Abstract
Motion planning problem is an active field in robotics. It is concerned with converting high-level task specifications into low-level descriptions of how to move and provides a feasible sequence of movements that avoid obstacles while respecting kinematic and dynamic equations. In this work, new planners are designed with the aim of developing an efficient motion planner in a heterogeneous, cluttered, and dynamic workspace. The planners are composed of two layers, and they use a rule-based system as a guidance. The first layer uses exact cell decomposition method, which divides the workspace into manageable regions and finds the adjacency information for them. The second layer utilizes rapidly exploring random tree algorithm RRT that finds a solution in a cluttered workspace. The adjacency information of the free cells and the exploration information that is provided by RRT are combined and utilized to help the planners classifying the free regions and guiding the growth of RRT trees efficiently toward the most important areas. Two types of the planners are proposed, the first one uses adviser that pulls the trees’ growth toward the boundary areas between explored and unexplored regions, while the adviser of the second planner uses the collision information and fuzzy rules to guide the trees’ growth toward areas that have low collision rate around the boundaries of explored regions. The planners are tested in stationary as well as in changed workspace. The proposed methods have been compared to other approaches and the simulation results show that they yield better results in terms of completeness and efficiency.
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Abbadi A, Matousek R (2012) RRTs review and statistical analysis. Int J Math Comput Simul 6(1):1–8
Abbadi A, Matousek R, Minar P, Soustek P (2011) RRTs review and options. Comput Eng Syst Appl II:194–199
Abbadi A, Matousek R, Jancik S, Roupec J (2012) Rapidly-exploring random trees: 3D planning. 18th international conference on soft computing, MENDEL 2012. Brno University of Technology, Brno, pp 594–599
Abbadi A, Matousek R, Osmera P, Knispel L (2014) Spatial guidance to Rrt planner using cell-decomposition algorithm. In: 20th international conference on soft computing, MENDEL 2014
Amato NM, Bayazit OB, Dale LK, Jones C, Vallejo D (1998) OBPRM: an obstacle-based PRM for 3D workspaces. In: Proceedings of the third workshop on the algorithmic foundations of robotics on robotics: the algorithmic perspective, pp 155–168
Arambula Cosío F, Padilla Castañeda M (2004) Autonomous robot navigation using adaptive potential fields. Math Comput Model 40(910):1141–1156. https://doi.org/10.1016/j.mcm.2004.05.001
Atramentov A, LaValle S (2002) Efficient nearest neighbor searching for motion planning. In: Proceedings 2002 IEEE international conference on robotics and automation (cat no. 02CH37292), vol 1, pp 632–637. https://doi.org/10.1109/ROBOT.2002.1013429
Aurenhammer F (1991) Voronoi diagrams—a survey of a fundamental geometric data structure. https://doi.org/10.1145/116873:116880
Aurenhammer F, Klein R (2000) Voronoi diagrams. Handb Comput Geom 5:201–290
Baginski B (1996) The \(Z^{3}\)-method for fast path planning in dynamic environments. In: IASTED conference applications of control and robotics, pp 47–52
Barraquand J, Latombe JC (1990) A Monte-Carlo algorithm for path planning with many degrees of freedom. In: Proceedings of the IEEE international conference on robotics and automation, vol 3, pp 1712–1717. https://doi.org/10.1109/ROBOT.1990.126256
Barraquand J, Latombe JC (1991) Robot motion planning: a distributed representation approach. Int J Robot Res 10(6):628–649. https://doi.org/10.1177/027836499101000604
Boor V, Overmars M, Stappena VD (1999) The Gaussian sampling strategy for probabilistic roadmap planners. In: Proceedings of the 1999 IEEE international conference on robotics and automation (cat. no. 99CH36288C), vol 2, pp 1018–1023. https://doi.org/10.1109/ROBOT.1999.772447
