Laziness is defines as "the quality of being unwilling to work". It is a common approach used in many algorithms (and by many graduate students) where work, or computation, is delayed until absolutely necessary. In the context of motion planning, this idea has been frequently used to reduce the computational cost of testing if a robot collides with obstacles, an operation that governs the running time of many motion-planning algorithms. In this talk I will describe and analyze several algorithms that use this simple, yet effective idea, to dramatically improve over the state-of-the-art. A by-product of lazily performing collision detection is a shift in the computational weight in motion-planning algorithms from collision detection to nearest-neighbor search or to graph search. This induces new challenges which I will also address in my talkā"Can we employ application-specific nearest-neighbor data structures tailored for lazy motion-planning algorithms? Do we need to be completely lazy (with respect to collision detection) or should we balance laziness with, say graph operations?
Speaker: Oren Salzman, Carnegie Mellon Univ.
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