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Randomized path planning with preferences in highly complex dynamic environments

Published online by Cambridge University Press:  22 May 2013

Khaled Belghith ,
Froduald Kabanza  and
Leo Hartman
Khaled Belghith*
Affiliation:
University Of Sherbrooke, 2500, boul. de l'Université Sherbrooke, Québec J1K 2R1, Canada
Froduald Kabanza
Affiliation:
University Of Sherbrooke, 2500, boul. de l'Université Sherbrooke, Québec J1K 2R1, Canada
Leo Hartman
Affiliation:
Canadian Space Agency, John H. Chapman Space Centre, 6767 Route de l'Aéroport Saint-Hubert, Québec J3Y 8Y9, Canada
*
*Corresponding author. E-mail: khaled.belghith@usherbrooke.ca
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Summary

In this paper we consider the problem of planning paths for articulated bodies operating in workplaces containing obstacles and regions with preferences expressed as degrees of desirability. Degrees of desirability could specify danger zones and desire zones. A planned path should not collide with the obstacles and should maximize the degrees of desirability. Region desirability can also convey search-control strategies guiding the exploration of the search space. To handle desirability specifications, we introduce the notion of flexible probabilistic roadmap (flexible PRM) as an extension of the traditional PRM. Each edge in a flexible PRM is assigned a desirability degree. We show that flexible PRM planning can be achieved very efficiently with a simple sampling strategy of the configuration space defined as a trade-off between a traditional sampling oriented toward coverage of the configuration space and a heuristic optimization of the path desirability degree. For path planning problems in dynamic environments, where obstacles and region desirability can change in real time, we use dynamic and anytime search exploration strategies. The dynamic strategy allows the planner to replan efficiently by exploiting results from previous planning phases. The anytime strategy starts with a quickly computed path with a potentially low desirability degree which is then incrementally improved depending on the available planning time.

