Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Bridging the Robot Perception Gap with Mid-Level Vision Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A General Region-Based Framework for Collaborative Planning

verfasst von : Jory Denny, Read Sandström, Nancy M. Amato

Erschienen in: Robotics Research

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Sampling-based planning is a common method for solving motion planning problems. However, this paradigm falters in difficult scenarios, such as narrow passages. In contrast, humans can frequently identify these challenges and can sometimes propose an approximate solution. A recent method called Region Steering takes advantage of this intuition by allowing a user to define regions in the workspace to weight the search space for probabilistic roadmap planners. In this work, we extend Region Steering into a generalized Region-Based framework that is suitable for any sampling-based planning approach. We explore three variants of our framework for graph-based, tree-based, and hybrid planning methods. We evaluate these variants in simulations as a proof of concept. Our results demonstrate the benefits of our framework in reducing overall planning time.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Incremental Sparse GP Regression for Continuous-Time Trajectory Estimation and Mapping
Nächstes Kapitel Robotic Invention: Challenges and Perspectives for Model-Free Design Optimization of Dynamic Locomotion Robots
Literatur
1.
Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C., Vallejo, D.: OBPRM: an obstacle-based PRM for 3D workspaces. In: Proceedings of the Third Workshop on the Algorithmic Foundations of Robotics (WAFR ‘98), pp. 155–168. A. K. Peters, Ltd., Natick, MA, USA (1998)
2.
Bayazit, O.B., Song, G., Amato, N.M.: Enhancing randomized motion planners: Exploring with haptic hints. In: Proceedings of IEEE International Conference on Robotics and Automation. (ICRA), pp. 529–536 (2000)
3.
Boor, V., Overmars, M.H., van der Stappen, A.F.: The Gaussian sampling strategy for probabilistic roadmap planners. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), vol. 2, pp. 1018–1023 (1999)
4.
Buss, A., Harshvardhan, Papadopoulos, I., Pearce, O., Smith, T., Tanase, G., Thomas, N., Xu, X., Bianco, M., Amato, N.M., Rauchwerger, L.: STAPL: Standard template adaptive parallel library. In: Proceedings of Annual Haifa Experimental Systems Conference (SYSTOR), pp. 1–10. ACM, New York, NY, USA (2010). doi:10.​1145/​1815695.​1815713
5.
Denny, J., Sandstrom, R., Julian, N., Amato, N.M.: A region-based strategy for collaborative roadmap construction. In: Proceedings of International Workshop on Algorithmic Foundations of Robotics (WAFR). Istanbul, Turkey (2014)
6.
Guo, C., Tarn, T., Xi, N., Bejczy, A.: Fusion of human and machine intelligence for telerobotic systems. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 3110–3115 (1995)
7.
8.
Hsu, D., Jiang, T., Reif, J., Sun, Z.: Bridge test for sampling narrow passages with probabilistic roadmap planners. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 4420–4426 (2003)
9.
Hsu, D., Latombe, J.C., Kurniawati, H.: On the probabilistic foundations of probabilistic roadmap planning. Int. J. Robot. Res. 25, 627–643 (2006)CrossRefMATH
10.
Hwang, Y., Cho, K., Lee, S., Park, S., Kang, S.: Human computer cooperation in interactive motion planning. In: Proceedings of IEEE International Conference on Advanced Robotics (ICAR), pp. 571–576 (1997)
11.
Ivanisevic, I., Lumelsky, V.: Augmenting human performance in motion planning tasks- the configuration space approach. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 2649–2654 (2001)
12.
Ivanisevic, I., Lumelsky, V.J.: Configuration space as a means for augmenting human performance in teleoperation tasks. IEEE Trans. Syst. Man Cybern. Part B Cybern. 30(3), 471–484 (2000)CrossRef
13.
