Harmonic-Based Optimal Motion Planning in Constrained Workspaces Using Reinforcement Learning

Panagiotis Rousseas, Charalampos Bechlioulis, Kostas J. Kyriakopoulos

Research output: Contribution to journalArticlepeer-review


In this work, we propose a novel reinforcement learning algorithm to solve the optimal motion planning problem. Particular emphasis is given on the rigorous mathematical proof of safety, convergence as well as optimality w.r.t. to an integral quadratic cost function, while reinforcement learning is adopted to enable the cost function's approximation. Both offline and online solutions are proposed, and an implementation of the offline method is compared to a state-of-the-art RRT$^{\star }$ approach. This novel approach inherits the strong traits from both artificial potential fields, i.e., reactivity, as well as sampling-based methods, i.e., optimality, and opens up new paths to the age-old problem of motion planning, by merging modern tools and philosophies from various corners of the field.

Original languageEnglish (US)
Article number9359451
Pages (from-to)2005-2011
Number of pages7
JournalIEEE Robotics and Automation Letters
Issue number2
StatePublished - Apr 2021


  • 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


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