Guiding Sampling-Based Motion Planning by Forward and Backward Discrete Search
This paper shows how to effectively compute collision-free and dynamically-feasible robot motion trajectories from an initial state to a goal region by combining sampling-based motion planning over the continuous state space with forward and backward discrete search over a workspace decomposition. Backward discrete search is used to estimate the cost of reaching the goal from each workspace region. Forward discrete search provides discrete plans, i.e., sequences of neighboring regions to reach the goal starting from low-cost regions. Sampling-based motion planning uses the discrete plans as a guide to expand a tree of collision-free and dynamically-feasible motion trajectories toward the goal. The proposed approach, as shown by the experiments, offers significant computational speedups over related work in solving high-dimensional motion-planning problems with dynamics.
KeywordsMotion Planning Goal Region Continuous State Space Computational Speedup Discrete Search
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- 1.Choset, H., Lynch, K.M., Hutchinson, S., Kantor, G., Burgard, W., Kavraki, L.E., Thrun, S.: Principles of Robot Motion: Theory, Algorithms, and Implementations. MIT Press (2005)Google Scholar
- 7.Jaillet, L., Yershova, A., LaValle, S.M., Simeon, T.: Adaptive tuning of the sampling domain for dynamic-domain RRTs. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Canada, pp. 4086–4091 (2005)Google Scholar
- 8.Jaillet, L., Cortes, J., Simeon, T.: Transition-based RRT for path planning in continuous cost spaces. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France, pp. 2145–2150 (2008)Google Scholar
- 9.Dalibard, S., Laumond, J.P.: Control of probabilistic diffusion in motion planning. In: International Workshop on Algorithmic Foundations of Robotics, Guanajuato, Mexico, pp. 467–481 (2008)Google Scholar
- 10.Berenson, D., Srinivasa, S., Ferguson, D., Romea, A.C., Kuffner, J.: Manipulation planning with workspace goal regions. In: IEEE International Conference on Robotics and Automation, Kobe, Japan, pp. 618–624 (2009)Google Scholar
- 11.Gonzalez-Banos, H.H., Hsu, D., Latombe, J.C.: Motion planning: Recent developments. In: Autonomous Mobile Robots: Sensing, Control, Decision-Making and Applications. CRC Press (2006)Google Scholar
- 14.de Berg, M., Cheong, O., van Kreveld, M., Overmars, M.H.: Computational Geometry: Algorithms and Applications, 3rd edn. Springer (2008)Google Scholar
- 15.Brin, S.: Near neighbor search in large metric spaces. In: International Conference on Very Large Data Bases, pp. 574–584 (1995)Google Scholar
- 16.Ladd, A.M., Kavraki, L.E.: Motion planning in the presence of drift, underactuation and discrete system changes. In: Robotics: Science and Systems, Boston, MA, pp. 233–241 (2005)Google Scholar