. New York: Nova Science

    Google Scholar 

  20. Lavalle S M 2006 Planning algorithms. New York: Cambridge University Press

    Book  MATH  Google Scholar 

  21. Kuffner J J and Lavalle S M 2000 RRT-Connect: an efficient approach to single-query path planning. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 995–1001

  22. Li T and Shie Y 2002 An incremental learning approach to motion planning with roadmap management. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3412–3416

  23. Hsu D, Jiang T, Reif J and Sun Z 2003 The bridge test for sampling narrow passages with probabilistic roadmap planners. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4420–4426

  24. Leven P and Hutchinson S 2000 Toward real-time path planning in changing environments. In: Proceedings of the Workshop on the Algorithmic Foundations of Robotics, pp. 393–406

  25. Song G, Thomas S and Amato N M 2003 A general framework for PRM motion planning. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4445–4450

  26. Akinc M, Bekris K E, Chen B Y, Ladd A M, Plaku E and Kavraki L E 2003 Probabilistic roadmaps of trees for parallel computation of multiple query roadmaps. In: Proceedings of the International Symposium on Robotics Research (ISRR), pp. 80–89

  27. Schwarzer F, Saha M and Latombe J C 2005 Adaptive dynamic collision checking for single and multiple articulated robots in complex environments. IEEE Trans. Robot. 21(3): 338–353

    Article  Google Scholar 

  28. Missiuro P E and Roy N 2006 Adapting probabilistic roadmaps to handle uncertain maps. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1261–1267

  29. Hsu D and Sun Z 2004 Adaptively combining multiple sampling strategies for probabilistic roadmap planning. In: Proceedings of the IEEE Conference on Robotics, Automation and Mechatronics, pp. 774–779

  30. Hause K and Latombe J C 2010 Multi-modal motion planning in non-expansive space. Int. J. Robot. Res. 29: 897–915

    Article  Google Scholar 

  31. Hsu D, Latombe J and Kurniawati H 2005 On the probabilistic foundations of probabilistic roadmap planning. In: Proceedings of the International Symposium on Robotics Research

  32. Bu T, Li Z and Sun Z 2005 Adaptive and relaxed visibility-based PRM. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics, pp. 174–175

  33. Baumann M, Leonard S, Croft E A and Little J J 2010 Path planning for improved visibility using a probabilistic road map. IEEE Trans. Robot. 26(1): 195–200,

    Article  Google Scholar 

  34. Sanchez A, Arenas J A and Zapata R 2002 Non-holonomic path planning using a quasi-random PRM approach. In: Proceedings of the International Conference on Intelligent Robots and Systems, pp. 2305–2310

  35. Branicky M S, Lavalle S M, Olson K and Yang L 2001 Quasi-randomized path planning. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1481–1487

  36. Song G and Amato N M 2001 Randomized motion planning for car-like robots with C-PRM. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and System, pp. 37–42

  37. Sun F, Huang Y, Yuan J and Kang Y 2007 A compound PRM method for path planning of the tractor–trailer mobile robot. In: Proceedings of the IEEE International Conference on Automation and Logistics, pp. 1880–1885

  38. Song G, Miller S and Amato N M 2001 Customizing PRM roadmaps at query time. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1500–1505

  39. Bohlin R and Kavraki L E 2000 Path planning using Lazy PRM. In: Proceedings of the IEEE International Conference on Robotics and Systems, pp. 521–528

  40. Bohlin R and Kavraki L E 2001 A randomized algorithm for robot path planning based on lazy evaluation. In: Handbook on randomized computing. Kluwer, Dordrecht, Netherlands, pp. 221–249

  41. Gupta K and Huang Y 2009 Collision-probability constrained PRM for a manipulator with base pose uncertainty. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1426–1432

  42. Sanchez G and Latombe J C 2001 A single query bi-directional probabilistic roadmap planner with lazy collision checking. In: Proceedings of the International Symposium on Robotics Research, pp. 403–417

  43. Nielsen C L and Kavraki L E 2000 A two level fuzzy PRM for manipulation planning. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1716–1722

  44. Latombe J C 1999 Motion planning: a journey of robots, molecules, digital actors, and other artifacts. Int. J. Robot. Res. 18(11): 1119–1128

    Article  Google Scholar 

  45. Reif J H 1979 Complexity of the mover’s problem and generalizations. In: Proceedings of the 20th Annual Symposium on Foundations of Computer Science, October, pp. 421–427

