How to construct small probabilistic roadmaps with a good coverage?

References

• Latombe JC. Robot motion planning. Boston (MA): Kluwer; 1991.
   Google Scholar
• Smed J, Hakonen H. Algorithms and networking for computer games. Chichester: John Wiley & Sons; 2006. Chapter 5, Path finding; p. 97–113.
   Google Scholar
• Al-Bluwi I, Siméon T, Cortés J. Motion planning algorithms for molecular simulations: a survey. Comput. Sci. Rev. 2012;6:125–143.
   Google Scholar
• Pan X, Han CS, Law KH. Using motion planning to determine the existence of an accessible route in a CAD environment. Assist. Technol. 2010;22:32–45.
   PubMed Web of Science ®Google Scholar
• Choset H, Lynch KM, Hutchinson S, Kantor G, Burgard W, Kavraki LE, Thrun S. Principles of robot motion: theory, algorithms, and implementations. Cambridge (MA): MIT Press; 2005.
   Google Scholar
• LaValle SM. Planning algorithms. Cambridge, UK: Cambridge University Press; 2006.
   Google Scholar
• Overmars MH. A random approach to motion planning. The Netherlands: Utrecht University, Department of Computer Science; 1992. Tech Rep RUU-CS-92-93.
   Google Scholar
• Kavraki L, Latombe JC. Randomized preprocessing of configuration space for fast path planning. In: 1994 IEEE International Conference on Robotics and Automation. Proceedings; 1994 May 8–13; San Diego, CA. Vol. 3. Piscataway, NJ: IEEE; 1994. p. 2138–2145.
   Google Scholar
• Kavraki LE, Švestka P, Latombe JC, Overmars MH. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 1996;12:566–580.
   Google Scholar
• LaValle SM. Rapidly-exploring random trees: a new tool for path planning. Tech Rep 98–11. Ames, IA, USA: Computer Science Department, Iowa State University; 1998.
   Google Scholar
• LaValle SM, Kuffner JJ. Rapidly-exploring random trees: progress and prospects. In: Donald BR, Lynch KM, Rus D, editors. Algorithmic and computational robotics: new directions. Wellesley (MA): A K Peters; 2001. p. 293–308.
   Google Scholar
• Geraerts R, Overmars MH. Sampling and node adding in probabilistic roadmap planners. Rob. Auton. Syst. 2006;54:165–173.
   Web of Science ®Google Scholar
• Geraerts R, Overmars MH. Reachability-based analysis for probabilistic roadmap planners. Rob. Auton. Syst. 2007;55:824–836.
   Web of Science ®Google Scholar
• Yershova A, LaValle SM. Improving motion-planning algorithms by efficient nearest-neighbor searching. IEEE Trans. Rob. 2007;23:151–157.
   Web of Science ®Google Scholar
• Jaillet L, Siméon T. A PRM-based motion planner for dynamically changing environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems. Proceedings; 2004 Sep 28 – Oct 2; Sendai, Japan. Vol. 2. Piscataway, NJ: IEEE; 2004. p. 1606–1611.
   Google Scholar
• Nieuwenhuisen D, Overmars MH. Useful cycles in probabilistic roadmap graphs. In: 2004 IEEE International Conference on Robotics and Automation. Proceedings; 2004 Apr 26 – May 1; New Orleans, LA, USA. Vol. 1. Piscataway, NJ: IEEE; 2004. p. 446–452.
   Google Scholar
• Sud A, Gayle R, Andersen E, Guy S, Lin M, Manocha D. Real-time navigation of independent agents using adaptive roadmaps. Paper presented at: ACM Symposium on Virtual Reality Software and Technology; 2007 Nov 5–7; Newport Beach, CA, USA.
   Google Scholar
• Rantanen MT, Juhola M. Using probabilistic roadmaps in changing environments. Comput. Animat. Virtual Worlds. 2014;25:17–31.
   Web of Science ®Google Scholar
• Geraerts R, Overmars MH. Creating high-quality paths for motion planning. Int. J. Rob. Res. 2007;26:845–863.
   Web of Science ®Google Scholar
• Guernane R, Achour N. Generating optimized paths for motion planning. Rob. Auton. Syst. 2011;59:789–800.
   Web of Science ®Google Scholar
• Karamouzas I, Overmars MH. Adding variation to path planning. Comput. Animat. Virtual Worlds. 2008;19:283–293.
   Web of Science ®Google Scholar
• Geraerts R, Overmars MH. Creating small roadmaps for solving motion planning problems. IEEE International Conference on Methods and Models in Automation and Robotics. Proceedings; 2005 Aug 29; Miedzyzdroje, Poland. p. 531–536.
