Abstract
The probabilistic roadmap technique mines out samples in the free configuration space, and every two neighbouring samples are connected by an edge if a local search algorithm can find a collision-free trajectory, thus iteratively producing a roadmap. Some sampling strategies focus on obstacle boundaries and narrow corridors for optimality and completeness, while others maximize the clearance for limiting the number of samples. The hybrid sampling technique calls different samplers for different proportions of time depending upon their assessed success. The technique further strategically selects vertex pairs to establish a connection by a stronger algorithm to add an edge, trying to connect harder areas like narrow corridors more emphatically. Sometimes, the number of edge connections is limited by prohibiting cycles or only admitting a cycle if that significantly reduces the travel cost. The collision checking may be deferred from roadmap construction to query time, making Lazy Probabilistic Roadmaps.
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