  46. Kuffner J 1999 Autonomous agents for real-time animation. Ph.D. Dissertation, Department of Computer Science, Stanford University, Stanford, CA, USA

  47. Karamouzas I and Overmars M 2012 Simulating and evaluating the local behavior of small pedestrian groups. IEEE Trans. Visualizat. Comput. Graph. 18(3): 394–406

    Article  Google Scholar 

  48. Kwangjin Y, Gan S K and Sukkarieh S 2013 A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV. Adv. Robot. 27(6): 431–443

    Article  Google Scholar 

  49. Al-Bluwi I, Siméon T and Cortés J 2012 Motion planning algorithms for molecular simulations: a survey. Comput. Sci. Rev. 6(4): 125–143

    Article  MATH  Google Scholar 

  50. Gipson B, Hsu D, Kavraki L E and Latombe J C 2012 Computational models of protein kinematics and dynamics: beyond simulation. Annu. Rev. Anal. Chem. 5: 273–291

    Article  Google Scholar 

  51. Han C S, Law K H, Latombe J C and Kunz J C 2002 A performance-based approach to wheelchair accessible route analysis. Adv. Eng. Informat. 16(1): 53–71

    Article  Google Scholar 

  52. Alterovitz R, Branicky M and Goldberg K 2008 Motion planning under uncertainty for image-guided medical needle steering. Int. J. Robot. Res. 27(11–12): 1361–1374

    Article  MATH  Google Scholar 

  53. Moll M and Kavraki L E 2006 Path planning for deformable linear objects. IEEE Trans. Robot. 22(4): 625–636

    Article  Google Scholar 

  54. Branicky M S, Curtiss M M, Levine J A and Morgan S B 2003 RRTs for nonlinear, discrete, and hybrid planning and control. In: Proceedings of the 42nd IEEE Conference on Decision Control, vol. 1, December, pp. 657–663

  55. Bruce J and Veloso M 2002 Real-time randomized path planning for robot navigation. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, October, pp. 2383–2388

  56. Durrant-Whyte H and Bailey T 2006 Simultaneous localization and mapping (SLAM): part I—the essential algorithms. IEEE Robot. Autom. Mag. 2: 2000–2005

    Google Scholar 

  57. Williams S B 2001 Efficient solutions to autonomous mapping and navigation problems. Ph.D. Thesis, University of Sydney

  58. Dissanayake G, Durrant-Whyte H and Bailey T 2000 A computationally efficient solution to the simultaneous localization and map building (SLAM) problem. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1009–1014

  59. Williams S B, Dissanayake G and Durrant-Whyte H 2002 An efficient approach to the simultaneous localization and mapping problem. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 15(5), pp. 406–411

  60. Zhou H and Sakane S 2007 Mobile robot localization using active sensing based on bayesian network inference. Robot. Auton. Syst. 55: 292–305

    Article  Google Scholar 

  61. Kwak N, Kim G W and Lee B H 2008 A new compensation technique based on analysis of resampling process in Fast SLAM. Robotica 26: 205–217

    Article  Google Scholar 

  62. Lee H C, Lee S H, Choi M H and Lee B H 2012 Probabilistic map merging for multi-robot RBPF-SLAM with unknown initial poses. Robotica 30(2): 205–220

    Article  Google Scholar 

  63. Masson F, Guivant J and Nebot E 2002 Hybrid architecture for simultaneous localization and map building in large outdoor areas. In: Proceedings of the IEEE/RSI International Conference on Intelligent Robots and Systems, pp. 570–575

  64. Guivant J and Nebot E 2001 Optimization of the simultaneous localization and map building algorithm for real time implementation. IEEE Trans. Robot. Autom. 17: 242–257

    Article  Google Scholar 

  65. Simeon T, Laumond J P and Nissoux C 2000 Visibility-based probabilistic roadmaps for motion planning. Adv. Robot. 14(6): 477–494

    Article  Google Scholar 

  66. Eliazar A and Parr R 2003 Dp-slam: fast robust simultaneous localization and mapping without predetermined landmarks. In: Proceedings of the IEEE International conference on Artificial Intelligence, pp. 1135–1142

  67. Milford M, Schulz R, Prasser D, Wyeth G and Wiles J 2007 Learning spatial concepts from RatSLAM representations. Robot. Autonom. Syst. 55: 403–410

    Article  Google Scholar 

  68. Choi K S and Lee S G 2010 Enhanced SLAM for a mobile robot using extended Kalman filter and neural networks. Int. J. Precis. Eng. Manuf. 11(2): 255–264

