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

@inproceedings{Janson2015DeterministicSM,
  title={Deterministic Sampling-Based Motion Planning: Optimality, Complexity, and Performance},
  author={Lucas Janson and Brian Ichter and Marco Pavone},
  booktitle={ISRR},
  year={2015}
}

• Lucas Janson, Brian Ichter, Marco Pavone
• Published 2015 in ISRR
• DOI:10.1007/978-3-319-60916-4_29

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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