Borenstein J, Koren Y (1991) The vector field histogram—fast obstacle avoidance for mobile robots. IEEE Trans Robot Autom 7(3):278–288. https://doi.org/10.1109/70.88137
Brooks R, Lozano-Perez T (1985) A subdivision algorithm in configuration space for findpath with rotation. https://doi.org/10.1109/TSMC.1985.6313352
Bruce J, Veloso M (2002) Real-time randomized path planning for robot navigation. In: IEEE/RSJ international conference on intelligent robots and systems, vol 3, pp 2383–2388. https://doi.org/10.1109/IRDS.2002.1041624
Chazelle B (1987) Approximation and decomposition of shapes. In: Schwartz JT, Yap CK (eds) Advances in robotics 1: algorithmic and geometric aspects of robotics. L. Erlbaum Associates Inc., Hillsdale, p 320
Cheng P (2004) Reducing RRT metric sensitivity for motion planning. PhD thesis, Iowa State University
Cheng PCP, LaValle S (2001) Reducing metric sensitivity in randomized trajectory design. In: Proceedings 2001 IEEE/RSJ international conference on intelligent robots and systems. Expanding the societal role of robotics in the the next millennium (cat. no. 01CH37180), vol 1, pp 43–48. https://doi.org/10.1109/IROS.2001.973334
Cheng PCP, LaValle S (2002) Resolution complete rapidly-exploring random trees. In: Proceedings 2002 IEEE international conference on robotics and automation (cat. no. 02CH37292), vol 1, pp 267–272. https://doi.org/10.1109/ROBOT.2002.1013372
Choi J, Choi M, Nam SY, Chung WK (2011) Autonomous topological modeling of a home environment and topological localization using a sonar grid map. Auton Robots 30(4):351–368. https://doi.org/10.1007/s10514-011-9223-6
Choset H (2000) Sensor-based exploration: incremental construction of the hierarchical generalized Voronoi graph. Int J Robot Res 19(2):126–148. https://doi.org/10.1177/02783640022066789
Choset H, Lynch KM, Hutchinson S, Kantor GA, Burgard W, Kavraki LE, Thrun S (2005) Principles of robot motion. In: Technical report, Theory, algorithm and implementation
Cowlagi R, Tsiotras P (2012) Multiresolution motion planning for autonomous agents via wavelet-based cell decompositions 42(5):1455–1469. https://doi.org/10.1109/TSMCB.2012.2192268
Denny J, Amato NM (2011) Toggle PRM: simultaneous mapping of C-free and C-obstacle—a study in 2D. In: IEEE international conference on intelligent robots and systems, pp 2632–2639. https://doi.org/10.1109/IROS.2011.6048865
de Berg M, Cheong O, van Kreveld M, Overmars M (2008) Computational geometry, 3rd edn. Springer, Berlin
Esposito JM (2013) Conditional density growth (CDG) model: a simplified model of RRT coverage for kinematic systems. Robotica 31:733–746. https://doi.org/10.1017/S0263574712000690
Fabbri R, Estrozi LF, Costa LDF (2002) On Voronoi diagrams and medial axes. J Math Imaging Vis 17:27–40. https://doi.org/10.1023/A:1020722624682
Garrido S, Moreno L, Abderrahim M, Martin F (2011) Path planning for mobile robot navigation using Voronoi diagram and fast marching. Int J Robot Autom 2(1):42–64
Glavina B (1990) Solving findpath by combination of goal-directed and randomized search. In: Proceedings of the IEEE international conference on robotics and automation, vol 3, pp 1718–1723. https://doi.org/10.1109/ROBOT.1990.126257
Hsu D, Jiang TJT, Reif J, Sun ZSZ (2003) The bridge test for sampling narrow passages with probabilistic roadmap planners. In: 2003 IEEE international conference on robotics and automation (cat. no. 03CH37422), vol 3, pp 4420–4426. https://doi.org/10.1109/ROBOT.2003.1242285
Hwang YK, Ahuja N (1992) A potential field approach to path planning. IEEE Trans Robot Autom 8(1):23–32. https://doi.org/10.1109/70.127236
Jaillet L, Hoffman J, Van Den Berg J, Abbeel P, Porta JM, Goldberg K (2011) EG-RRT: environment-guided random trees for kinodynamic motion planning with uncertainty and obstacles. In: IEEE international conference on intelligent robots and systems, pp 2646–2652. https://doi.org/10.1109/IROS.2011.6048409
