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Petter Ögren

Profile picture of Petter Ögren

PROFESSOR

Details

Address
LINDSTEDTSVÄGEN 24

Researcher

Researcher ID

About me

Petter Ögren was born in Stockholm, Sweden, in 1974. He received the M.S. degree in engineering physics and the Ph.D. degree in applied mathematics from the Royal Institute of Technology (KTH), Stockholm, Sweden, in 1998 and 2003, respectively. In the fall of 2001, he visited the Mechanical Engineering Department, Princeton University, Princeton, NJ. From 2003 to 2012 he worked as a senior scientist and deputy research director in Autonomous Systems at the Swedish Defence Research Agency (FOI). In 2013 he started as an Associate Professor at KTH, and since 2018, he is a Professor at the division of Robotics, Perception and Learning (RPL) at KTH.

My recent book, Behavior Trees in Robotics and AI.

Research areas

His current research interests include modular and flexible control systems, multi-agent systems, and human robot teaming, with the joint goal of realizing robot systems that are robust, safe and easy to use. 

Modular Robot Control Architectures

Modularity is a key enabler for designing and understanding complex systems. In this research activity we have investigated the use of a new modular task switching control structure called Behavior Trees (BTs). It turns out that BTs generalize a number of earlier control structures, such as Decision Trees, the Subsumption Architecture, and the Teleo-Reactive approach. The modularity furthermore enables analysis of system performance across compositions of sub-systems into more complex systems, and also provides a convenient structure for both planning and learning approaches.

Coordination of Multi-agent teams

A Multi-agent team performing a task can often improve robustness, flexibility and performance, as compared to a few more complex agents. Robustness is improved by reducing the impact of single failures. Flexibility is improved by the possibility of moving agents to different locations to perform simultaneous work, and performance can be improved by e.g. providing sensing capabilities across a larger area in a search task.
In this research activity we study how to design efficient decision making schemes for Multi-agent teams performing different tasks, such as formation obstacle avoidance, search, pursuit-evasion, patrolling, or performing adversarial games.

Human-robot teaming

Humans and robots have complementary skills in many areas. Robots often have good precision, endurance, repeatability, predictability and focus, while humans are superior in high level understanding and decision making, innovation and improvisation, adaptability and fine manipulation.  In this research activity we explore combining the best qualities in both to maximize team performance.

Supervision of PhD students

  • David Anisi  
    • Assistant Supervisor, Project manager, Coordinator of the funding project (AURES)
  • Johan Thunberg  
    • Assistant Supervisor, project funded by the Swedish Space Corporation (SSC)
  • Yuquan Wang  
    • Main Supervisor, project funded by the EU through the project RoboHow
  • Michele Colledanchise  
    • Main Supervisor, project funded by the VR through the project CARMA
  • Alejandro Marzinotto  
    • Assistant Supervisor, project funded by the VR through the project CARMA
  • Erik Ward 
    • Assistant Supervisor, project funded by Vinnova through the project IQMatic
  • Fredrik Båberg  
    • Main Supervisor, project funded by the EU through the project TRADR
  • Sergio Caccamo  
    • Assistant Supervisor, project funded by the EU through the project TRADR
  • Diogo Almeida 
    • Assistant Supervisor, project funded by the EU through the project SARAFun
  • Özer Özkahraman
    • Main Supervisor, project funded by SSF through the project SMaRC
  • Chris Sprague 
    • Main Supervisor, project funded by SSF through the project SMaRC
  • Martin Pallin 
    • Main Supervisor, project funded by WASP 
  • Edvards Scukins
    • Main Supervisor, project funded by Vinnova through the project ARCOP

Keywords: Behavior Trees, Control Architectures, Human-robot teaming, Multi-agent coordination

A google scholar citation count can be found here.


Courses

Artificial Intelligence and Multi Agent Systems (DD2438), examiner, course responsible, teacher | Course web

Degree Project in Computer Science and Engineering, specialising in ICT Innovation, Second Cycle (DA258X), examiner | Course web

Degree Project in Computer Science and Engineering, specializing in Industrial Management, Second Cycle (DA235X), examiner | Course web

Degree Project in Computer Science and Engineering, specializing in Machine Learning, Second Cycle (DA233X), examiner | Course web

Degree Project in Computer Science and Engineering, specializing in Systems, Control and Robotics, Second Cycle (DA236X), examiner | Course web

Degree Project in Electrical Engineering, Second Cycle (EA238X), examiner | Course web

Degree Project in Electrical Engineering, Second Cycle (EA250X), examiner | Course web

Degree Project in Electrical Engineering, specializing in Systems, Control and Robotics, Second Cycle (EA236X), examiner | Course web

Introduction to Robotics (DD2410), teacher | Course web

The Sustainable Systems and Control Engineer (EL2221), examiner, course responsible | Course web

The Sustainable Systems and Control Engineer (EL2220), examiner, course responsible | Course web

Topics in Robotics I (FDD3353), examiner, course responsible | Course web

Topics in Robotics II (FDD3354), examiner, course responsible | Course web

Topics in Robotics III (FDD3355), examiner, course responsible | Course web