Keywords

RoboticsPath planningReplanningAnytime planningDynamic planning
Type
Articles
Information
Robotica , Volume 31 , Issue 8 , December 2013 , pp. 1195 - 1208
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Belghith, K., Kabanza, F., Hartman, L. and Nkambou, R., “Anytime Dynamic Path-Planning with Flexible Probabilistic Roadmaps,” In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (Orlando, Florida, USA, 2006) pp. 2372–2377.Google Scholar
2.Berg, J. P. V. D. and Overmars, M. H., “Using workspace information as a guide to non-uniform sampling in probabilistic roadmap planners,” Int. J. Robot. Res. 24 (12), 1055–1071 (2005).CrossRefGoogle Scholar
3.Bohlin, R. and Kavraki, L., “Path Planning Using Lazy PRM,” In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (San Francisco, CA, USA, 2000) pp. 521–528.Google Scholar
4.Burns, B. and Brock, O., “Sampling-Based Motion Planning Using Predictive Models,” In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (Barcelona, Spain, 2005) pp. 3120–3125.Google Scholar
5.Choset, H., Lynch, K., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L. and Thrun, S., Principles of Robot Motion: Theory, Algorithms, and Implementations (MIT Press, Cambridge, MA, 2005).Google Scholar
6.Currie, N. and Peacock, B., “International Space Station Robotic Systems Operations: A Human Factors Perspective,” In: Proceedings of Human Factors and Ergonomics Society Annual Meeting, Aerospace Systems, Baltimore, Maryland (Human Factors and Ergonomics Society, Santa Monica, CA, USA, 2002) pp. 26–30.Google Scholar
7.Dean, T. and Boddy, M., “An Analysis of Time-Dependent Planning,” Proceedings of the 14th National Conference on Artificial Intelligence (AAAI) (St. Paul, Minnesota, USA, 1988).Google Scholar
8.Ferguson, D., Likhachev, M. and Stentz, A., “A Guide to Heuristic-Based Path-Planning,” In: Proceedings of ICAPS Workshop on Planning Under Uncertainty for Autonomous Systems (Monterey, California, USA, 2005) pp. 9–18.Google Scholar
9.Hart, P., Nilsson, N. and Rafael, B., “A formal basis for the heuristic determination of minimum cost paths,” J. IEEE Trans. Syst. Sci. Cybern. (SSC) 4 (2), 100–107 (1968).CrossRefGoogle Scholar
10.Hsu, D., Latombe, J. and Kurniawati, H., “On the probabilistic foundations of probabilistic roadmap planning,” Int. J. Robot. Res. 25 (7), 627–643 (2006).CrossRefGoogle Scholar
11.Hsu, D., Sanchez-Ante, G. and Sun, Z., “Hybrid PRM Sampling with a Cost-Sensitive Adaptive Strategy,” In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (Barcelona, Spain, 2005) pp. 3885–3891.Google Scholar
12.Kavraki, L., Svestka, P., Latombe, J. C. and Overmars, M., “Probabilistic Roadmaps for Path Planning in High Dimensional Configuration Spaces,” IEEE Trans. Robot. Autom. 12 (4), 566–580 (1996).CrossRefGoogle Scholar
13.Koenig, S. and Likhachev, M., “D*lite,” In: Proceedings of the 18th National Conference on Artificial Intelligence (AAAI/IAAI) (Edmonton, Alberta, Canada, 2002) pp. 476–483.Google Scholar
14.Koenig, S. and Likhachev, M., “Adaptive a*,” In: Proceedings of the 4th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (Utrecht, The Netherlands, 2005) pp. 1311–1312.Google Scholar
15.Kurniawati, H. and Hsu, D., “Workspace Importance Sampling for Probabilistic Roadmap Planning,” In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Sendai, Japan, 2004) pp. 1618–1623.Google Scholar
16.Kurniawati, H. and Hsu, D., “Workspace-based connectivity oracle: An adaptive sampling strategy for PRM planning,” In: Proceedings of the 7th International Workshop on the Algorithmic Foundations of Robotics (WAFR), New York, USA (Akella, S.et al., eds.) (Springer, Berlin, Germany, 2006).Google Scholar
17.Lavalle, S. M., Planning Algorithms (Cambridge University Press, Cambridge, UK, 2006).CrossRefGoogle Scholar
18.LaValle, S., Branicky, M. and Lindemann, S., “On the relationship between classical grid search and probabilistic roadmaps,” Int. J. Robot. Res. 23 (7–8), 673–692 (2004).CrossRefGoogle Scholar
19.Likhachev, M., Ferguson, D., Gordon, G. J., Stentz, A. and Thrun, S., “Anytime search in dynamic graphs,” Artif. Intell. 172 (14), 1613–1643 (2008).CrossRefGoogle Scholar
20.Likhachev, M., Ferguson, D. I., Gordon, G. J., Stentz, A. and Thrun, S., “Anytime Dynamic a*: An Anytime, Replanning Algorithm,” In: Proceedings of the 15th International Conference on Automated Planning and Scheduling (ICAPS) (Monterey, California, USA, 2005) pp. 262–271.Google Scholar
21.Likhachev, M., Gordon, G. and Thrun, S., “ARA*: Anytime A* Search with Provable Bounds on Sub-Optimality,” Proceedings of the 17th Annual Conference on Neural Information Processing Systems (NIPS) (Vancouver, British Columbia, Canada, 2003).Google Scholar
22.Melchior, P., Orsoni, B., Lavialle, O., Poty, A. and Oustaloup, A., “Consideration of Obstacle Danger Level in Path Planning Using a* and Fast-Marching Optimisation: Comparative Study,” J. Signal Process. 83 (11), 2387–2396 (2003).CrossRefGoogle Scholar
23.Nilsson, N., Principles of Artificial Intelligence (Tioga, Wellsboro, PA, 1980).Google Scholar
24.Saha, M., Latombe, J., Chang, Y. and Prinz, F., “Finding narrow passages with probabilistic roadmaps: The small-step retraction method,” J. Auton. Robots 19 (3), 301–319 (2005).CrossRefGoogle Scholar
25.Sanchez, G. and Latombe, J., “A Single-Query Bi-Directional Probabilistic Roadmap Planner with Lazy Collision Checking,” In: Proceedings of the 10th International Symposium on Robotics Research (ISRR) (Lorne, Victoria, Australia, 2001) pp. 403–417.Google Scholar
26.Sent, D. and Overmars, M., “Motion Planning in Environments with Danger zones,” In: Proceedings of IEEE International Conference on Robotics and Automation (ICRA) (Seoul, Korea, 2001) pp. 1488–1493.Google Scholar
27.Sun, X., Koenig, S. and Yeoh, W., “Generalized Adaptive a*,” In: Proceedings of the 7th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (Estoril, Portugal, 2008) pp. 469–476.Google Scholar