Jaradat M, Garibeh M, Feilat E (2012) Fuzzy potential field. Dynamic motion planning. Mobile robot. Soft Comput. https://doi.org/10.1007/s00500-011-0742-z
Kalisiak M, Van De Panne M (2006) RRT-blossom: RRT with a local flood-fill behavior. Proc IEEE Int Conf Robot Autom 2006:1237–1242. https://doi.org/10.1109/ROBOT.2006.1641878
Kamon I, Rimon E, Rivlin E (1998) TangentBug: a range-sensor-based navigation algorithm. Int J Robot Res 17(9):934–953. https://doi.org/10.1177/027836499801700903
Karaman S (2012) Sampling-based algorithms for optimal path planning problems. PhD thesis
Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Robot Res 30(7):846–894. https://doi.org/10.1177/0278364911406761. arXiv:1105.1186v1
Katevas NI, Tzafestas SG, Pnevmatikatos CG (1998) The approximate cell decomposition with local node refinement global path planning method: path nodes refinement and curve parametric interpolation. J Intell Robot Syst 22(3–4):289–314. https://doi.org/10.1023/A:1008034314006
Kavraki LE, Švestka P, Latombe JC, Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans Robot Autom 12(4):566–580. https://doi.org/10.1109/70.508439
Kavraki LE, Kolountzakis MN, Latombe JC (1998) Analysis of probabilistic roadmaps for path planning. IEEE Trans Robot Autom 14(1):166–171. https://doi.org/10.1109/70.660866
Khatib O (1985) Real-time obstacle avoidance for manipulators and mobile robots. In: Proceedings of the 1985 IEEE international conference on robotics and automation, vol 2, pp 500–505. https://doi.org/10.1109/ROBOT.1985.1087247
Kim JO, Khosla P (1991) Real-time obstacle avoidance using harmonic potential functions. In: Proceedings of the 1991 IEEE international conference on robotics and automation, vol 1, pp 790–796. https://doi.org/10.1109/ROBOT.1991.131683
Kuffner JJ, LaValle S (2000) RRT-connect: an efficient approach to single-query path planning. In: Proceedings 2000 ICRA millennium conference IEEE international conference on robotics and automation symposia proceedings (cat no. 00CH37065), vol 2, pp 995–1001. https://doi.org/10.1109/ROBOT.2000.844730
Kuffner JJ, LaValle SM (2011) Space-filling trees: a new perspective on incremental search for motion planning. In: IEEE international conference on intelligent robots and systems, pp 2199–2206. https://doi.org/10.1109/IROS.2011.6048346
Latombe JC (1991) Robot motion planning. https://doi.org/10.1016/1049-9660(91)90042-N
LaValle SM (1998) Rapidly-exploring random trees: a new tool for path planning. In: Technical report, Computer Science Department, Iowa State University
LaValle SM (2006) Planning algorithms, vol 2006. Cambridge University Press, Cambridge
LaValle SM, Branicky MS (2004) On the relationship between classical grid search and probabilistic roadmaps. In: Springer tracts in advanced robotics 7 STAR(7–8), pp 59–75, https://doi.org/10.1007/978-3-540-45058-0_5
LaValle SM, Kuffner JJ (2000) Rapidly-exploring random trees: progress and prospects. In: 4th workshop on algorithmic and computational robotics: new directions, pp 293–308
Le D, Plaku E (2014) Guiding sampling-based tree search for motion planning with dynamics via probabilistic roadmap abstractions. In: 2014 IEEE/RSJ international conference on intelligent robots and systems, pp 212–217. https://doi.org/10.1109/IROS.2014.6942563
Li D, Li Q, Cheng N, Song J (2012) Extended RRT-based path planning for flying robots in complex 3D environments with narrow passages. In: IEEE international conference on automation science and engineering, pp 1173–1178. https://doi.org/10.1109/CoASE.2012.6386513
Li J, Liu S, Zhang B, Zhao X, (2014) RRT-A* motion planning algorithm for non-holonomic mobile robot. In: 2014 proceedings of the SICE annual conference (SICE), pp 1833–1838. https://doi.org/10.1109/SICE.2014.6935304
