Improving Efficiency in Mobile Robot Task Planning Through World Abstraction

IEEE Transactions on Robotics

Volumn 20, Issue 4, 2004, Pages 677-690

  Galindo, Cipriano ^a   Fernández Madrigal, Juan Antonio ^a   Gonzalez, Javier ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

Hierarchical task planning; mobile robotics; robotics; world abstraction; world modeling

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL COMPLEXITY; GRAPH THEORY; HIERARCHICAL SYSTEMS; LARGE SCALE SYSTEMS; MATHEMATICAL MODELS; MOBILE ROBOTS; PROBLEM SOLVING; REAL TIME SYSTEMS; ROBOTICS;

HIERARCHICAL TASK PLANNING; MOBILE ROBOTICS; WORLD ABSTRACTION; WORLD MODELING;

MOTION PLANNING;

EID: 4344622780     PISSN: 15523098     EISSN: 19410468     Source Type: Journal    
DOI: 10.1109/TRO.2004.829480     Document Type: Article

References (51)

1. R. Alami and R. Chatila,“An architecture for autonomy,” Int. J. Robot. Res., vol. 17, no. 4, pp. 315-337, 1998.
2. C. R. Anderson, D. E. Smith, and D. S. Weld,“Conditional effects in Graphplan,” in Proc. Int. Conf. AI Planning Systems, Pittsburgh, PA, 1998, pp. 44-53.
3. M. Asada, Y. Fukui, and S. Tsuji,“Representing global world of a mobile robot with relational local maps,” IEEE Trans. Syst., Man, Cybern., vol. 20, pp. 1456-1461, June 1990.
4. M. Baioletti, S. Marcugini, and A. Milani,“DPPlan: an algorithm for fast solutions extraction from a planning graph,” in Proc. Artificial In telligence Planning Systems, Breckenridge, CO, 2000, pp. 13-21.
5. K. Baker, Introduction to Sequencing and Scheduling. New York: Wiley, 1974.
6. G. Barish,“Approaches to integrating abstractions in Graphplan-based planning system,” CS541: Artif. Intell. Planning Final Project, Univ. of Southern California, Los Angeles, CA, Dec. 1998.
7. A. L. Blum and M. L. Furst,“Fast planning through planning graph analysis,” Artif. Intell., vol. 90, pp. 281-300, 1997.
8. R. A. Brooks,“A robust layered control system for a mobile robot,” IEEE J. Robot. Automat., vol. RA-2, pp. 14-23, Mar. 1986.
9. J. Christensen,“A hierarchical planner that generates its own hierar chies,” in Proc. 8th Nat. Conf. Artificial Intelligence, Boston, MA, 1990, pp. 1004-1009.
10. P. K. Davis and R. J. Hillestad,“Families of models that cross levels of resolution: issues for design, calibration, and management,” in Proc. Winter Simulation Conf. ACM, Los Angeles, CA, 1993, pp. 1003-1012.
11. A. Delchambre and P. Gaspart,“KBAP: An industrial prototype of knowledge-based assembly planner,” in IEEE Int. Conf. Robotics and Automation, Nice, France, 1992, pp. 2404-2409.
12. K. Erol, J. Hendler, and D. Nau,“Semantics for Hierarchical Task Net work Planning,” Dept. Comput. Sci., U. Maryland, Tech. Rep. CS-TR-3239, 1994.
13. J. A. Fernández and J. González,“Hierarchical path search for mobile robot path planning,” in IEEE Int. Conf. Robotics and Automation, Leuven, Belgium, 1998, pp. 656-661.
14. J. A. Fernández and J. González,“Mobile robot path planning and navigation using hierarchical graphs,” in Proc. ICSC Symp. Engineering of Intelligent Systems (EIS), Tenerife, Spain, 1998.
15. J. A. Fernández and J. González, Multi-Hierarchical Representation of Large-Scale Space. Boston, MA: Kluwer, 2001, vol. 24.
16. J. A. Fernández and J. González,“Multihierarchical graph search,” IEEE Trans. Pattern Anal. Ma chine Intell., vol. 24, pp. 103-113, Jan. 2002.
17. J. A. Fernández, J. González, C. Galindo, A. Reina, and A. Muñoz,“As sistive navigation using a hierarchical model of the environment,” in Proc. 3rd Int. Congress EIS'02, Málaga, Spain, 2002.
18. R. Fikes and N. J. Nilson,“STRIPS: A new approach to the applica tion of theorem providing to problem solving,” Artif. Intell., vol. 2, pp. 189-208, 1971.
19. E. Fink and Q. Yang,“A spectrum of plan justifications,” in Proc. AAAI Spring Symp., Stanford, CA, 1993, pp. 29-33.
20. E. Giunchiglia and T. Walsh,“A theory of abstraction,” Artif. Intell., vol. 56, no. 2-3, pp. 323-389, 1992.
21. F. Giunchiglia and T. Walsh,“Using abstraction,” in Proc. 8th Conf. Soc. Study of AI and Simulation of Behavior, Leeds, U.K., 1991.
22. B. Hendrickson and R. Leland,“Multidimensional spectral load bal ancing,” in Proc. 6th SIAM Conf. Parallel Processing, Scientific Com putation, Norfolk, VA, 1993, pp. 953-961.
