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Autonomous Robots

Erschienen in:

01.01.2014

Object search by manipulation

verfasst von: Mehmet R. Dogar, Michael C. Koval, Abhijeet Tallavajhula, Siddhartha S. Srinivasa

Erschienen in: Autonomous Robots | Ausgabe 1-2/2014

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Abstract

We investigate the problem of a robot searching for an object. This requires reasoning about both perception and manipulation: some objects are moved because the target may be hidden behind them, while others are moved because they block the manipulator’s access to other objects. We contribute a formulation of the object search by manipulation problem using visibility and accessibility relations between objects. We also propose a greedy algorithm and show that it is optimal under certain conditions. We propose a second algorithm which takes advantage of the structure of the visibility and accessibility relations between objects to quickly generate plans. Our empirical evaluation strongly suggests that our algorithm is optimal under all conditions. We support this claim with a partial proof. Finally, we demonstrate an implementation of both algorithms on a real robot using a real object detection system.

Vorheriger Artikel Autonomously learning to visually detect where manipulation will succeed

Nächster Artikel Analyzing dexterous hands using a parallel robots framework

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We use two-dimensional examples, e.g. Fig. 2, throughout the paper for clarity of illustration. Our actual formulation and implementation uses complete three-dimensional models of the scene, objects, and volumes.

In the rare event that that multiple sequences share the maximum utility, the algorithm breaks the tie by choosing the sequence with the maximum utility prefix recursively.

We use \(\Lambda ^\pi \) in place of \(V^\pi \) to denote the value function to avoid confusion with revealed volume \(V_s\).

Anand, A., Koppula, H. S., Joachims, T., & Saxena, A. (2013). Contextually guided semantic labeling and search for three-dimensional point clouds. International Journal of Robotics Research, 32(1), 19–34.CrossRef

Baird, D. (1995). Experimentation: An introduction to measurement theory and experiment design. Englewood Cliffs: Prentice-Hall.

Ben-Shahar, O., & Rivlin, E. (1998). Practical pushing planning for rearrangement tasks. IEEE Transactions on Robotics and Automation, 14, 549–565.CrossRef

Chen, P., & Hwang, Y. (1991). Practical path planning among movable obstacles. IEEE: In IEEE International Conference on Robotics and Automation.

de Berg, M., Cheong, O., van Kreveld, M., & Overmars, M. (2008). Computational geometry: Algorithms and applications. Berlin: Springer.

Diankov, R., & Kuffner, J. (2008). OpenRAVE: A planning architecture for autonomous robotics. Technical Report CMU-RI-TR-08-34, Robotics Institute.

Dogar, M., & Srinivasa, S. (2012). A planning framework for non-prehensile manipulation under clutter and uncertainty. Autonomous Robots, 33(3), 217–236.CrossRef

Espinoza, J., Sarmiento, A., Murrieta-Cid, R., & Hutchinson, S. (2011). Motion planning strategy for finding an object with a mobile manipulator in three-dimensional environments. Advanced Robotics, 25, 1627–1650.CrossRef

Gupta, M., & Sukhatme, G. (2012). Interactive perception in clutter. In R. S. S. The (Ed.), 2012. Workshop on Robots in Clutter: Manipulation, Perception and Navigation in Human Environments.

Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107.CrossRef

Hauser, K. (2012). The minimum constraint removal problem with three robotics applications. In Workshop on the Algorithmic Foundations of Robotics (WAFR).

Hopcroft, J., & Tarjan, R. (1973). Algorithm 447: Efficient algorithms for graph manipulation. Communications of the ACM, 16(6), 372–378.CrossRef

Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Researc, 4, 237–285.

Kaelbling, L., & Lozano-Perez, T. (2012). Unifying perception, estimation and action for mobile manipulation via belief space planning. IEEE: In IEEE International Conference on Robotics and Automation.

Kollar, T., & Roy, N. (2009). Utilizing object-object and object-scene context when planning to find things. IEEE: In IEEE International Conference on Robotics and Automation.

Ma, J., Chung, T. H., & Burdick, J. (2011). A probabilistic framework for object search with 6-dof pose estimation. International Journal of Robotics Research, 30(10), 1209–1228.CrossRef

Martinez, M., llet, A., & Srinivasa, S. (2010). MOPED: A scalable and low latency object recognition and pose estimation system. In IEEE International Conference on Robotics and Automation. IEEE.

Moizumi, K., & Cybenko, G. (2001). The traveling agent problem. Mathematics of Control, Signals, and Systems, 14(3), 213–232.CrossRefMATHMathSciNet

Ota, J. (2009). Rearrangement planning of multiple movable objects by a mobile robot. Advanced Robotics, 23, 1–18.CrossRef

Overmars, M. H., Nieuwenhuisen, D., Nieuwenhuisen, D., Frank, A., & Overmars, H. (2006). An effective framework for path planning amidst movable obstacles. In International Workshop on the Algorithmic Foundations of Robotics.

Shubina, K., & Tsotsos, J. (2010). Visual search for an object in a 3D environment using a mobile robot. Computer Vision and Image Understanding, 114, 535–547.CrossRef

Sjo, K., Lopez, D., Paul, C., Jensfelt, P., & Kragic, D. (2009). Object search and localization for an indoor mobile robot. Journal of Computing and Information Technology, 17, 67–80.

Srinivasa, S., Berenson, D., Cakmak, M., Dogar, M., Dragan, A., Knepper, R. A., et al. (2012). Herb 2.0: Lessons learned from developing a mobile manipulator for the home. Proceedings of the IEEE, 100(8), 1–19.CrossRef

Stilman, M., Schamburek, J. U., Kuffner, J., & Asfour, T. (2007). Manipulation planning among movable obstacles. In IEEE International Conference on Robotics and Automation. IEEE.

van den Berg, J. P., Stilman, M., Kuffner, J., Lin, M. C., & Manocha, D. (2008). Path planning among movable obstacles: A probabilistically complete approach. In International Workshop on the Algorithmic Foundations of Robotics (pp. 599–614).

van Hoof, H., Kroemer, O., Amor, H. B., & Peters, J. (2012). Maximally informative interaction learning for scene exploration. In IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 5152–5158). IEEE.

Wilfong, G. (1988). Motion planning in the presence of movable obstacles. In Proceedings of the Fourth Annual Symposium on Computational Geometry (pp. 279–288).

Wong, L. L., Kaelbling, L. P., & Lozano-Pérez, T. (2013). Manipulation-based active search for occluded objects. In IEEE International Conference on Robotics and Automation. IEEE.

Ye, Y., & Tsotsos, J. (1995). Where to look next in 3D object search. In IEEE International Symposium on Computer Vision. IEEE.

Ye, Y., & Tsotsos, J. (1999). Sensor planning for 3D object search. Computer Vision and Image Understanding, 73(2), 145–168.CrossRef

Titel: Object search by manipulation
verfasst von: Mehmet R. Dogar
Michael C. Koval
Abhijeet Tallavajhula
Siddhartha S. Srinivasa
Publikationsdatum: 01.01.2014
Verlag: Springer US
Erschienen in: Autonomous Robots / Ausgabe 1-2/2014
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI: https://doi.org/10.1007/s10514-013-9372-x

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