Optimal obstacle avoidance based on the Hamilton-Jacobi-Bellman equation

S. Sundar, Z. Shiller

Research output: Contribution to journalArticlepeer-review

97 Scopus citations


This paper solves the on-line obstacle avoidance problem using the Hamilton-Jacobi-Bellman (HJB) theory. Formulating the shortest path problem as a time optimal control problem, the shortest paths are generated by following the negative gradient of the return function, which is the solution of the HJB equation. To account for multiple obstacles, we avoid obstacles optimally one at a time. This is equivalent to following the pseudoreturn function, which is an approximation of the true return function for the multi-obstacle problem. Paths generated by this method are near-optimal and guaranteed to reach the goal, at which the pseudoreturn function is shown to have a unique minimum. The proposed method is computationally very efficient, and applicable for on-line applications. Examples for circular obstacles demonstrate the advantages of the proposed approach over traditional path planning methods.

Original languageEnglish
Pages (from-to)305-310
Number of pages6
JournalIEEE Transactions on Robotics and Automation
Issue number2
StatePublished - 1997
Externally publishedYes


  • Approximation theory
  • Equations of motion
  • Motion planning
  • Online systems
  • Optimal control systems
  • Hamilton Jacobi Bellman (HJB) theory
  • Collision avoidance


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