Lin YT (2006) The Gaussian PRM sampling for dynamic configuration spaces. In: 9th international conference on control, automation, robotics and vision, 2006, ICARCV ’06, pp 1–5. https://doi.org/10.1109/ICARCV.2006.345422
Lindemann S, LaValle S (2004) Incrementally reducing dispersion by increasing Voronoi bias in RRTs. In: Proceedings of the IEEE international conference on robotics and automation, 2004, ICRA ’04, vol 4, pp 3251–3257. https://doi.org/10.1109/ROBOT.2004.1308755
Lulu L, Elnagar A (2005) A comparative study between visibility-based roadmap path planning algorithms. In: 2005 IEEE/RSJ international conference on intelligent robots and systems, IROS, pp 3700–3705. https://doi.org/10.1109/IROS.2005.1545545
Lumelsky V, Stepanov A (1986) Dynamic path planning for a mobile automaton with limited information on the environment. IEEE Trans Autom Control 31(11):1058–1063. https://doi.org/10.1109/TAC.1986.1104175
Masehian E, Naseri A (2010) Mobile robot online motion planning using generalized Voronoi graphs. J Ind Eng 5:1–15
Masoud A (2013) A harmonic potential field approach for joint planning and control of a rigid, separable nonholonomic, mobile robot. Robot Auton Syst 61(6):593–615. https://doi.org/10.1016/j.robot.2013.02.007
Mazer E, Ahuactzin JM, Bessière P (1998) The Ariadne’s clew algorithm. J Artif Intell Res 9(1):295–316. https://doi.org/10.1613/jair.468. arXiv:1105.5440
Mbede JB, Huang X, Wang M (2000) Fuzzy motion planning among dynamic obstacles using artificial potential fields for robot manipulators. Robot Auton Syst 32(1):61–72. https://doi.org/10.1016/S0921-8890(00)00073-7
McFetridge L, Yousef Ibrahim M (1998) New technique of mobile robot navigation using a hybrid adaptive fuzzy potential field approach. Comput Ind Eng 35(34):471–474. https://doi.org/10.1016/S0360-8352(98)00136-3
Militão F, Naden K, Toninho B (2010) Improving RRT with context sensitivity. In: Technical report, School of Computer Science, Carnegie Mellon University
Nasir J, Islam F, Malik U, Ayaz Y, Hasan O, Khan M, Muhammad MS (2013) RRT*-SMART: a rapid convergence implementation of RRT*. Int J Adv Robot Syst 10:299. https://doi.org/10.5772/56718
Perez A, Platt R, Konidaris G, Kaelbling L, Lozano-Perez T (2012) LQR-RRT*: optimal sampling-based motion planning with automatically derived extension heuristics. In: Proceedings of the IEEE international conference on robotics and automation, pp 2537–2542. https://doi.org/10.1109/ICRA.2012.6225177
Pêtrès C, Ma Romero-Ramirez, Plumet F (2012) A potential field approach for reactive navigation of autonomous sailboats. Robot Auton Syst 60(12):1520–1527. https://doi.org/10.1016/j.robot.2012.08.004
Rodríguez S, Tang X, Lien JM, Amato NM (2006) An obstacle-based rapidly-exploring random tree. Proc IEEE Int Conf Robot Autom 2006:895–900. https://doi.org/10.1109/ROBOT.2006.1641823
Rosell J, Iniguez P (2005) Path planning using harmonic functions and probabilistic cell decomposition. In: Proceedings of the 2005 IEEE international conference on robotics and automation, pp 1803–1808. https://doi.org/10.1109/ROBOT.2005.1570375
Saffiotti A (1997) The uses of fuzzy logic in autonomous robot navigation. Soft Comput. https://doi.org/10.1007/s005000050020
Saha M, Latombe JC (2005) Finding narrow passages with probabilistic roadmaps: the small step retraction method. In: 2005 IEEE/RSJ international conference on intelligent robots and systems, IROS, pp 4080–4085. https://doi.org/10.1109/IROS.2005.1545606
Sakahara H, Masutani Y, Miyazaki F (2008) Real-time motion planning in unknown environment: a Voronoi-based StRRT (spatiotemporal RRT). In: Proceedings of the SICE annual conference, pp 2326–2331. https://doi.org/10.1109/SICE.2008.4655053
Schwartz JT, Sharir M (1983) On the piano movers problem. General techniques for computing topological properties of real algebraic manifolds, II. https://doi.org/10.1016/0196-8858(83)90014-3
Sfeir J, Saad M, Saliah-Hassane H (2011) An improved artificial potential field approach to real-time mobile robot path planning in an unknown environment. In: IEEE international symposium on robotic and sensors environments (ROSE), pp 208–213. https://doi.org/10.1109/ROSE.2011.6058518