    Article  Google Scholar 

  69. Tellez R, Ferro F, Mora D, Pinyol D and Faconti D 2008 Autonomous humanoid navigation using laser and odometry data. In: Proceedings of the IEEE-RAS International Conference on Humanoid Robots

  70. Karlsson N, Di Bernardo E, Ostrowski J, Goncalves L, Pirjanian P and Munich M 2005 The VSLAM algorithm for robust localization and mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 24–29

  71. Stasse D O and Yokoi K 2005 Vision-based SLAM for a humanoid robot. In: Proceedings of the International Conference on Robotics and Automation

  72. Guivant J, Nebot E and Durrant-Whyte H 2000 Simultaneous localization and map building using natural features in outdoor environments. In: Proceedings of the Sixth Intelligent Autonomous Systems (IAS), pp. 581–586

  73. Austin D J and McCarragher B J 2001 Geometric constraint identification and mapping for mobile robots. Robot. Autonom. Syst. 35 59–76

    Article  MATH  Google Scholar 

  74. Meyer J A and Filliat D 2003 Map-based navigation in mobile robots: II. A review of map-learning and path-planning strategies. Cognit. Syst. Res. 4: 283–317

    Article  Google Scholar 

  75. Meyer J A and Filliat D 2003 Map-based navigation in mobile robots: I. A review of localization strategies. Cognit. Syst. Res. 4: 243–282

    Article  Google Scholar 

  76. Vasudevan S, Gachter S, Nguyen V and Siegwart R 2007 Cognitive maps for mobile robots—an object based approach. Robot. Autonom. Syst. 55: 359–371

    Article  Google Scholar 

  77. Chrysafiadi K and Virvou M 2013 A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system. SpringerPlus 2: 81

    Article  Google Scholar 

  78. Wijk O and Christensen H I 2000 Localization and navigation of a mobile robot using natural point landmarks extracted from sonar data. Robot. Autonom. Syst. 31: 31–42

    Article  Google Scholar 

  79. Yuan H and Shim T 2013 Model based real-time collision-free motion planning for nonholonomic mobile robots in unknown dynamic environments. Int. J. Precis. Eng. Manuf. 14(3): 359–365

    Article  Google Scholar 

  80. Kuipers B J 2000 The spatial semantic hierarchy. Artif. Intell. 119: 191–233

    Article  MathSciNet  MATH  Google Scholar 

  81. Franz M O and Mallot H A 2000 Biomimetic robot navigation. Robot. Autonom. Syst. 30: 133–153

    Article  Google Scholar 

  82. Hanek R and Khmitt T 2000 Vision-based localization and data fusion in a system of cooperating mobile robots. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1199–1204

  83. Thrun S 2001 A probabilistic online mapping algorithm for teams of mobile robots. Int. J. Robot. Res. 20(5): 335–363

    Article  Google Scholar 

  84. Borges G A and Aldon M J 2003 Robustified estimation algorithms for mobile robot localization based on geometrical environment maps. Robot. Autonom. Syst. 45(3–4): 131–159

    Article  Google Scholar 

  85. Kim Y G, Kwak J H, Hong D H, Kim I H and Shin D H 2012 Autonomous terrain adaptation and user-friendly tele-operation of wheel-track hybrid mobile robot. Int. J. Precis. Eng. Manuf. 13(10): 1781–1788

    Article  Google Scholar 

  86. Abeyruwan S, Seekircher A and Visser U 2012 Dynamic role assignment using general value functions. In: Proceedings of the IEEE Humanoid Robots Workshop, Osaka, Japan, November

  87. Guinaldo M, Fábregas E, Farias G, Dormido-Canto S, Chaos D, Sánchez J and Dormido S 2013 A mobile robots experimental environment with event-based wireless communication. Sensors 13(7): 9396–9413

    Article  Google Scholar 

  88. Lau N, Lopes L S, Corrente G and Filipe N 2009 Multi-robot team coordination through roles, positioning and coordinated procedures. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, St. Louis, USA, October, pp. 5841–5848

  89. Frias-Martinez V, Sklar E and Parsons S 2004 Exploring auction mechanisms for role assignment in teams of autonomous robots. In: Proceedings of the RoboCup Symposium, pp. 532–539

  90. Vail D, Veloso M, Schultz A, Parker L and Schneider F 2003 Multi-robot dynamic role assignment and coordination through shared potential fields. In: Proceedings of the International Workshop on Multi-Robot Systems, pp. 87–98

