1932

Abstract

Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: () sampling constraint-satisfying configurations and () generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints.

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2018-05-28
2024-12-05
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Literature Cited

  1. 1.  Diftler MA, Mehling JS, Abdallah ME, Radford NA, Bridgwater LB et al. 2011. Robonaut 2 – the first humanoid robot in space. 2011 IEEE International Conference on Robotics and Automation2178–83 New York: IEEE
    [Google Scholar]
  2. 2.  Canny JF 1988. The Complexity of Robot Motion Planning Cambridge, MA: MIT Press
    [Google Scholar]
  3. 3.  Khatib O 1986. Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5:90–98
    [Google Scholar]
  4. 4.  Barraquand J, Latombe JC 1991. Robot motion planning: a distributed representation approach. Int. J. Robot. Res. 10:628–49
    [Google Scholar]
  5. 5.  Rimon E, Koditschek DE 1992. Exact robot navigation using artificial potential functions. IEEE Trans. Robot. Autom. 8:501–18
    [Google Scholar]
  6. 6.  Aine S, Swaminathan S, Narayanan V, Hwang V, Likhachev M 2015. Multi-heuristic A*. Int. J. Robot. Res. 35:224–43
    [Google Scholar]
  7. 7.  Choset H, Lynch KM, Hutchinson S, Kantor G, Burgard W et al. 2005. Principles of Robot Motion: Theory, Algorithms, and Implementations Cambridge, MA: MIT Press
    [Google Scholar]
  8. 8.  LaValle SM 2006. Planning Algorithms Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  9. 9.  Phillips M, Hwang V, Chitta S, Likhachev M 2016. Learning to plan for constrained manipulation from demonstrations. Auton. Robots 40:109–24
    [Google Scholar]
  10. 10.  Ambler AP, Popplestone RJ 1975. Inferring the positions of bodies from specified spatial relationships. Artif. Intell. 6:157–74
    [Google Scholar]
  11. 11.  Mason MT 1981. Compliance and force control for computer controlled manipulators. IEEE Trans. Syst. Man Cybern. 11:418–32
    [Google Scholar]
  12. 12.  Khatib O 1987. A unified approach for motion and force control of robot manipulators: the operational space formulation. IEEE J. Robot. Autom. 3:43–53
    [Google Scholar]
  13. 13.  Seereeram S, Wen JT 1995. A global approach to path planning for redundant manipulators. IEEE Trans. Robot. Autom. 11:152–60
    [Google Scholar]
  14. 14.  Whitney DE 1969. Resolved motion rate control of manipulators and human prostheses. IEEE Trans. Man Mach. Syst. 10:47–53
    [Google Scholar]
  15. 15.  Buss SR, Kim JS 2005. Selectively damped least squares for inverse kinematics. J. Graph. Tools 10:37–49
    [Google Scholar]
  16. 16.  Beeson P, Hart S, Gee S 2016. Cartesian motion planning & task programming with CRAFTSMAN Poster presented at Robot. Sci. Syst. Workshop Task Motion Plan Cambridge, MA: July 15
    [Google Scholar]
  17. 17.  Mitchell JSB, Mount DM, Papdimitrious CH 1987. The discrete geodesic problem. SIAM J. Comput. 16:647–68
    [Google Scholar]
  18. 18.  Asano T, Asano T, Guibas L, Hershberger J, Imai H 1985. Visibility-polygon search and Euclidean shortest paths. 26th Annual Symposium on Foundations of Computer Science155–164 New York: IEEE
    [Google Scholar]
  19. 19.  Alexopoulos C, Griffin PM 1992. Path planning for a mobile robot. IEEE Trans. Syst. Man Cybern. 22:318–22
    [Google Scholar]
  20. 20.  Zucker M, Ratliff N, Dragan A, Pivtoraiko P, Klingensmith M et al. 2013. CHOMP: covariant Hamiltonian optimization for motion planning. Int. J. Robot. Res. 32:1164–93
    [Google Scholar]
  21. 21.  Kalakrishnan M, Chitta S, Theodorou E, Pastor P, Schaal S 2011. STOMP: stochastic trajectory optimization for motion planning. 2011 IEEE International Conference on Robotics and Automation4569–74 New York: IEEE
    [Google Scholar]
  22. 22.  Schulman J, Duan Y, Ho J, Lee A, Awwal I et al. 2014. Motion planning with sequential convex optimization and convex collision checking. Int. J. Robot. Res. 33:1251–70
    [Google Scholar]
  23. 23.  Dong J, Mukadam M, Dellaert F, Boots B 2016. Motion planning as probabilistic inference using Gaussian processes and factor graphs. Robotics: Science and Systems XII D Hsu, N Amato, S Berman, S Jacobs, chap. 1. N.p.: Robot. Sci. Syst. Found.
