Abstract
Motion planning in continuous space is a fundamentalrobotics problem that has been approached from many per-spectives. Rapidly-exploring Random Trees (RRTs) usesampling to efficiently traverse the continuous and high-dimensional state space. Heuristic graph search methods uselower bounds on solution cost to focus effort on portions ofthe space that are likely to be traversed by low-cost solutions.In this work, we bring these two ideas together in a tech-nique called f -biasing: we use estimates of solution cost,computed as in heuristic search, to guide sparse sampling,as in RRTs. We see this new technique as strengthening theconnections between motion planning in robotics and combi-natorial search in artificial intelligence.
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