  91. Sipahioglu Y A and Parlaktuna O 2009 Heuristic-based dynamic route planning method for a homogeneous multi-robot team. Adv. Robot. 23(3): 269–287

    Article  Google Scholar 

  92. Chaimowicz L, Kumar V and Campos M 2004 A mechanism for dynamic coordination of multiple robots. Auton. Robots 17(1): 7–21

    Article  Google Scholar 

  93. MacAlpine P, Barrera F and Stone P 2013 Positioning to win: a dynamic role assignment and formation positioning system. In: Stone P (Ed.) RoboCup 2012, LNCS, vol. 7500. Berlin: Springer-Verlag, pp. 190–201

    Google Scholar 

  94. Jong-Hwan K and Prahlad V 2000 Multi-agent systems: a survey from the robot-soccer perspective. Adv. Robot. 6(1), 3–17

    Google Scholar 

  95. Amoozgar M H, Sadati S H and Alipour K 2012 Trajectory tracking of wheeled mobile robots using a kinematical fuzzy controller. Int. J. Robot. Autom. 27(1): 49–59

    Google Scholar 

  96. Guechi T E H, Lauber J, Dambrine M and Defoort M 2012 Output feedback controller design of a unicycle type mobile robot with delayed measurements. IET Control Theory Appl. 6(5): 726–733

    Article  MathSciNet  Google Scholar 

  97. Shojaei K, Shahri A M and Tarakameh A 2011 Adaptive feedback linearizing control of nonholonomic wheeled mobile robots in presence of parametric and nonparametric uncertainties. Robot. Comput. Integr. Manuf. 27(1): 194–204

    Article  Google Scholar 

  98. Park B S, Park J B and Choi Y H 2011 Adaptive observer-based trajectory tracking control of nonholonomic mobile robots. Int. J. Control Autom. Syst. 9(3): 534–541

    Article  Google Scholar 

  99. Wagdy A and Khamis A 2013 Adaptive group formation in multirobot systems. Adv. Artif. Intell. Article ID 692658

  100. Zhuang Y, Wang K, Wang W and Hu H 2012 A hybrid sensing approach to mobile robot localization in complex indoor environments. Int. J. Robot. Autom. 27(2): 198–205

    Google Scholar 

  101. Zhang Y, Park J and Chong K 2010 Model algorithm control for path tracking of wheeled mobile robots. Int. J. Precis. Eng. Manuf. 11(5): 705–714

    Article  Google Scholar 

  102. Kim Y G and Kwak J H 2011 Terrain-adaptive and user-friendly remote control of wheel–track hybrid mobile robot platform. In: Proceedings of IEEE HRI’11, pp. 165–166

  103. Shojaei K and Shahri A M 2012 Output feedback tracking control of uncertain non-holonomic wheeled mobile robots: a dynamic surface control approach. IET Control Theory Appl. 6(2): 216–228

    Article  MathSciNet  Google Scholar 

  104. Alvarez-Aguirre A, van de Wouw N and Oguchi T 2011 Remote tracking control of unicycle robots with network-induced delays. In: Cetto J A, et al (Eds.) Informatics in control, automation and robotics, pp. 225–238

  105. Macdonald E A 2011 Multi-robot assignment and formation control. M.Sc. Thesis, Georgia Institute of Technology, Atlanta, GA, USA, 7 June

  106. Huang J, Farritor S M, Qadi A and Goddard S 2006 Localization and follow the leader control of a heterogeneous group of mobile robots. IEEE/ASME Trans. Mechatron. 11: 205–215

    Article  Google Scholar 

  107. Mehrjerdi H, Saad M and Ghommam M J 2011 Hierarchical fuzzy cooperative control and path following for a team of mobile robots. IEEE/ASME Trans. Mechatron. 16: 907–917

    Article  Google Scholar 

  108. Parker L E 1998 ALLIANCE: an architecture for fault tolerant multi-robot cooperation. IEEE Trans. Robot. Autom. 14(2): 220–240

    Article  Google Scholar 

  109. Wang Y T, Chen Y C, Chen Y C and Wang Y T 2009 A method for obstacle avoidance in role reassignment of robot formation control. WSEAS Trans. Syst. 8(8): 1031–1040

    Google Scholar 

  110. Hao Y and Gong-You T 2014 Trajectory tracking control of wheeled mobile robots via fuzzy approach. In: Proceedings of the Control Conference (CCC), 28–30 July, pp. 8444–8449