    [Google Scholar]
  24. 24.  Latombe JC 1999. Motion planning: a journey of robots, molecules, digital actors, and other artifacts. Int. J. Robot. Res. 18:1119–28
    [Google Scholar]
  25. 25.  Gipson B, Hsu D, Kavraki LE, Latombe JC 2012. Computational models of protein kinematics and dynamics: beyond simulation. Annu. Rev. Anal. Chem. 5:273–91
    [Google Scholar]
  26. 26.  Baker W, Kingston Z, Moll M, Badger J, Kavraki LE 2017. Robonaut 2 and you: specifying and executing complex operations. 2017 IEEE Workshop on Advanced Robotics and Its Social Impacts New York: IEEE https://doi.org/10.1109/ARSO.2017.8025204
    [Crossref] [Google Scholar]
  27. 27.  Spivak M 1999. A Comprehensive Introduction to Differential Geometry Houston, TX: Publ. Perish
    [Google Scholar]
  28. 28.  Stilman M 2010. Global manipulation planning in robot joint space with task constraints. IEEE Trans. Robot. 26:576–84
    [Google Scholar]
  29. 29.  Berenson D 2011. Constrained manipulation planning PhD Thesis, Carnegie Mellon Univ. Pittsburgh, PA:
    [Google Scholar]
  30. 30.  Yakey JH, LaValle SM, Kavraki LE 2001. Randomized path planning for linkages with closed kinematic chains. IEEE Trans. Robot. Autom. 17:951–58
    [Google Scholar]
  31. 31.  Jaillet L, Porta JM 2013. Path planning under kinematic constraints by rapidly exploring manifolds. IEEE Trans. Robot. 29:105–17
    [Google Scholar]
  32. 32.  Mirabel J, Tonneau S, Fernbach P, Seppälä A-K, Campana M et al. 2016. HPP: a new software for constrained motion planning. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems383–89 New York: IEEE
    [Google Scholar]
  33. 33.  Sentis L, Khatib O 2005. Synthesis of whole-body behaviors through hierarchical control of behavioral primitives. Int. J. Humanoid Robot. 2:505–18
    [Google Scholar]
  34. 34.  Siméon T, Laumond JC, Cortés J, Sahbani A 2004. Manipulation planning with probabilistic roadmaps. Int. J. Robot. Res. 32:729–46
    [Google Scholar]
  35. 35.  Hauser K, Bretl T, Latombe JC, Harada K, Wilcox B 2008. Motion planning for legged robots on varied terrain. Int. J. Robot. Res. 27:1325–49
    [Google Scholar]
  36. 36.  Perrin N, Stasse O, Lamiraux F, Kim YJ, Manocha D 2012. Real-time footstep planning for humanoid robots among 3D obstacles using a hybrid bounding box. 2012 IEEE International Conference on Robotics and Automation977–82 New York: IEEE
    [Google Scholar]
  37. 37.  Reid W, Fitch R, Göktogan AH, Sukkarieh S 2016. Motion planning for reconfigurable mobile robots using hierarchical fast marching trees Presented at Workshop Algorithmic Found. Robot., 12th, San Francisco, Dec 18–20 Paper available at http://wafr2016.berkeley.edu/program.html
    [Google Scholar]
  38. 38.  Dantam NT, Kingston ZK, Chaudhuri S, Kavraki LE 2016. Incremental task and motion planning: a constraint-based approach. Robotics: Science and Systems XII D Hsu, N Amato, S Berman, S Jacobs, chap. 2. N.p.: Robot. Sci. Syst. Found.
