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Topologically-directed navigation

Published online by Cambridge University Press:  01 March 2008

David Rawlinson*
Affiliation:
Intelligent Robotics Research Centre, Monash University, Melbourne, Australia.
Ray Jarvis
Affiliation:
Intelligent Robotics Research Centre, Monash University, Melbourne, Australia.
*
*Corresponding author. E-mail: [email protected]

Summary

Recent advances in simultaneous localization and mapping permit robots to autonomously explore enclosed environments and, subsequently, navigate to selected positions within them. But, for many tasks, it is more useful to immediately navigate to goals in unexplored environments, without a map. This is possible if a human director can describe the ideal route to the robot using grounded symbols that both parties can perceive directly.

In this paper, a mobile robot is autonomously navigated to many locations in a cluttered laboratory environment by a variety of routes. A series of topological navigation instructions are provided in advance by the director, in a form that can be expressed verbally and translates easily to software representation. The instructions are based on the perception of spatial affordances available to the robot, namely nearby junctions and edges in a pruned Generalized Voronoi Diagram. The operator can generate the instructions by viewing or imagining the environment without any measurements. Only three to five instructions are needed to navigate anywhere in our laboratory. The instructions contain only topology. No spatial measurements or environmental data such as landmarks are provided to the robot.

Type
Article
Copyright
Copyright © Cambridge University Press 2007

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References

1.Maass, W., Wazinski, P. and Herzog, G., “Vitra Guide: Multimodal Route Descriptions for Computer Assisted Vehicle Navigation,” Proceedings of the 6th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE) (1993), pp. 144–147.Google Scholar
2.Levit, M. and Roy, D., “Interpretation of spatial language in a map navigation task,” IEEE Trans. Syst., Man, Cybern. B, Cybern. 37 (3), 667679 (2006).Google Scholar
3.Blisard, S. and Skubic, M., “Modelling Spatial Referencing Language for Human–Robot Interaction,” in Proceedings of the 2005 RO-MAN Workshop (2005) pp. 698–703.Google Scholar
4.Park, I.-P. and Kender, J. R., “Topological direction-giving and visual navigation in large environments,” Artif. Intell. 78 (1–2), 355395 (1995).Google Scholar
5.Rawlinson, D. and Jarvis, R. A., “Simple yet Effective VisuoSpatial Topological Mapping,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2006) pp. 2766–2771.Google Scholar
6.Beeson, P., Jong, N. K. and Kuipers, B., “Towards Autonomous Topological Place Detection Using the Extended Voronoi Graph,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2005).Google Scholar
7.Kuipers, B., Modayil, J., Beeson, P., MacMahon, M. and Savelli, F., “Local Metrical and Global Topological Maps in the Hybrid Spatial Semantic Hierarchy,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2004) pp. 4845–4851.Google Scholar
8.Beeson, P., MacMahon, M., Modayil, J., Provost, J., Savelli, F. and Kuipers, B., “Exploiting Local Perceptual Models for Topological Map-building,” Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Reasoning with Uncertainty in Robotics (RUR) (2003) pp. 15–22.Google Scholar
9.Wang, R. F. and Spelke, E. S., “Updating egocentric representations in human navigation,” Cognition 77 (3), 215250 (2000).Google Scholar
10.Smith, R., Self, M. and Cheeseman, P., “Estimating uncertain spatial relationships in robotics,” In: Autonomous Robot Vehicles (Cox, I. J. and Wilfong, G. T., eds.) (Springer-Verlag, 1990) pp. 167193.CrossRefGoogle Scholar
11.Maybeck, P. S., Stochastic Models, Estimation and Control (Academic, New York, 1979).Google Scholar
12.Thrun, S., “A probabilistic online mapping algorithm for teams of mobile robots,” Int. J. Robot. Res. 20, 335363 (2001).Google Scholar