  111. Ellekilde L P and Petersen H G 2013 Motion planning efficient trajectories for industrial bin-picking. Int. J. Robot. Res. 32: 991–1004

    Article  Google Scholar 

  112. Srinivasa S, Ferguson D, Helfrich C, Berenson D, Collet A, Diankov R, et al 2010 HERB: a home exploring robotic butler. Auto. Robots 28(1): 5–20

    Article  Google Scholar 

  113. Srinivasa S S, Berenson D, Cakmak M, Collet A, Dogar M R, Dragan A D, et al 2012 HERB 2.0: lessons learned from developing a mobile manipulator for the home. Proc. IEEE 100(8): 2410–2428

    Article  Google Scholar 

  114. Kuwata Y, Karaman S, Teo J, Frazzoli E, How J P and Fiore G 2009 Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 17(5): 1105–1118

    Article  Google Scholar 

  115. Jihyun Y and Crane C D 2011 Path planning for Unmanned Ground Vehicle in urban parking area. In: Proceedings of the 11th ICCAS, October, pp. 887–892

  116. Guarino Lo Bianco C 2013 Minimum-jerk velocity planning for mobile robot applications. IEEE Trans. Robot. 29(5): 1317–1326

    Article  Google Scholar 

  117. Lewis M A and Tan K H 1997 Virtual structures for high-precision cooperative mobile robotics control. Autonom. Robots 4(4): 387–403

    Article  Google Scholar 

  118. Desai J P 2002 A graph theoretic approach for modeling mobile robot team formations. J. Robot. Syst. 19(11), pp. 511–525,

    Article  MATH  Google Scholar 

  119. Das A K, Fierro R, Kumar V, Ostrowski J P, Spletzer J and Taylor C J 2002 A framework and architecture for multirobot coordination. Int. J. Robot. Res. 21(10–11): 977–995

    Google Scholar 

  120. Das A K, Fierro R, Kumar V, Ostrowski J P, Spletzer J and Taylor C J 2002 A vision-based formation control framework. IEEE Trans. Robot. Autom. 18: 813–825

    Article  Google Scholar 

  121. Cheng L, Yu H, Wu H Y and Wang Y J 2008 A sequential flocking control system for multiple mobile robots. Control Theory Appl. 25(6): 1117–1120

    Google Scholar 

  122. Hu J and Feng G 2010 Distributed tracking control of leader–follower multi-agent systems under noisy measurement. Automatica 46(8): 1382–1387

    Article  MathSciNet  MATH  Google Scholar 

  123. Lee G and Chong N Y 2009 Mechatronics decentralized formation control for small-scale robot teams with anonymity. Mechatronics 19(1): 85–105

    Article  Google Scholar 

  124. Notarstefano G, Egerstedt M and Haque M 2011 Containment in leader–follower networks with switching communication topologies. Automatica 47(5): 1035–1040

    Article  MathSciNet  MATH  Google Scholar 

  125. Semsar-Kazerooni E and Khorasani K 2011 Switching control of a modified leader–follower team of agents under the leader and network topological changes. IET Control Theory Appl. 5(12): 1369–1376

    Article  MathSciNet  Google Scholar 

  126. Wang Y T and Chen Y C 2012 Multiple-obstacle avoidance in role assignment of formation control. Int. J. Robot. Autom. 27(2): 177–184

    Google Scholar 

  127. Kostić D, Adinandra S, Caarls J and Nijmeijer H 2010 Collision-free motion coordination of unicycle multi-agent systems. In: Proceedings of the American Control Conference, Baltimore, USA, pp. 3186–3191

  128. Yang X, Wang J and Tan Y 2012 Robustness analysis of leader–follower consensus for multi-agent systems characterized by double integrators. Syst. Control Lett. 61(11): 1103–1115

    Article  MathSciNet  MATH  Google Scholar 

  129. Yu W, Chen G and Cao M 2010 Distributed leader–follower flocking control for multi-agent dynamical systems with time-varying velocities. Syst. Control Lett. 59(9): 543–552

    Article  MathSciNet  MATH  Google Scholar 

  130. Orlando G, Frontoni E, Mancini A and Zingarett P 2007 Sliding mode control for vision based leader follower. In: Proceedings of the 3rd European Conference on Mobile Robots, Freiburg, Germany, September 19–21

  131. Grob R, Bonani M, Mondada F and Dorigo M 2006 Autonomous self-assembly in swarm-bots. IEEE Trans. Robot. 22: 1115–1130