    [Google Scholar]
  39. 39.  Lozano-Pérez T, Kaelbling LP 2014. A constraint-based method for solving sequential manipulation planning problems. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems3684–91 New York: IEEE
    [Google Scholar]
  40. 40.  Kaelbling L, Lozano-Perez T 2011. Hierarchical task and motion planning in the now. 2011 IEEE International Conference on Robotics and Automation1470–77 New York: IEEE
    [Google Scholar]
  41. 41.  Elbanhawi M, Simic M 2014. Sampling-based robot motion planning: a review. IEEE Access 2:56–77
    [Google Scholar]
  42. 42.  Ladd AM, Kavraki LE 2004. Measure theoretic analysis of probabilistic path planning. IEEE Trans. Robot. Autom. 20:229–42
    [Google Scholar]
  43. 43.  McCarthy Z, Bretl T, Hutchinson S 2012. Proving path non-existence using sampling and alpha shapes. 2012 IEEE International Conference on Robotics and Automation2563–69 New York: IEEE
    [Google Scholar]
  44. 44.  Kavraki LE, Švestka P, Latombe JC, Overmars M 1996. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12:566–80
    [Google Scholar]
  45. 45.  Kuffner J, LaValle SM 2000. RRT-Connect: an efficient approach to single-query path planning. IEEE International Conference on Robotics and Automation995–1001 New York: IEEE
    [Google Scholar]
  46. 46.  LaValle SM, Kuffner JJ 2001. Randomized kinodynamic planning. Int. J. Robot. Res. 20:378–400
    [Google Scholar]
  47. 47.  Hsu D, Latombe JC, Motwani R 1999. Path planning in expansive configuration spaces. Int. J. Comput. Geom. Appl. 9:495–512
    [Google Scholar]
  48. 48.  Ladd AM, Kavraki LE 2005. Motion planning in the presence of drift, underactuation and discrete system changes. Robotics: Science and Systems I S Thrun, GS Sukhatme, S Schaal 233–41 Cambridge, MA: MIT Press
    [Google Scholar]
  49. 49.  Şucan IA, Kavraki LE 2012. A sampling-based tree planner for systems with complex dynamics. IEEE Trans. Robot. 28:116–31
    [Google Scholar]
  50. 50.  Amato N, Bayazit O, Dale L, Jones C, Vallejo D 1999. OBPRM: an obstacle-based PRM for 3D workspaces. Robotics: The Algorithmic Perspective PK Agarwal, LE Kavraki, MT Mason 155–68 Wellesley, MA: A.K. Peters
    [Google Scholar]
  51. 51.  Boor V, Overmars MH, van der Stappen AF 1999. The Gaussian sampling strategy for probabilistic roadmap planners. 1999 IEEE International Conference on Robotics and Automation1018–23 New York: IEEE
    [Google Scholar]
  52. 52.  Wilmarth SA, Amato N, Stiller PF 1999. MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space. 1999 IEEE International Conference on Robotics and Automation1024–31 New York: IEEE
    [Google Scholar]
  53. 53.  Hsu D, Jiang T, Reif J, Sun Z 2003. The bridge test for sampling narrow passages with probabilistic roadmap planners. 2003 IEEE International Conference on Robotics and Automation 34420–26 New York: IEEE
    [Google Scholar]
  54. 54.  Salzman O, Hemmer M, Halperin D 2015. On the power of manifold samples in exploring configuration spaces and the dimensionality of narrow passages. IEEE Trans. Autom. Sci. Eng. 12:529–38
    [Google Scholar]
  55. 55.  LaValle SM, Branicky MS, Lindemann SR 2004. On the relationship between classical grid search and probabilistic roadmaps. Int. J. Robot. Res. 23:673–92
    [Google Scholar]
  56. 56.  Andoni A, Indyk P 2017. Nearest neighbors in high-dimensional spaces. Handbook of Discrete and Computational Geometry JE Goodman, J O'Rourke, CD Tóth 1135–55 Boca Raton, FL: CRC, 3rd ed..