   Google Scholar
• Amato NM, Bayazit OB, Dale LK, Jones C, Vallejo D. OBPRM: An obstacle-based PRM for 3D workspaces. In: Agarwal PK, Kavraka LE, Mason MT, editors. Proceedings of the Third International Workshop on Algorithmic Foundations of Robotics; 1998 Mar 5–7; Houston, TX, USA. Natick, MA, USA: A.K. Peters, Ltd; 1998. p. 155–168
   Google Scholar
• Boor V, Overmars MH, van der Stappen AF. The Gaussian sampling strategy for probabilistic roadmap planners. In: IEEE International Conference on Robotics and Automation. Proceedings; 1999 May 10–15; Detroit, MI, USA. Vol. 2. Piscataway, NJ: IEEE; 1999. p. 1018–1023.
   Google Scholar
• Wilmarth SA, Amato NM, Stiller PF. MAPRM: A probabilistic roadmap planner with sampling on the medial axis of the free space. In: IEEE International Conference on Robotics and Automation. Proceedings; 1999 May 10–15; Detroit, MI, USA. Vol. 2. Piscataway, NJ: IEEE; 1999. p. 1024–1031.
   Google Scholar
• Holleman C, Kavraki LE. A framework for using the workspace medial axis in PRM planners. IEEE International Conference on Robotics and Automation. Proceedings; Apr 24–28; San Francisco. Vol. 2. Piscataway, NJ: IEEE; 2000. p. 1408–1413.
   Google Scholar
• Sun Z, Hsu D, Jiang T, Kurniawati H, Reif J. Narrow passage sampling for probabilistic roadmap planning. IEEE Trans. Rob. 2005;21:1105–1115.
   Web of Science ®Google Scholar
• Yeh HY, Thomas S, Eppstein D, Amato NM. UOBPRM: a uniformly distributed obstacle-based PRM. IEEE/RSJ International Conference on Intelligent Robots and Systems; 2012. p. 2655–2662
   Google Scholar
• Hsu D, Jiang T, Reif J, Sun Z. The bridge test for sampling narrow passages with probabilistic roadmap planners. In: IEEE International Conference on Robotics and Automation. Proceedings; 2003 Sep 14–19; Taipei, Taiwan. Vol. 3. Piscataway, NJ: IEEE; 2003. p. 4420–4426.
   Google Scholar
• Hsu D, Sánchez-Ante G, Sun Z. Hybrid PRM sampling with a cost-sensitive adaptive strategy. In: IEEE International Conference on Robotics and Automation. Proceedings; 2005 Apr 18–22; Barcelona. Piscataway, NJ: IEEE; 2005. p. 3874–3880.
   Google Scholar
• Kurniawati H, Hsu D. Workspace-based connectivity oracle: An adaptive sampling strategy for PRM planning. In: Akella S, Amato NM, Huang WH, Mishra B, editors. Algorithmic foundation of robotics VII. Berlin: Springer; 2008. p. 35–51.
   Google Scholar
• Thomas S, Morales M, Tang X, Amato NM. Biasing samplers to improve motion planning performance. In: IEEE International Conference on Robotics and Automation. Proceedings; 2007 Apr 10–14; Roma, Italy. Piscataway, NJ: IEEE; 2007. p. 1625–1630.
   Google Scholar
• Morales M, Tapia L, Pearce R, Rodriguez S. A machine learning approach for feature-sensitive motion planning. In: Erdmann M, Hsu D, Overmars M, van der Stappen AF, editors. Algorithmic foundations of robotics VI. Berlin: Springer; 2005. p. 361–376.
   Google Scholar
• Rodriguez S, Thomas S, Pearce R, Amato NM. RESAMPL: a region-sensitive adaptive motion planner. In: Akella S, Amato NM, Huang WH, Mishra B, editors. Algorithmic foundation of robotics VII. Berlin: Springer; 2008. p. 285–300.
   Google Scholar
• Rantanen MT. A connectivity-based method for enhancing sampling in probabilistic roadmap planners. J. Intell. Rob. Syst. 2011;64:161–178.
   Web of Science ®Google Scholar
• Siméon T, Laumond JP, Nissoux C. Visibility-based probabilistic roadmaps for motion planning. Adv. Robot. 2000;14:477–493.
   Web of Science ®Google Scholar
• Larsen E, Gottschalk S, Lin MC, Manocha D. Fast distance queries with rectangular swept sphere volumes. In: IEEE International Conference on Robotics and Automation. Proceedings; 2000 Apr 24–28; San Francisco. Vol. 4. Piscataway, NJ: IEEE; 2000. p. 3719–3726.
   Google Scholar