    Article  Google Scholar 

  132. Bahceci E, Soysal O and Sahin E 2003 A review: Pattern formation and adaptation in multirobot systems. Pittsburgh: Robotics Institute, Carnegie Mellon University

    Google Scholar 

  133. Nascimento T P, Moreira A P, Conceio A G S and Bonarini A 2012 Intelligent state changing applied to multirobot systems. Robot. Auton. Syst. 61(2): 115–124

    Article  Google Scholar 

  134. Bian X, Mou C, Yan Z and Xu J 2009 Simulation model and fault tree analysis for AUV. In: Proceedings of the IEEE International Conference on Mechatronics and Automation, pp. 4452–4457

  135. Harmati I and Skrzypczyk K 2009 Robot team coordination for target tracking using fuzzy logic controller in game theoretic framework. Robot. Auton. Syst. 57(1): 75–86

    Article  Google Scholar 

  136. Carlos C and Walker I D 2001 Interval methods for fault-tree analyses in robotics. IEEE Trans. Reliab. 50(1): 3–11

    Article  Google Scholar 

  137. Liang X and Zhao Y 2010 Research on application of fuzzy fault tree analysis in the electronic equipment fault diagnosis. In: Proceedings of the 2nd International Conference on Computer and Automation Engineering, pp. 65–67

  138. Jiang Y, Wang H, Fang L and Zhao M 2009 Fault detection and identification based on combining logic and model in a wall-climbing robot. J Control Theory Appl. 7(2): 157–162

    Article  MathSciNet  Google Scholar 

  139. Yevkin O 2011 An improved modular approach for dynamic fault tree analysis. In: Proceedings of the Annual Reliability and Maintainability Symposium, Florida, USA, January, pp. 1–5

  140. Yasuda T, Ohkura K and Ueda K 2006 A homogeneous mobile robot team that is fault-tolerant. In: Proceedings of the International Conference on Intelligent Robots and Systems, vol. 20, pp. 301–311

  141. Selma B and Chouraqui S 2013 Neuro-fuzzy controller to navigate an unmanned vehicle. SpringerPlus 2: 188

    Article  Google Scholar 

  142. Fierro R, Song P, Das A and Kumar V 2001 Cooperative control of robot formations. In: Cooperative control and automation, pp. 73–93

  143. Kanjanawanishkul K 2005 Formation control of mobile robots survey. eng.ubu.ac.th, pp. 50–64

  144. Chiddarwar S and Ramesh Babu N 2011 Dynamic priority allocation for conflict free coordinated manipulation of multiple agents. In: Proceedings of the IEEE Conference on Automation Science and Engineering, CASE 2009, Bangalore, India, 22–25 August

  145. Ogren P 2003 Formations and obstacle avoidance in mobile robot control. Sweden: Royal Institute of Technology

    Google Scholar 

  146. Leonard S, Croft E A and Little J J 2008 Dynamic visibility checking for vision-based motion planning. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2283–2288

  147. Morales M, Rodriguez S and Amato N M 2003 Improving the connectivity of PRM roadmaps. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4427–4432

  148. Nissoux C, Simon T and Laumond J P 2000 Visibility based probabilistic roadmaps. In: Proceedings of the International Conference on Intelligent Robots and Systems, pp. 1316–1321

  149. Collins A D, Agarwal P K and Harer J L 2003 HPRM: a hierarchical PRM. In: Proceedings of the 2003 IEEE International Conference on Robotics and Automation, pp. 4433–4438

  150. Kazemi M and Mehrandezh M 2004 Robotic navigation using harmonic function-based probabilistic roadmap. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4760–4765

  151. Arkin R 1998 Behaviour-based robotics. Cambridge, Massachusetts, USA: MIT Press

    Google Scholar 

  152. Balch T and Arkin R C 1998 Behavior-based formation control for multirobot teams. IEEE Trans. Robot. Autom. 14(6): 926–939

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology Design and Manufacturing (IIITDM) Kancheepuram, Chennai, 600127, India

    B Madhevan & M Sreekumar

Authors
  1. B Madhevan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M Sreekumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B Madhevan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Madhevan, B., Sreekumar, M. Identification of probabilistic approaches and map-based navigation in motion planning for mobile robots. Sādhanā 43, 8 (2018). https://doi.org/10.1007/s12046-017-0776-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-017-0776-8

Keywords

Search

Navigation