    [Google Scholar]
  57. 57.  Shoemake K 1985. Animating rotation with quaternion curves. ACM SIGGRAPH Comput. Graph. 19:245–54
    [Google Scholar]
  58. 58.  Sánchez G, Latombe JC 2001. A single-query bi-directional probabilistic roadmap planner with lazy collision checking. Robotics Research: The Tenth International Symposium RA Jarvis, Z Zelinsky 403–17 Berlin: Springer
    [Google Scholar]
  59. 59.  Şucan IA, Kavraki LE 2009. On the performance of random linear projections for sampling-based motion planning. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems2434–39 New York: IEEE
    [Google Scholar]
  60. 60.  Şucan IA, Moll M, Kavraki LE 2012. The Open Motion Planning Library. IEEE Robot. Autom. Mag. 19:72–82
    [Google Scholar]
  61. 61.  Geraerts R, Overmars M 2007. Creating high-quality paths for motion planning. Int. J. Robot. Res. 26:845–63
    [Google Scholar]
  62. 62.  Karaman S, Frazzoli E 2011. Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30:846–94
    [Google Scholar]
  63. 63.  Gammell JD, Srinivasa SS, Barfoot TD 2015. Batch informed trees (BIT*): sampling-based optimal planning via the heuristically guided search of implicit random geometric graphs. 2015 IEEE International Conference on Robotics and Automation3067–74 New York: IEEE
    [Google Scholar]
  64. 64.  Luna R, Şucan IA, Moll M, Kavraki LE 2013. Anytime solution optimization for sampling-based motion planning. 2013 IEEE International Conference on Robotics and Automation5053–59 New York: IEEE
    [Google Scholar]
  65. 65.  Luo J, Hauser K 2014. An empirical study of optimal motion planning. IEEE/RSJ International Conference on Intelligent Robots and Systems1761–68 New York: IEEE
    [Google Scholar]
  66. 66.  Webb DJ, van den Berg J 2013. Kinodynamic RRT*: asymptotically optimal motion planning for robots with linear dynamics. 2013 IEEE International Conference on Robotics and Automation5054–61 New York: IEEE
    [Google Scholar]
  67. 67.  Hauser K, Zhou Y 2016. Asymptotically optimal planning by feasible kinodynamic planning in a state–cost space. IEEE Trans. Robot. 32:1431–43
    [Google Scholar]
  68. 68.  Li Y, Littlefield Z, Bekris KE 2016. Asymptotically optimal sampling-based kinodynamic planning. Int. J. Robot. Res. 35:528–64
    [Google Scholar]
  69. 69.  Dobson A, Bekris KE 2014. Sparse roadmap spanners for asymptotically near-optimal motion planning. Int. J. Robot. Res. 33:18–47
    [Google Scholar]
  70. 70.  Moll M, Şucan IA, Kavraki LE 2015. Benchmarking motion planning algorithms: an extensible infrastructure for analysis and visualization. IEEE Robot. Autom. Mag. 22:96–102
    [Google Scholar]
  71. 71.  Bohlin R, Kavraki LE 2000. Path planning using lazy PRM. 2000 IEEE International Conference on Robotics and Automation521–28 New York: IEEE
    [Google Scholar]
  72. 72.  Tenenbaum JB, de Silva V, Langford JC 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290:2319–23
    [Google Scholar]
  73. 73.  Chaudhry R, Ivanov Y 2010. Fast approximate nearest neighbor methods for non-Euclidean manifolds with applications to human activity analysis in videos. Computer Vision – ECCV 2010 K Daniilidis, P Maragos, N Paragios 735–48 Berlin: Springer
    [Google Scholar]
  74. 74.  Kimmel R, Sethian JA 1998. Computing geodesic paths on manifolds. PNAS 95:8431–35
    [Google Scholar]
  75. 75.  Ying L, Candes EJ 2006. Fast geodesic computation with the phase flow method. J. Comput. Phys. 220:6–18
    [Google Scholar]
  76. 76.  Crane K, Weischedel C, Wardetzky M 2013. Geodesics in heat: a new approach to computing distance based on heat flow. ACM Trans. Graph. 32:152
    [Google Scholar]
  77. 77.  Hauser K 2014. Fast interpolation and time-optimization with contact. Int. J. Robot. Res. 33:1231–50
    [Google Scholar]
  78. 78.  Deza MM, Deza E 2016. Encyclopedia of Distances Berlin: Springer