13.Thrun, S., Beetz, M., Bennewitz, M., Burgard, W., Cremers, A. B., Dellaert, F., Fox, D., Hhnel, D., Rosenberg, C., Roy, N., Schulte, J. and Schulz, D., “Probabilistic algorithms and the interactive museum tour-guide robot Minerva,” Int. J. Robot. Res. 19 (11), 972999 (2000).Google Scholar
14.Dempster, A., Laird, N. and Rubin, D., “Maximum likelihood from incomplete data via the EM algorithm,” J. R. Stat. Soc., Ser. B 39 (1), 138 (1977).Google Scholar
15.Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B., “FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem,” Proceedings of the AAAI National Conference on Artificial Intelligence (2002) pp. 593–598.Google Scholar
16.Tomatis, R. S. N. and Nourbakhsh, I., “Simultaneous Localization and Map Building: A Global Topological Model with Local Metric Maps,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2001) pp. 421–426.Google Scholar
17.Stachniss, C., Hahnel, D. and Burgard, W., “Exploration with Active loop-closing for fastSLAM,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2004) pp. 1505–1510.Google Scholar
18.Sim, R. and Dudek, G., “Examining Exploratory Trajectories for Minimizing Map Uncertainty,” Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) Workshop on Reasoning with Uncertainty in Robotics (RUR) (2003) pp. 69–76.Google Scholar
19.Grabowski, R., Khosla, P. and Choset, H., “Autonomous Exploration via Regions of Interest,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2003) pp. 1691–1696.Google Scholar
20.Morris, A., Silver, D., Ferguson, D. and Thayer, S., “Towards Topological Exploration of Abandoned Mines,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2005) pp. 2117–2123.Google Scholar
21.Kuipers, B. and Byun, Y.-T., “A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations,” Robot. Auton. Syst. 8, 4763 (1991).CrossRefGoogle Scholar
22.Stachniss, C., Mozos, O. M. and Burgard, W., “Speeding up Multi-Robot Exploration by Considering Semantic Place Information,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2006) pp. 1692–1697.Google Scholar
23.Stachniss, C., Grisetti, G. and Burgard, W., “Information Gain-Based Exploration Using Rao-Blackwellized Particle Filters,” Proceedings of Robotics: Science and Systems (RSS) (2005).Google Scholar
24.Salichs, M. and Moreno, L., “Navigation of mobile robots: Open questions,” Robotica 18, 227234 (2000).CrossRefGoogle Scholar
25.Guivant, J. E., Masson, F. R. and Nebot, E. M., “Simultaneous localization and map building using natural features and absolute information,” Robot. Auton. Syst. 40, 7990 (2002).Google Scholar
26.Folkesson, J. and Christensen, H. I., “Robust SLAM,” Proceedings of the IFAC Symposium on Intelligent Autonomous Vehicles (IAV) (2004).Google Scholar
27.Wang, Y., Huber, M., Papudesi, V. N. and Cook, D. J., “User-Guided Reinforcement Learning of Robot Assistive Tasks for an Intelligent Environment,” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2003).Google Scholar
28.Stentz, A., “Optimal and Efficient Path Planning for Partially-Known Environments,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (1994).Google Scholar
29.Hart, P. E., Nilsson, N. J. and Raphael, B., “A formal basis for the heuristic determination of minimum cost paths,” IEEE Trans. Syst. Sci. Cybern. 2 (4), 100107 (1968).CrossRefGoogle Scholar
30.Kuipers, B., “The spatial semantic hierarchy,” Artif. Intell. 119 (1–2), 191233 (2000).Google Scholar
31.Thrun, S., “Learning metric-topological maps for indoor mobile robot navigation,” Artif. Intell. 99 (1), 2171 (1998).CrossRefGoogle Scholar
32.Ko, B.-Y. and Song, J.-B., “Real-Time Building of a Thinning-Based Topological Map with Metric Features,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2004).Google Scholar
33.Lisien, B., Morales, D., Silver, D., Kantor, G., Rekleitis, I. and Choset, H., “The hierarchical atlas,” IEEE Trans. Robot. Autom. 21 (3), 473481 (2005).Google Scholar
34.Modayil, J., Beeson, P. and Kuipers, B., “Using the Topological Skeleton for Scalable Global Metrical Map-Building,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2004).Google Scholar
35.Tomatis, N., Nourbakhsh, I. and Siegwart, R., “Hybrid simultaneous localization and map building: A natural integration of topological and metric,” Robot. Auton. Syst. 44, 314 (2003).CrossRefGoogle Scholar
