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Combined heuristic task and motion planning for bi-manual robots

Autonomous Robots volume 43, pages 1575–1590 (2019)Cite this article

Abstract

Planning efficiently at task and motion levels allows the setting of new challenges for robotic manipulation problems, like for instance constrained table-top problems for bi-manual robots. In this scope, the appropriate combination of task and motion planning levels plays an important role. Accordingly, a heuristic-based task and motion planning approach is proposed, in which the computation of the heuristic addresses a geometrically relaxed problem, i.e., it only reasons upon objects placements, grasp poses, and inverse kinematics solutions. Motion paths are evaluated lazily, i.e., only after an action has been selected by the heuristic. This reduces the number of calls to the motion planner, while backtracking is reduced because the heuristic captures most of the geometric constraints. The approach has been validated in simulation and on a real robot, with different classes of table-top manipulation problems. Empirical comparison with recent approaches solving similar problems is also reported, showing that the proposed approach results in significant improvement both in terms of planing time and success rate.

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Notes

  1. https://sir.upc.edu/projects/kautham/.

References

  • Akbari, A., Gillani, M., & Rosell, J. (2015a). Reasoning-based evaluation of manipulation actions for efficient task planning. In ROBOT2015: Second Iberian robotics conference. Berlin: Springer.

  • Akbari, A., Gillani, M., & Rosell, J. (2015b). Task and motion planning using physics-based reasoning. In IEEE international conference on emerging technologies and factory automation.

  • Akbari, A., Gillani, M., & Rosell, J. (2016). Task planning using physics-based heuristics on manipulation actions. In IEEE international conference on emerging technologies and factory automation.

  • Azizi, V., Kimmel, A., Bekris, K., & Kapadia, M. (2017). Geometric reachability analysis for grasp planning in cluttered scenes for varying end-effectors. In 2017 13th IEEE conference on automation science and engineering (CASE), IEEE (pp. 764–769).

  • Blum, A. L., & Furst, M. L. (1997). Fast planning through planning graph analysis. Artificial intelligence, 90(1), 281–300.

    Article  MATH  Google Scholar 

  • Cambon, S., Alami, R., & Gravot, F. (2009). A hybrid approach to intricate motion, manipulation and task planning. The International Journal of Robotics Research, 28(1), 104–126.

    Article  Google Scholar 

  • Clarke, E. M., Grumberg, O., & Peled, D. (1999). Model checking. London: MIT Press.

    Google Scholar 

  • Dantam, N., Kingston, Z. K., Chaudhuri, S., & Kavraki, L. E. (2016). Incremental task and motion planning: A constraint-based approach. In Robotics: Science and systems.

  • De Moura, L., & Bjørner, N. (2011). Satisfiability modulo theories: Introduction and applications. Communications of the ACM, 54(9), 69–77.

    Article  Google Scholar 

  • de Silva, L., Pandey, A. K., Gharbi, M., & Alami, R. (2013). Towards combining HTN planning and geometric task planning. In RSS workshop on combined robot motion planning and AI planning for practical applications.

  • Dornhege, C., Eyerich, P., Keller, T., Trüg, S., Brenner, M., & Nebel, B. (2012). Semantic attachments for domain-independent planning systems. In Towards service robots for everyday environments. Berlin: Springer (pp. 99–115).

  • Erdem, E., Haspalamutgil, K., Palaz, C., Patoglu, V., & Uras, T. (2011). Combining high-level causal reasoning with low-level geometric reasoning and motion planning for robotic manipulation. In IEEE international conference on robotics and automation robotics and automation (pp. 4575–4581).

  • Erdem, E., Patoglu, V., & Schüller, P. (2016). A systematic analysis of levels of integration between high-level task planning and low-level feasibility checks. AI Communications, 29(2), 319–349.

    Article  MathSciNet  Google Scholar 

  • Garrett, C. R., Lozano-Pérez, T., & Kaelbling, L. P. (2015). FFRob: An efficient heuristic for task and motion planning. In Algorithmic foundations of robotics XI. Berlin: Springer (pp. 179–195).

  • Ghallab, M., Nau, D., & Traverso, P. (2004). Automated planning: Theory and practice. Amsterdam: Elsevier.

    MATH  Google Scholar 

  • Hauser, K. (2014). The minimum constraint removal problem with three robotics applications. The International Journal of Robotics Research, 33(1), 5–17.

    Article  MathSciNet  Google Scholar 

  • Hauser, K., & Latombe, J. C. (2010). Multi-modal motion planning in non-expansive spaces. The International Journal of Robotics Research, 29(7), 897–915.

    Article  MATH  Google Scholar 

  • Hauser, K., Ng-Thow-Hing, V., & Gonzalez-Baños, H. (2010). Multi-modal motion planning for a humanoid robot manipulation task. In Robotics research. Berlin: Springer (pp. 307–317).

  • He, K., Lahijanian, M., Kavraki, L. E., & Vardi, M. Y. (2015). Towards manipulation planning with temporal logic specifications. In 2015 IEEE international conference on robotics and automation (ICRA), IEEE (pp. 346–352).

  • Hertle, A., & Nebel, B. (2017). Identifying good poses when doing your household chores: Creation and exploitation of inverse surface reachability maps. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE (pp. 6053–6058).

  • Hoffmann, J., & Nebel, B. (2001). The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research, 14, 253–302.

    Article  MATH  Google Scholar 

  • Kaelbling, L. P., & Lozano-Pérez, T. (2011). Hierarchical task and motion planning in the now. In IEEE international conference on robotics and automation robotics and automation (pp. 1470–1477).

  • Kavraki, L. E., Svestka, P., Latombe, J. C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.

