Effectiveness evaluation of precomputation search using steering set

We present a new pruning method for compact precomputed search trees and evaluate the effectiveness and the efficiency of our precomputation planning with steering sets. Precomputed search trees are one method for reducing planning time; however, there is a time-memory trade off. Our precomputed search tree (PCS) is built with pruning based on a rule of constant memory, the maximum-size pruning method (MSP), which is the preset ratio of pruning. Using MSP, we get a large precomputed search tree of reasonable size. Additionally, we apply the node selection strategy (NSS) to MSP. We extend the outer edge of the tree and enhance the path reachability. In maps with less than a 12% obstacle rate, the runtime of precomputation planning is more than two orders of magnitude faster than the planning without precomputed search trees. Our precomputed search tree with steering sets finds an optimal path in the map of its obstacle rate at 20%. Then, our precomputation planning speedily produces the smooth optimal path in an indoor environment.

[1]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  L. Shepp,et al.  OPTIMAL PATHS FOR A CAR THAT GOES BOTH FORWARDS AND BACKWARDS , 1990 .

[3]  Jehee Lee,et al.  Precomputing avatar behavior from human motion data , 2006, Graph. Model..

[4]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[5]  Lydia E. Kavraki,et al.  Randomized preprocessing of configuration space for path planning: articulated robots , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).

[6]  Harry Shum,et al.  Bi-scale radiance transfer , 2003, ACM Trans. Graph..

[7]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[8]  Doug L. James,et al.  Precomputing interactive dynamic deformable scenes , 2003, ACM Trans. Graph..

[9]  Manfred Lau,et al.  Precomputed search trees: planning for interactive goal-driven animation , 2006, SCA '06.

[10]  Manfred Lau,et al.  Behavior planning for character animation , 2005, SCA '05.

[11]  Makoto Matsumoto,et al.  SIMD-Oriented Fast Mersenne Twister: a 128-bit Pseudorandom Number Generator , 2008 .

[12]  E. Feron,et al.  Real-time motion planning for agile autonomous vehicles , 2000, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[13]  J. Kuffner.  Efficient Optimal Search of Euclidean-Cost Grids and Lattices , 2004 .

[14]  Pat Hanrahan,et al.  All-frequency shadows using non-linear wavelet lighting approximation , 2003, ACM Trans. Graph..

[15]  Takeo Kanade,et al.  Rotation Invariant Neural Network-Based Face Detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[16]  Satoshi Kagami,et al.  Smooth path planning with pedestrian avoidance for wheeled robots: Implementation and evaluation , 2000, 2009 4th International Conference on Autonomous Robots and Agents.

[17]  Lydia E. Kavraki,et al.  Randomized preprocessing of configuration for fast path planning , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[18]  James J. Kuffner,et al.  Autonomous behaviors for interactive vehicle animations , 2004, SCA '04.

[19]  Satoshi Kagami,et al.  2P2-E08 Path Planning with Steering Sets for Car-Like Robots , 2006 .