Principles of artificial intelligence
Principles of artificial intelligence
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2003 Papers
All-frequency shadows using non-linear wavelet lighting approximation
ACM SIGGRAPH 2003 Papers
Precomputing interactive dynamic deformable scenes
ACM SIGGRAPH 2003 Papers
Behavior planning for character animation
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Autonomous behaviors for interactive vehicle animations
Graphical Models - Special issue on SCA 2004
Precomputing avatar behavior from human motion data
Graphical Models - Special issue on SCA 2004
Planning Algorithms
Precomputed search trees: planning for interactive goal-driven animation
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
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We present a high-speed planning method with compact precomputed search trees using a new pruning method and evaluate the effectiveness and the efficiency of our precomputation planning. Its speed is faster than an A* planner in maps in which the obstacle rate is the same as indoor environments. Precomputed search trees are one way of 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 a preset ratio of pruning. Using MSP, we get a large precomputed search tree which is a 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 less than 30% obstacle rates on a map, the runtime of precomputation planning is more than one order of magnitude faster than the planning without precomputed search trees. Our precomputed tree finds an optimal path in maps with 25% obstacle rates. Then our precomputation planning speedily produces the optimal path in indoor environments.