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.