    [Google Scholar]
  79. 79.  Kingston Z, Moll M, Kavraki LE 2018. Decoupling constraints from sampling-based planners. Robotics Research: The 18th International Symposium (ISRR) Forthcoming
    [Google Scholar]
  80. 80.  Bonilla M, Farnioli E, Pallottino L, Bicchi A 2015. Sample-based motion planning for soft robot manipulators under task constraints. 2015 IEEE International Conference on Robotics and Automation2522–27 New York: IEEE
    [Google Scholar]
  81. 81.  Bonilla M, Pallottino L, Bicchi A 2017. Noninteracting constrained motion planning and control for robot manipulators. 2017 IEEE International Conference on Robotics and Automation4038–43 New York: IEEE
    [Google Scholar]
  82. 82.  Rodriguez S, Thomas S, Pearce R, Amato NM 2008. RESAMPL: a region-sensitive adaptive motion planner. Algorithmic Foundations of Robotics VII S Akella, NM Amato, WH Huang, B Mishra 285–300 Berlin: Springer
    [Google Scholar]
  83. 83.  Bialkowski J, Otte M, Frazzoli E 2013. Free-configuration biased sampling for motion planning. 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems1272–79 New York: IEEE
    [Google Scholar]
  84. 84.  Dennis J, Schnabel R 1983. Numerical Methods for Unconstrained Optimization and Nonlinear Equations Englewood Cliffs, NJ: Prentice Hall
    [Google Scholar]
  85. 85.  Press WH, Teukolsky SA, Vetterling WT, Flannery BP 1992. Numerical Recipes in C: The Art of Scientific Computing Cambridge, UK: Cambridge Univ. Press, 2nd ed..
    [Google Scholar]
  86. 86.  Mirabel J, Lamiraux F 2017. Manipulation planning: building paths on constrained manifolds Tech. Rep., LAAS-GEPETTO – Équipe Mouvement des Systèmes Anthropomorphes, Toulouse, Fr.
    [Google Scholar]
  87. 87.  Wedemeyer WJ, Scheraga HA 1999. Exact analytical loop closure in proteins using polynomial equations. J. Comput. Chem. 20:819–44
    [Google Scholar]
  88. 88.  Cortés J, Siméon T, Laumond JP 2002. A random loop generator for planning the motions of closed kinematic chains using PRM methods. 2002 IEEE International Conference on Robotics and Automation2141–46 New York: IEEE
    [Google Scholar]
  89. 89.  Oriolo G, Ottavi M, Vendittelli M 2002. Probabilistic motion planning for redundant robots along given end-effector paths. 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems 21657–62 New York: IEEE
    [Google Scholar]
  90. 90.  Yao Z, Gupta K 2005. Path planning with general end-effector constraints: using task space to guide configuration space search. 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems1875–80 New York: IEEE
    [Google Scholar]
  91. 91.  Kanehiro F, Yoshida E, Yokoi K 2012. Efficient reaching motion planning and execution for exploration by humanoid robots. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems1911–16 New York: IEEE
    [Google Scholar]
  92. 92.  Kim J, Ko I, Park FC 2016. Randomized path planning for tasks requiring the release and regrasp of objects. Adv. Robot. 30:270–83
    [Google Scholar]
  93. 93.  Xian Z, Lertkultanon P, Pham QC 2017. Closed-chain manipulation of large objects by multi-arm robotic systems. IEEE Robot. Autom. Lett. 2:1832–39
    [Google Scholar]
  94. 94.  Weghe MV, Ferguson D, Srinivasa SS 2007. Randomized path planning for redundant manipulators without inverse kinematics. 2007 7th IEEE-RAS International Conference on Humanoid Robots477–82 New York: IEEE
    [Google Scholar]
  95. 95.  Oriolo G, Vendittelli M 2009. A control-based approach to task-constrained motion planning. 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems297–302 New York: IEEE
    [Google Scholar]
  96. 96.  Vendittelli M, Oriolo G 2009. Task-constrained motion planning for underactuated robots. 2015 IEEE International Conference on Robotics and Automation2965–70 New York: IEEE