36.Poncela, A., Perez, E., Bandera, A., Urdiales, C. and Sandoval, F., “Efficient integration of metric and topological maps for directed exploration of unknown environments,” Robot. Auton. Syst. 41, 2139 (2002).CrossRefGoogle Scholar
37.Burghard, W., Cremers, A. B., Fox, D., Hahnel, D., Lakemeyer, G., Schulz, D., Steiner, W. and Thrun, S., “Experiences with an interactive museum tour-guide robot,” Artif. Intell. 1 (53), 355 (2000).Google Scholar
38.Singh, S., Simmons, R., Smith, T., Stentz, A., Verma, V., Yahja, A. and Schwehr, K., “Recent Progress in Local and Global Traversability for Planetary Rovers,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2000).Google Scholar
39.Urmson, C., Anhalt, J., Clark, M., Galatali, T., Gonzalez, J. P., Gowdy, J., Gutierrez, A., Harbaugh, S., Johnson-Roberson, M., Kato, H., Koon, P., Peterson, K., Smith, B., Spiker, S., Tryzelaar, E. and Whittaker, W. Â., “High Speed Navigation of Unrehearsed Terrain: Red Team Technology for Grand Challenge 2004,” Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, Tech. Rep. CMU-RI-04-37 (2004).Google Scholar
40.Tompkins, A. S. P. and Wettergreen, D., “Mission-level path planning and re-planning for rover exploration,” Robot. Auton. Syst. 54, 174183 (2006).CrossRefGoogle Scholar
41.Nourbakhsh, I., Powers, R. and Birchfield, S., “Dervish: An office-navigating robot,” Artif. Intell. Mag. 16 (2), 5360 (1995).Google Scholar
42.Hinkle, D., Kortenkamp, D. and Miller, D., “The 1995 robot competition and exhibition,” Artif. Intell. Mag. 17 (2), 3145 (1996).Google Scholar
43.Choset, H. and Burdick, J., “Sensor based planning. part i: The generalized Voronoi graph,” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), (1995) pp. 1649–1655.Google Scholar
44.Choset, H. and Burdick, J., “Sensor-based exploration: The hierarchical generalized Voronoi graph,” Int. J. Robot. Res. 19 (2), 96125 (2000).Google Scholar
45.Choset, H. and Nagatani, K., “Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization,” IEEE Trans. Robot. Autom., 17 (2), 125136 (2001).Google Scholar
46.Gibson, J., The Ecological Approach to Human Perception (Houghton-Mifflin, Boston, MA, 1979).Google Scholar
47.Bayes, T., “Studies in the history of probability and statistics: Ix. Thomas Bayes' essay towards solving a problem in the doctrine of chances,” Biometrika 45, 296315 (1763/1958) [Bayes' essay in modernized notation].Google Scholar
48.Elfes, A., “Sonar-based real-world mapping and navigation,” IEEE J. Robot. Autom. 3, 249265 (1987).Google Scholar
49.Elfes, A., “Using occupancy grids for mobile robot perception and navigation,” Comput. 22 (6), 4657 (1989).Google Scholar
50.Mahkovic, R. and Slivnik, T., “Constructing the generalized local Voronoi diagram from laser range scanner data,” IEEE Trans. Syst., Man, Cybern. A, Syst. Hum. 30 (6), 710719, (2000).CrossRefGoogle Scholar
51.Blum, H., A Transformation for Extracting New Descriptors of Shape (MIT Press, Cambridge, MA, 1979).Google Scholar
52.Rosenfeld, A. and Pfaltz, J. L., “Sequential operations in digital picture processing,” J. Assoc. Comput. Mach. 13, 471494 (1966).Google Scholar
53.Jarvis, R. A., “Collision-Free Path Planning in Time Varying Environments,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (1989), pp. 99–106.Google Scholar
54.Haralick, R. M. and Shapiro, L. G., Computer and Robot Vision (Addison-Wesley Longman, Reading, MA, 1992) vol. 1.Google Scholar
55.Althaus, P. and Christensen, H., “Behaviour coordination in structured environments,” Adv. Robot. 17 (7), 657674 (2003).Google Scholar
56.Schner, G. and Dose, M., “A dynamical systems approach to task-level system integration used to plan and control autonomous vehicle motion,” Robot. Auton. Syst. 10, 253267 (1992).CrossRefGoogle Scholar
57.Khatib, O., “Real-time obstacle avoidances for manipulators and mobile robots,” Int. J. Robot. Res. 5 (1), 9098 (1986).CrossRefGoogle Scholar
58.Rimon, E. and Koditscheck, D., “Exact robot navigation using artificial potential functions,” IEEE Trans. Robot. Autom. 8 (5), 501518 (1992).CrossRefGoogle Scholar