Anytime Motion Planning using the RRT*

  title={Anytime Motion Planning using the RRT*},
  author={Sertac Karaman and Matthew R. Walter and Alejandro Perez and Emilio Frazzoli and Seth J. Teller},
  journal={2011 IEEE International Conference on Robotics and Automation},
The Rapidly-exploring Random Tree (RRT) algorithm, based on incremental sampling, efficiently computes motion plans. Although the RRT algorithm quickly produces candidate feasible solutions, it tends to converge to a solution that is far from optimal. Practical applications favor “anytime” algorithms that quickly identify an initial feasible plan, then, given more computation time available during plan execution, improve the plan toward an optimal solution. This paper describes an anytime… CONTINUE READING
Highly Influential
This paper has highly influenced a number of papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 324 citations. REVIEW CITATIONS

7 Figures & Tables



Citations per Year

325 Citations

Semantic Scholar estimates that this publication has 325 citations based on the available data.

See our FAQ for additional information.