    [Google Scholar]
  97. 97.  Cefalo M, Oriolo G, Vendittelli M 2013. Task-constrained motion planning with moving obstacles. IEEE/RSJ International Conference on Intelligent Robots and Systems5758–63 New York: IEEE
    [Google Scholar]
  98. 98.  Zhang Y, Hauser K 2013. Unbiased, scalable sampling of protein loop conformations from probabilistic priors. BMC Struct. Biol. 13:Suppl. 1S9
    [Google Scholar]
  99. 99.  Pachov DV, van den Bedem H 2015. Nullspace sampling with holonomic constraints reveals molecular mechanisms of protein Gαs. PLOS Comput. Biol. 11:e1004361
    [Google Scholar]
  100. 100.  Fonseca R, Budday D, van dem Bedem H 2016. Collision-free Poisson motion planning in ultra high-dimensional molecular conformation spaces. arXiv1607.07483
  101. 101.  Henderson ME 2002. Multiple parameter continuation: computing implicitly defined k-manifolds. Int. J. Bifurc. Chaos 12:451–76
    [Google Scholar]
  102. 102.  Rheinboldt WC 1996. MANPAK: a set of algorithms for computations on implicitly defined manifolds. Comput. Math. Appl. 32:15–28
    [Google Scholar]
  103. 103.  Kim B, Um TT, Suh C, Park FC 2016. Tangent bundle RRT: a randomized algorithm for constrained motion planning. Robotica 34:202–25
    [Google Scholar]
  104. 104.  Bohigas O, Henderson ME, Ros L, Manubens M, Porta JM 2013. Planning singularity-free paths on closed-chain manipulators. IEEE Trans. Robot. 29:888–98
    [Google Scholar]
  105. 105.  Jaillet L, Porta JM 2013. Asymptotically-optimal path planning on manifolds. Robotics: Science and Systems VIII N Roy, P Newman, S Srinivasa 145–52 Cambridge, MA: MIT Press
    [Google Scholar]
  106. 106.  Karaman S, Frazzoli E 2011. Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30:846–94
    [Google Scholar]
  107. 107.  Bordalba R, Ros L, Porta JM 2017. Kinodynamic planning on constraint manifolds. arXiv1705.07637
  108. 108.  Voss C, Moll M, Kavraki LE 2017. Atlas + X: sampling-based planners on constraint manifolds Tech. Rep. 17-02, Dep. Comput. Sci., Rice Univ., Houston, TX
    [Google Scholar]
  109. 109.  Han L, Rudolph L, Blumenthal J, Valodzin I 2008. Convexly stratified deformation spaces and efficient path planning for planar closed chains with revolute joints. Int. J. Robot. Res. 27:1189–212
    [Google Scholar]
  110. 110.  McMahon T 2016. Sampling based motion planning with reachable volumes PhD Thesis, Texas A&M Univ., College Station
    [Google Scholar]
  111. 111.  Kuffner JJ, Kagami S, Nishiwaki K, Inaba M, Inoue H 2002. Dynamically-stable motion planning for humanoid robots. Auton. Robots 12:105–18
    [Google Scholar]
  112. 112.  Burget F, Hornung A, Bennewitz M 2013. Whole-body motion planning for manipulation of articulated objects. 2013 IEEE International Conference on Robotics and Automation1656–62 New York: IEEE
    [Google Scholar]
  113. 113.  Igarashi T, Stilman M 2010. Homotopic path planning on manifolds for cabled mobile robots. Algorithmic Foundations of Robotics IX D Hsu, V Isler, JC Latombe, MC Lin 1–18 Berlin: Springer
    [Google Scholar]
  114. 114.  Şucan IA, Chitta S 2012. Motion planning with constraints using configuration space approximations. 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems1904–10 New York: IEEE
    [Google Scholar]
  115. 115.  Coleman D, Şucan IA, Moll M, Okada K, Correll N 2015. Experience-based planning with sparse roadmap spanners. 2015 IEEE International Conference on Robotics and Automation900–5 New York: IEEE
    [Google Scholar]
  116. 116.  Hauser K 2016. Continuous pseudoinversion of a multivariate function: application to global redundancy resolution Presented at Workshop Algorithmic Found. Robot., 12th, San Francisco, Dec 18–20 Paper available at http://wafr2016.berkeley.edu/program.html
    [Google Scholar]
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