Path planning for a robot manipulator based on probabilistic roadmap and reinforcement learning

Jung Jun Park, Ji Hun Kim, Jae Bok Song

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)


The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

Original languageEnglish
Pages (from-to)674-680
Number of pages7
JournalInternational Journal of Control, Automation and Systems
Issue number6
Publication statusPublished - 2007 Dec


  • Path planning
  • Probabilistic roadmap
  • Reinforcement learning
  • Robot manipulator

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications


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