Shkolnik AC, Tedrake R (2009) Path planning in 1000+ dimensions using a task-space Voronoi bias. In: Proceedings of the IEEE international conference on robotics and automation (ICRA)
Sleumer NH, Tschichold-Gürman N (1999) Exact cell decomposition of arrangements used for path planning in robotics. In: Technical report, Department of Computer Science, ETH Zürich. https://doi.org/10.3929/ethz-a-006653440
Strandberg M (2004) Augmenting RRT-planners with local trees. In: Proceedings of the IEEE international conference on robotics and automation, 2004, ICRA ’04, vol 4, pp 3258–3262. https://doi.org/10.1109/ROBOT.2004.1308756
Sun Z, Hsu D, Jiang T, Kurniawati H, Reif JH (2005) Narrow passage sampling for probabilistic roadmap planning. IEEE Trans Robot 21(6):1105–1115. https://doi.org/10.1109/TRO.2005.853485
Šeda M (2007) Roadmap methods vs. cell decomposition in robot motion planning. In: Proceedings of the 6th WSEAS international conference on signal processing, robotics and automation (WSEAS), pp 127–132
Tanaka T, Ohwi J, Litvintseva LV, Yamafuji K, Ulyanov SV (1997) Soft computing algorithms for intelligent control of a mobile robot for service use. Soft Comput. https://doi.org/10.1007/s005000050011
Urmson C, Simmons R (2003) Approaches for heuristically biasing RRT growth. In: Proceedings of the 2003 IEEE/RSJ international conference on intelligent robots and systems (IROS 2003) (cat no. 03CH37453), vol 2, pp 1178–1183. https://doi.org/10.1109/IROS.2003.1248805
Vahrenkamp N, Kaiser P, Asfour T, Dillmann R (2011) RDT+: a parameter-free algorithm for exact motion planning. In: Proceedings of the IEEE international conference on robotics and automation, pp 715–722. https://doi.org/10.1109/ICRA.2011.5979777
van den Berg JP, Overmars MH (2005) Using workspace information as a guide to non-uniform sampling in probabilistic roadmap planners. Int J Robot Res 24(12):1055–1071. https://doi.org/10.1177/0278364905060132
Vendrell E, Mellado M, Crespo A (2001) Robot planning and re-planning using decomposition, abstraction, deduction, and prediction. Eng Appl Artif Intell 14(4):505–518. https://doi.org/10.1016/S0952-1976(01)00027-6
Vonasek V, Faigl J, Krajnik T, Preucil L (2011) A sampling schema for rapidly exploring random trees using a guiding path. In: 5th European conference on mobile robots, pp 201–206
Wang W, Li Y, Xu X, Yang SX (2010) An adaptive roadmap guided multi-RRTs strategy for single query path planning. In: Proceedings of the IEEE international conference on robotics and automation, pp 2871–2876. https://doi.org/10.1109/ROBOT.2010.5509529
Yershova A, LaValle SM (2007) Improving motion-planning algorithms by efficient nearest-neighbor searching. IEEE Trans Robot 23(1):151–157. https://doi.org/10.1109/TRO.2006.886840
Zhang Q, Chen D, Chen T (2012) An obstacle avoidance method of Soccer Robot based on evolutionary artificial potential field. https://doi.org/10.1016/j.egypro.2012.01.276
Zhong J, Su J (2011) Narrow passages identification for probabilistic roadmap method. In: Proceedings of the 30th Chinese control conference, pp 3908–3912
Acknowledgements
This work was partially supported by BUT IGA No. FSI-S-14-2533: “Applied Computer Science and Control”. This work was partially supported by the Czech Science Foundation under the project 16-08549S. This work was partially realized in CEITEC with research infrastructure supported by the project CZ.1.05/1.1.00/02.0068 financed from European Regional Development Fund.
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Abbadi, A., Matousek, R. Hybrid rule-based motion planner for mobile robot in cluttered workspace. Soft Comput 22, 1815–1831 (2018). https://doi.org/10.1007/s00500-016-2103-4
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DOI: https://doi.org/10.1007/s00500-016-2103-4