Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance

  title={Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance},
  author={Lucas Janson and Brian Ichter and Marco Pavone},
Probabilistic sampling-based algorithms, such as the probabilistic roadmap (PRM) and the rapidly-exploring random tree (RRT) algorithms, represent one of the most successful approaches to robotic motion planning, due to their strong theoretical properties (in terms of probabilistic completeness or even asymptotic optimality) and remarkable practical performance. Such algorithms are probabilistic in that they compute a path by connecting independently and identically distributed (i.i.d.) random… CONTINUE READING
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