    Article  Google Scholar 

  • Kuffner, J. J., & LaValle, S. M. (2000). RRT-connect: An efficient approach to single-query path planning. In Proceedings of IEEE international conference on robotics and automation. ICRA’00, IEEE (Vol. 2, pp. 995–1001).

  • Lagriffoul, F., & Andres, B. (2016). Combining task and motion planning: A culprit detection problem. The International Journal of Robotics Research, 35(8), 890–927.

    Article  Google Scholar 

  • Lagriffoul, F., Dimitrov, D., Saffiotti, A., & Karlsson, L. (2012). Constraint propagation on interval bounds for dealing with geometric backtracking. In 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE (pp. 957–964).

  • Lagriffoul, F., Dimitrov, D., Bidot, J., Saffiotti, A., & Karlsson, L. (2014). Efficiently combining task and motion planning using geometric constraints. The International Journal of Robotics Research, 33(14), 1726–1747.

    Article  Google Scholar 

  • Lagriffoul, F., Karlsson, L., Bidot, J., & Saffiotti, R. (2013). Combining task and motion planning is not always a good idea. In RSS workshop on combined robot motion planning.

  • LaValle, S. M., & Kuffner, J. J. (2001). Randomized kinodynamic planning. The International Journal of Robotics Research, 20(5), 378–400.

    Article  Google Scholar 

  • Lifschitz, V. (2002). Answer set programming and plan generation. Artificial Intelligence, 138(1–2), 39–54.

    Article  MathSciNet  MATH  Google Scholar 

  • Lozano-Perez, T. (1983). Spatial planning: A configuration space approach. IEEE Transactions on Computers, C–32, 108–120.

    Article  MathSciNet  MATH  Google Scholar 

  • Mcdermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., & Wilkins, D. (1998). PDDL–The planning domain definition language. AIPS-98 Planning Competition Committee.

  • Muhayyuddin, G., Akbari A., & Rosell, J. (2015). Ontological physics-based motion planning for manipulation. In IEEE international conference on emerging technologies and factory automation, IEEE.

  • Muhayyuddin, G., Moll, M., Kavraki, L. E., & Rosell, J. (2018). Randomized physics-based motion planning for grasping in cluttered and uncertain environments. IEEE Robotics and Automation Letters, 3(2), 712–719. https://doi.org/10.1109/LRA.2017.2783445.

    Article  Google Scholar 

  • Pednault, E. P. D. (1989). ADL: Exploring the middle ground between strips and the situation calculus. In Proceedings of the first international conference on principles of knowledge representation and reasoning. San Francisco: Morgan Kaufmann Publishers Inc. (pp. 324–332).

  • Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., & Ng, A. Y. (2009). ROS: An open-source robot operating system. In ICRA workshop on open source software (Vol. 3, pp. 5).

  • Rosell, J., Pérez, A., Aliakbar, A., Muhayyuddin, G., Palomo, L., & García, N. (2014). The Kautham project: A teaching and research tool for robot motion planning. In IEEE international conference on emerging technologies and factory automation.

  • Siméon, T., Laumond, J. P., Cortés, J., & Sahbani, A. (2004). Manipulation planning with probabilistic roadmaps. The International Journal of Robotics Research, 23(7–8), 729–746.

    Article  Google Scholar 

  • Srivastava, S., Fang, E., Riano, L., Chitnis, R., Russell, S., & Abbeel, P. (2014). Combined task and motion planning through an extensible planner-independent interface layer. In IEEE international conference on robotics and automation robotics and automation (pp. 639–646).

  • Stilman, M., & Kuffner, J. (2008). Planning among movable obstacles with artificial constraints. The International Journal of Robotics Research, 27(11–12), 1295–1307.

    Article  MATH  Google Scholar 

  • Stilman, M., Schamburek, J. U., Kuffner, J., & Asfour, T. (2007). Manipulation planning among movable obstacles. In 2007 IEEE international conference on robotics and automation, IEEE (pp. 3327–3332).

  • Sucan, I., Moll, M., Kavraki, L. E., et al. (2012). The open motion planning library. IEEE Robotics and Automation Magazine, 19(4), 72–82.

    Article  Google Scholar 

  • Zaplana, I., Claret, J. A., & Basanez, L. B. (2018). Análisis cinemático de robots manipuladores redundantes: Aplicación a los robots kuka lwr 4+ y abb yumi. Revista Iberoamericana de Automática e Informática industrial, 15(2), 192–202.

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Industrial and Control Engineering (IOC), Universitat Politècnica de Catalunya (UPC)—Barcelona Tech, Barcelona, Spain

    Aliakbar Akbari & Jan Rosell

  2. Örebro Universitetet, 70182, Fakultetsgatan 1, Örebro, Sweden

    Fabien Lagriffoul

Authors
  1. Aliakbar Akbari
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  2. Fabien Lagriffoul
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  3. Jan Rosell
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Correspondence to Aliakbar Akbari.

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This work is partially supported by the Spanish Government through the Project DPI2016-80077-R. It is also supported by Swedish Knowledge Foundation (KKS) Project “Semantic Robots”. Aliakbar Akbari is supported by the Spanish Government through the Grant FPI 2015.

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Akbari, A., Lagriffoul, F. & Rosell, J. Combined heuristic task and motion planning for bi-manual robots. Auton Robot 43, 1575–1590 (2019). https://doi.org/10.1007/s10514-018-9817-3

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  • DOI: https://doi.org/10.1007/s10514-018-9817-3

Keywords

  • Combined task and motion planning
  • Robot manipulation
  • Geometric reasoning
  • Path planning