Kavraki, L.E., Švestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996)CrossRef
14.
Kuffner, J.J., LaValle, S.M.: RRT-connect: an efficient approach to single-query path planning. In: Proceedings of IEEE International Conference on Robotics and Automatic (ICRA), pp. 995–1001 (2000)
15.
16.
Ladeveze, N., Fourquet, J.Y., Puel, B., Taix, M.: Haptic assembly and disassembly task assistance using interactive path planning. In: Virtual Reality Conference, 2009. VR 2009. IEEE, pp. 19–25 (2009)
17.
LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. Int. J. Robot. Res. 20(5), 378–400 (2001)CrossRef
18.
Lee, S., Sukhatme, G., Kim, G.J., Park, C.M.: Haptic teleoperation of a mobile robot: a user study. Presence Teleoperat. Virtual Environ. 14(3), 345–365 (2005)CrossRef
19.
Lien, J.M., Pratt, E.: Interactive planning for shepherd motion. The AAAI Spring Symposium (2009)
20.
Lin, M.C.: Efficient collision detection for animation and robotics. Ph.D. thesis, University of California, Berkeley, CA (1993)
21.
Lozano-Pérez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM 22(10), 560–570 (1979)CrossRef
22.
Masone, C., Franchi, A., Bulthoff, H.H., Giordano, P.R.: Interactive planning of persistent trajectories for human-assisted navigation of mobile robots. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 2641–2648 (2012)
23.
Morales, M., Tapia, L., Pearce, R., Rodriguez, S., Amato, N.M.: A machine learning approach for feature-sensitive motion planning. In: Algorithmic Foundations of Robotics VI, Springer Tracts in Advanced Robotics (WAFR ‘04), pp. 361–376. Springer, Berlin (2005)
24.
Plaku, E., Bekris, K.E., Chen, B.Y., Ladd, A.M., Kavraki, L.E.: Sampling-based roadmap of trees for parallel motion planning. IEEE Trans. Robot. Autom. (2005)
25.
Plaku, E., Kavraki, L., Vardi, M.: Motion planning with dynamics by a synergistic combination of layers of planning 26(3), 469–482 (2010)
26.
Reif, J.H.: Complexity of the mover’s problem and generalizations. In: Proceedings of IEEE Symposium Foundations of Computer Science (FOCS), pp. 421–427. San Juan, Puerto Rico (1979)
27.
Rodriguez, S., Tang, X., Lien, J.M., Amato, N.M.: An obstacle-based rapidly-exploring random tree. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2006)
28.
Shi, K., Denny, J., Amato, N.M.: Spark PRM: Using RRTs within PRMs to efficiently explore narrow passages. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, P. R. China (2014)
29.
Singh, A.P., Latombe, J.C., Brutlag, D.L.: A motion planning approach to flexible ligand binding. In: International Conference on Intelligent Systems for Molecular Biology (ISMB), pp. 252–261 (1999)
30.
Taïx, M., Flavigné, D., Ferré, E.: Human interaction with motion planning algorithm. J. Intel. Robot. Syst. 67(3–4), 285–306 (2012)CrossRef
31.
van den Berg, J.P., Overmars, M.H.: Using workspace information as a guide to non-uniform sampling in probabilistic roadmap planners. Int. J. Robot. Res. 24(12), 1055–1071 (2005)CrossRef
32.
Vargas Estrada, A., Lien, J.M., Amato, N.M.: Vizmo++: a visualization, authoring, and educational tool for motion planning. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA), pp. 727–732 (2006)
33.
Yan, Y., Poirson, E., Bennis, F.: Integrating user to minimize assembly path planning time in plm. Product lifecycle management for society. In: IFIP Advances in Information and Communication Technology, vol. 409, pp. 471–480. Springer, Berlin (2013)
34.
Zhang, L., Manocha, D.: An efficient retraction-based RRT planner. In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (2008)
Metadaten
Titel
A General Region-Based Framework for Collaborative Planning
verfasst von
Jory Denny
Read Sandström
Nancy M. Amato
Copyright-Jahr
2018
Buch
Robotics Research

Print ISBN: 978-3-319-60915-7

Electronic ISBN: 978-3-319-60916-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-60916-4_32

Neuer Inhalt

    Bildnachweise