Optimal Motion Planning in Unknown Workspaces Using Integral Reinforcement Learning

Panagiotis Rousseas, Charalampos P. Bechlioulis, Kostas J. Kyriakopoulos

Research output: Contribution to journalArticlepeer-review


A novel motion planning scheme for optimal navigation in unknown workspaces is proposed in this letter. Based upon the Artificial Harmonic Potential Fields (AHPFs) theory, a robust framework for provably correct (i.e., safe and globally convergent) navigation is enhanced through Integral Reinforcement Learning (IRL)1 to obtain a provably complete solution for optimal motion planning in unknown workspaces. Our method aims at bridging the gap between the control theoretic framework of mathematical rigor, with the data-driven Reinforcement Learning (RL) paradigm, while preserving the strong traits of each approach. Finally, it is compared against an RRT∗ method to asses the optimality of the final results in a multiply connected synthetic workspace.

Original languageEnglish (US)
Pages (from-to)6926-6933
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number3
StatePublished - Jul 1 2022


  • Motion and path planning
  • optimization and optimal control
  • reinforcement learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Biomedical Engineering
  • Human-Computer Interaction
  • Mechanical Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Control and Optimization
  • Artificial Intelligence


Dive into the research topics of 'Optimal Motion Planning in Unknown Workspaces Using Integral Reinforcement Learning'. Together they form a unique fingerprint.

Cite this