Asymptotically optimal planning by feasible kinodynamic planning in a state-cost space

Kris Hauser, Yilun Zhou

Research output: Contribution to journalArticlepeer-review


This paper presents an equivalence between feasible kinodynamic planning and optimal kinodynamic planning, in that any optimal planning problem can be transformed into a series of feasible planning problems in a state-cost space, whose solutions approach the optimum. This transformation yields a meta-algorithm that produces an asymptotically optimal planner, given any feasible kinodynamic planner as a subroutine. The meta-algorithm is proven to be asymptotically optimal and a formula is derived relating expected running time and solution suboptimality. It is directly applicable to a wide range of optimal planning problems because it does not resort to the use of steering functions or numerical boundary-value problem solvers. On a set of benchmark problems, it is demonstrated to perform, using the expansive space tree (EST) and rapidly-exploring random tree (RRT) algorithms as subroutines, at a level that is superior or comparable to related planners.

Original languageEnglish (US)
Article number7588078
Pages (from-to)1431-1443
Number of pages13
JournalIEEE Transactions on Robotics
Issue number6
StatePublished - Dec 2016
Externally publishedYes


  • Motion planning
  • nonlinear control systems
  • trajectory optimization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


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