23. M. Henning and S. Vinoski, Advanced CORBA Programming With C++. Reading, MA: Addison-Wesley, 1999.
24. J. Hoffmann and N. Bernhard,“The FF planning system: Fast plan generation through heuristic search,” J. Artif. Intell. Res., vol. 14, pp. 253-302, 2001.
25. C. A. Knoblock,“Search reduction in hierarchical problem solving,” in Proc. 9th Nat. Conf. Artificial Intelligence, Anaheim, CA, 1991, pp. 686-691.
26. C. A. Knoblock,“Learning abstraction hierarchies for problem solving,” in Proc. 8th Nat. Conf. Artificial Intelligence, Boston, MA, 1990, pp. 923-928.
27. C. A. Knoblock,“An analysis of ABSTRIPS,” in Artificial Intelligence Planning Systems: First International Conference. San Mateo, CA: Morgan Kaufmann, 1992, pp. 126-135.
28. C. A. Knoblock, Generating Abstraction Hierarchies: An Automated Approach to Reducing Search in Planning. Norwell, MA: Kluwer, 1993.
29. C. A. Knoblock,“Automatically generating abstractions for planning,” Artif. In tell., vol. 68, no. 2, pp. 243-302, 1994.
30. J. Koehler, B. Nebel, J. Hoffmann, and Y. Dimopoulos,“Extending Plan ning Graphs to an ADL Subset,” Inst. Comput. Sci., Albert Ludwigs Univ., Freiburg, Germany, Tech. Rep. 88, 1997.
31. S. Koening, R. Goodwing, and R. Simmons,“Robot navigation with Markov models: A framework for path planning and learning with limited computational resources,” in Reasoning With Uncertainty in Robotics, L. Dorst, M. van Lambarlge, and R. Voorbraak, Eds. Berlin, Germany: Springer, 1996, vol. 1903, Lecture Notes in Artificial Intelligence.
32. R. E. Korf,“Planning as search: A quantitative approach,” Artif. Intell., vol. 33, no. 1, pp. 65-88, 1987.
33. B. J. Kuipers,“The cognitive map: Could it have been any other way?,” in Spatial Orientation: Theory, Research, and Application, H. L. Picks and L. P. Acredolo, Eds. New York: Plenum, 1983.
34. B. J. Kuipers,“The spatial semantic hierarchy,” Artif. Intell., vol. 119, pp. 191-233, 2000.
35. B. J. Kuipers and Y. T. Byun,“A qualitative approach to robot exploration and map-learning,” in Proc. IEEE Workshop Spatial Reasoning and Multi-Sensor Fusion, Los Altos, CA, 1987, pp. 390-404.
36. J. C. Latombe, Robot Motion Planning. Boston, MA: Kluwer, 1991.
37. S. Minto, J. G. Carbonell, C. A. Knoblock, D. R. Kuokka, O. Etzioni, and Y. Gil,“Explanation-based learning: A problem solving perspective,” Artif. Intell., vol. 40, no. 1-3, pp. 63-118, 1989.
38. T. Mkadmi,“Speeding up state-space search by automatic abstraction,” Master's thesis, Univ. Ottawa, Ottawa, ON, Canada, 1993.
39. I.-P. Park and J. R. Kender,“Topological direction-giving and visual navigation in large environments,” Artif. Intell., vol. 78, pp. 355-395, 1995.
40. P. Perona and W. Freeman, A Factorization Approach To Grouping. New York: Springer-Verlag, 1998, vol. 1406, Lecture Notes in Computer Science, pp. 665-670.
41. G. L. Peterson and D. J. Cook,“Incorporating decision-theoretic planning in a robot architecture,” Robot. Auton. Syst., vol. 42, pp. 89-106, 2003.
42. P. F. J. Reynolds, A. Natrajan, and S. Srinivasan,“Consistency maintenance in multi-resolution simulations,” ACM Trans. Comput. Modeling Simul., vol. 7, no. 3, pp. 368-392, 1997.
43. E. D. Sacerdoti,“Planning in a hierarchy of abstraction spaces,” Artif. Intell., vol. 5, no. 2, pp. 115-135, 1974.
44. D. Schmidt. ACE+TAO Corba Homepage. [Online]. Available: http://www.cs.wustl.edu/∽schmidt/TAO.html
45. D. E. Smith, J. Frank, and A. K. Jónsson,“Bridging the gap between planning and scheduling,” Knowledge Eng. Rev., vol. 15, no. 1, pp. 47-83, 2000.
46. G. J. Sussman, A Computer Model of Skill Acquisition. New York: Elsevier, 1975.
47. H. Tamaki, H. Murac, and S. Kitamura,“Decision-making strategies for decentralized production planning and scheduling,” in Proc. IEEE Int. Symp. Industrial Electronics, L'Aquila, Italy, 2002, pp. 1352-1357.
48. R. J. Trudeau, Introduction to Graph Theory. New York: Dover, 1993.
49. M. Veloso and J. Carbonell,“Integrating planning and learning: The prodigy architecture,” J. Experimental Theoret. AI, vol. 7, pp. 81-120, 1995.
50. Q. Yang, Intelligent Planning: A Decomposition and Abstraction Based Approach. Berlin, Germany: Springer-Verlag, 1997.
51. M. Zweben and M. Fox, Intelligent Scheduling. San Mateo, CA: Morgan Kaufmann, 1994.