Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-21T22:58:21.546Z Has data issue: false hasContentIssue false
  • English
  • Français

Robot navigation algorithms using learned spatial graphs†

Published online by Cambridge University Press:  09 March 2009

S. S. Iyengar ,
C. C. Jorgensen ,
S. V. N. Rao  and
C. R. Weisbin
S. S. Iyengar
Affiliation:
Dept. of Computer Science, Louisiana State Univ., Baton Rouge, LA 70803 (USA).
C. C. Jorgensen
Affiliation:
Engineering Physics and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (USA).
S. V. N. Rao
Affiliation:
Dept. of Computer Science, Louisiana State Univ., Baton Rouge, LA 70803 (USA).
C. R. Weisbin
Affiliation:
Engineering Physics and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831 (USA).
Get access Rights & Permissions [Opens in a new window]

Summary

Finding optimal paths for robot navigation in a known terrain has been studied for some time but, in many important situations, a robot would be required to navigate in completely new or partially explored terrain. We propose a method of robot navigation which requires no pre-learned model, makes maximal use of available information, records and synthesizes information from multiple journeys, and contains concepts of learning that allow for continuous transition from local to global path optimality. The model of the terrain consists of a spatial graph and a Voronoi diagram. Using acquired sensor data, polygonal boundaries containing perceived obstacles shrink to approximate the actual obstacles surfaces, free space for transit is correspondingly enlarged, and additional nodes and edges are recorded based on path intersections and stop points. Navigation planning is gradually accelerated with experience since improved global map information minimizes the need for further sensor data acquisition. Our method currently assumes obstacle locations are unchanging, navigation can be successfully conducted using two-dimensional projections, and sensor information is precise.

Type
Article
Information
Robotica , Volume 4 , Issue 2 , April 1986 , pp. 93 - 100
Copyright
Copyright © Cambridge University Press 1986

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Hart, P. et al. , “A Formal Basis for the Heuristic Determination of Minimum Cost Paths” IEEE Trans. Sys. Sci. Cyber, SSC-4(2), 100–107 (1968).CrossRefGoogle Scholar
2.Nilsson, N., “Mobile Automation: An Application of Artificial Intelligence Techniques” Proceedings of First International Joint Conference on Artificial Intelligence 509–520 (05, 1969).Google Scholar
3.Thompson, A.M., ‘The Navigation System of the JPL Robot’ Proceedings of the Fifth International Joint Conference on Artificial Intelligence Cambridge, MA, 749–757 (08 22–25, 1977).Google Scholar
4.Giralt, G. et al. , “A Multilevel Planning and Navigation System for a Mobile Robot” Proceedings of the Sixth International Joint Conference on Artificial Intelligence Tokyo, Japan335–338 (08 20–23, 1979).Google Scholar
5.Giralt, G. et al. , “An Integrated Navigation and Motion Control System for Autonomous Multisensory Mobile Robots” Robotics Research 191–214 (1984).Google Scholar
6.Moravec, H. P., ‘Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover’ Carnegie-Mellon Robotics Institute, Pittsburgh, PA. Technical Report CMU-RI-TR- (09 3, 1980).Google Scholar
7.Wallace, R. et al. , “First Results in Robot Road Following” Proceedings of the Ninth International Joint Conference on Artificial Intelligence Los Angeles, CA1089–1095 (08 18–23, 1985).Google Scholar
8.Kanamaya, Y. et al. , “A Mobile Robot with Sonic Sensors and Its Understanding of a Simple World” Proceedings of Seventh International Joint Conference on Artificial Intelligence (1981) (see also IECON '84 1984, p. 303).Google Scholar
9.Weisbin, C.R., Barhen, J., de Saussure, G., Hamel, W.R., Jorgensen, C.C., Lucius, J.L., Oblow, E.M. and Swift, T.E., “Machine Intelligence for Robotics Applications” Proceedings of the 1985 Conference on Intelligent Systems and Machines Oakland, MI (04 22–24, 1985).Google Scholar
10.Brooks, R.A., “Solving the Find-Path Problem by Good Representation of Free-Space” IEEE Trans. Systems, Man and Cybernetics SMC-13, No. 3 (03/04 1983).Google Scholar
11.Brooks, R.A., “Planning Collision-Free Motions for Pick-and-Place Operations” Robotics Research 2, No. 4, 19–44 (Winter, 1983).CrossRefGoogle Scholar
12.Brooks, R.A.and Lozano-Perez, T., “A Subdivision Algorithm in Configuration Space for Find Path with Rotation” IEEE Trans. Systems, Man and Cybernetics SMC-15, No. 2, 224–233 (03/04, 1985).CrossRefGoogle Scholar
13.Lozano-Perez, T., “Automatic Planning of Manipulator Transfer Movements” IEEE Trans. Systems. Man, and Cybernetics SMC-11, 681–689 (10, 1981).CrossRefGoogle Scholar
14.Lozano-Perez, T., “Spatial Planning: A Configuration Space Approach” IEEE Trans. Computers C-32, 108–120 (02, 1983).CrossRefGoogle Scholar
15.Lozano-Perez, T., and Wesley, M.A., “An Algorithm for Planning Collision-Free Paths Among Polyhedral Obstacles” Commun. ACM 22, No. 10, 560–570 (10, 1979).CrossRefGoogle Scholar
16.Hopcroft, J.E., Schwartz, J.T. and Sharir, M., “On the Complexity of Motion Planning for Multiple Independent Objects; PSPACE Hardness of the Warehouseman's Problem” Robotics Research 3(4), 76–88, (1984).CrossRefGoogle Scholar
17.Sharir, M. and Ariel-Sheffi, E., “On the Piano Movers' Problem: IV. Various Decomposable Two-Dimensional Motion Planning Problems” Comm. Pure and Applied Mathematics 37, 479–493 (1984).CrossRefGoogle Scholar
18.Schwartz, J.T. and Sharir, M., “On the Piano Movers' Problem: V. The Case of a Rod Moving in Three-Dimensional Space Amidst Polyhedral Obstacles” Comm. Pure and Applied Mathematics 37, 815–848 (1984).CrossRefGoogle Scholar
19.Crowley, J., “Navigation for an Intelligent Mobile Robot” IEEE Journal of Robotics and Automation RA-1, No. 1, 31, ff (03, 1985).CrossRefGoogle Scholar
20.Parodi, A., “Multi-Goal Real-time Global path Planning for an Autonomous Land Vehicle Using a High-Speed Graph Search Processor” IEEE International Conference on Robotics and Automation, St. Louis, Missouri, 161–167 (1985).Google Scholar
21.Chattergy, A., “Some Heuristics for the Navigation of a Robot” Robotics Research 4, No. 1, Spring, 59–66 (Spring, 1985).CrossRefGoogle Scholar
22.Lee, D.T. and Preparta, F.P., “Computational Geometry— A Survey” IEEE Transactions on Computers C-33, No. 12, 1072–1101 (12, 1984).CrossRefGoogle Scholar