Planning the behavior of mobile robots
It is necessary to choose a visual aid that is appropriate for the material and audience.
Among the many complex tasks in artificial intelligence, there is one, without the solution of which there can be no talk of any independent robot. It's a matter of planning. An important stage in any behavior is preparation for it, i.e. creating a plan for your action. However, in order to plan well, you need to have experience in performing these very actions - you need to learn what certain operations and sequences of actions lead to.

To increase the degree of autonomy of robotic platforms, it is necessary to create new methods for planning their interaction with objects and movement in space.

In this area, we are engaged in the integration of computer vision methods and classical symbolic planning methods, we work with simulation environments (Apollo) and real robots.

Publications
  • Schwarting W., Alonso-Mora J., Rus D. Planning and Decision-Making for Autonomous Vehicles // Annual Review of Control, Robotics, and Autonomous Systems. 2018. Т. 1. № 1. С. 187–210. Ссылка
  • Ghallab M., Nau D., Traverso P. Automated Planning and Acting // Automated Planning and Acting. 2016.
  • Panov A.I., Yakovlev K.S. Psychologically Inspired Planning Method for Smart Relocation Task // Procedia Computer Science. Elsevier, 2016. Vol. 88. P. 115–124. ScienceDirect
  • Panov A.I., Yakovlev K.S. Interaction of strategic and tactical planning of the behavior of coalitions of agents in a dynamic environment // Artificial Intelligence and Decision Making. 2016. No. 4. P. 68–78. ИИПР
  • Panov A.I., Yakovlev K. Behavior and Path Planning for the Coalition of Cognitive Robots in Smart Relocation Tasks // Robot Intelligence Technology and Applications 4. Advances in Intelligent Systems and Computing / ed. Kim J.-H. et al. Springer, 2017. Vol. 447. P. 3–20. Springer
  • Kiselev G.A., Panov A.I. Synthesis of the Behavior Plan for Group of Robots with Sign Based World Model // Interactive Collaborative Robotics. ICR 2017. Lecture Notes in Computer Science / ed. Ronzhin A., Rigoll G., Meshcheryakov R. Springer, 2017. Vol. 10459. P. 83–94. Springer
  • Panov A.I. Behavior Planning of Intelligent Agent with Sign World Model // Biologically Inspired Cognitive Architectures. 2017. Vol. 19. P. 21–31. ScienceDirect
  • Kiselev G., Kovalev A., Panov A.I. Spatial reasoning and planning in sign-based world model // Artificial Intelligence. RCAI 2018. Communications in Computer and Information Science / ed. Kuznetsov S., Osipov G.S., Stefanuk V. Springer, 2018. Vol. 934. P. 1–10. Springer
  • Киселев Г.А., Панов А.И. Знаковый подход к задаче распределения ролей в коалиции когнитивных агентов // Труды СПИИРАН. 2018. Т. 2. № 57. С. 161–187. СПИИРАН
Presentations
  • Hierarchical planning- ICR-2019. Slides
  • Behavior and trajectory planning - RCAI-2019. Slides
Required skills for trainees
  • Excellent knowledge of Python and C++
  • Technical English
  • Ability to understand technical details
  • Tendency to work with technology
Research project topics
  • Hierarchical planning of behavior and movement
  • Use Case Planning
  • Planning Behavior in Coalitions of Agents
  • Adaptive planning in the face of uncertainty
  • Planning maneuvers of an unmanned vehicle