Path Planning in Expansive Configuration Spaces
Abstract¶
The authors introduce the notion of expansiveness to characterize a family of robot configuration spaces whose connectivity can be effectively captured by a roadmap of randomly-sampled milestones. The analysis of expansive configuration spaces inspired them to develop a new randomized planning algorithm that tries to sample only the portion of the configuration space that is relevant to the current query, avoiding the cost of precomputing a roadmap for the entire configuration space.
Summary¶
Introduces the Expansive Space Trees (EST) algorithm and the notion of "expansive" configuration spaces, characterizing when randomized sampling-based planners can efficiently capture connectivity. EST explores the configuration space by biasing sampling toward less explored regions, a complementary approach to RRT's goal-biased exploration.