Publications by Petter Ögren
Peer reviewed
Articles
[1]
M. Kartasev and P. Ögren, "Improving the Performance of Learned Controllers in Behavior Trees Using Value Function Estimates at Switching Boundaries," IEEE Robotics and Automation Letters, vol. 9, no. 5, pp. 4647-4654, 2024.
[2]
R. Parasuraman, B.-C. Min and P. Ögren, "Rapid prediction of network quality in mobile robots," Ad hoc networks, vol. 138, 2023.
[3]
M. Iovino et al., "A survey of Behavior Trees in robotics and AI," Robotics and Autonomous Systems, vol. 154, 2022.
[4]
P. Ögren and C. Sprague, "Behavior Trees in Robot Control Systems," Annual Review of Control, Robotics, and Autonomous Systems, vol. 5, no. 1, pp. 81-107, 2022.
[5]
C. Sprague and P. Ögren, "Continuous-Time Behavior Trees as Discontinuous Dynamical Systems," IEEE Control Systems Letters, vol. 6, pp. 1891-1896, 2022.
[6]
P. Ögren, "Convergence Analysis of Hybrid Control Systems in the Form of Backward Chained Behavior Trees," IEEE Robotics and Automation Letters, 2020.
[7]
E. Scukins and P. Ögren, "Classical Formation Patterns and Flanking Strategies as a Result of Utility Maximization," IEEE Control Systems Letters, vol. 3, no. 2, pp. 422-427, 2019.
[8]
D. Tardioli, R. Parasuraman and P. Ögren, "Pound : A multi-master ROS node for reducing delay and jitter in wireless multi-robot networks," Robotics and Autonomous Systems, vol. 111, pp. 73-87, 2019.
[9]
M. Colledancise, R. N. Parasuraman and Ö. Petter, "Learning of Behavior Trees for Autonomous Agents," IEEE Transactions on Games, 2018.
[10]
R. Parasuraman et al., "A New UGV Teleoperation Interface for Improved Awareness of Network Connectivity and Physical Surroundings," Journal of Human-Robot Interaction, vol. 6, no. 3, pp. 48-70, 2017.
[11]
M. Colledanchise and P. Ögren, "How Behavior Trees Modularize Hybrid Control Systems and Generalize Sequential Behavior Compositions, the Subsumption Architecture, and Decision Trees," IEEE Transactions on robotics, vol. 33, no. 2, pp. 372-389, 2017.
[12]
Y. Karayiannidis et al., "An Adaptive Control Approach for Opening Doors and Drawers Under Uncertainties," IEEE Transactions on robotics, vol. 32, no. 1, pp. 161-175, 2016.
[13]
Y. Wang et al., "Whole Body Control of a Dual-Arm Mobile Robot Using a Virtual Kinematic Chain," INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS, vol. 13, no. 1, 2016.
[14]
I. Kruijff-Korbayová et al., "TRADR Project : Long-Term Human-Robot Teaming for Robot Assisted Disaster Response," Künstliche Intelligenz, vol. 29, no. 2, pp. 193-201, 2015.
[15]
P. Ögren et al., "Design and implementation of a new teleoperation control mode for differential drive UGVs," Autonomous Robots, vol. 37, no. 1, pp. 71-79, 2014.
[16]
J. Robinson and P. Ögren, "On the use of gradual dense-sparse discretizations in receding horizon control," Optimal control applications & methods, vol. 35, no. 3, pp. 253-270, 2014.
[17]
J. Thunberg and P. Ögren, "A Mixed Integer Linear Programming approach to Pursuit Evasion Problems with optional Connectivity Constraints," Autonomous Robots, vol. 31, no. 4, pp. 333-343, 2011.
[18]
P. Ögren and J. W.C. Robinson, "A Model Based Approach to Modular Multi-Objective Robot Control," Journal of Intelligent and Robotic Systems, vol. 63, no. 2, pp. 257-282, 2011.
[19]
D. A. Anisi, P. Ögren and X. Hu, "Cooperative Minimum Time Surveillance With Multiple Ground Vehicles," IEEE Transactions on Automatic Control, vol. 55, no. 12, pp. 2679-2691, 2010.
[20]
P. Ögren and M. Winstrand, "Minimizing Mission Risk in Fuel Constrained UAV Path Planning," Journal of Guidance Control and Dynamics, vol. 31, no. 5, pp. 1497-1500, 2008.
[21]
P. Ögren and N. E. Leonard, "A Convergent Dynamic Window Approach to Obstacle Avoidance," IEEE Transactions on robotics, vol. 21, no. 2, pp. 188-195, 2005.
[22]
P. Ögren, E. Fiorelli and N. Leonard, "Cooperative Control of Mobile Sensor Networks : Adaptive Gradient Climbing in a Distributed Environment," IEEE Transactions on Automatic Control, vol. 49, no. 8, pp. 1292-1302, 2004.
[23]
P. Ögren, M. Egerstedt and X. Hu, "A control Lyapunov function approach to multi-agent coordination," IEEE transactions on robotics and automation, vol. 18, no. 5, pp. 847-851, 2002.
[24]
P. Ögren and C. Martin, "Vaccination Strategies for Epidemics in Highly Mobile Populations," Applied Mathematics and Computation, vol. 127, no. 2-3, pp. 261-276, 2002.
Conference papers
[25]
E. Scukins, A. N. Costa and P. Ögren, "A Data-driven Method for Estimating Formation Flexibility in Beyond-Visual-Range Air Combat," in 2024 International Conference on Unmanned Aircraft Systems (ICUAS), 2024, pp. 241-247.
[26]
E. Scukins et al., "Deep Learning Based Situation Awareness for Multiple Missiles Evasion," in 2024 International Conference on Unmanned Aircraft Systems, ICUAS 2024, 2024, pp. 1446-1452.
[27]
P. Ögren and J. Alfredson, "Creating Trustworthy AI for UAS using Labeled Backchained Behavior Trees," in 2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023, 2023, pp. 1029-1036.
[28]
E. Scukins, M. Klein and P. Ögren, "Enhancing Situation Awareness in Beyond Visual Range Air Combat with Reinforcement Learning-based Decision Support," in 2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023, 2023, pp. 56-62.
[29]
M. Kartasev, J. Salér and P. Ögren, "Improving the Performance of Backward Chained Behavior Trees that use Reinforcement Learning," in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2023, 2023, pp. 1572-1579.
[30]
D. D. Nimara et al., "Model-Based Reinforcement Learning for Cavity Filter Tuning," in Proceedings of the 5th Annual Learning for Dynamics and Control Conference, L4DC 2023, 2023, pp. 1297-1307.
[31]
E. Scukins et al., "Monte Carlo Tree Search and Convex Optimization for Decision Support in Beyond-Visual-Range Air Combat," in 2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023, 2023, pp. 48-55.
[32]
C. Sprague and P. Ögren, "Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees," in The 61th IEEE Conference on Decision and Control (CDC 2022), 2022.
[33]
C. I. Sprague and P. Ögren, "Adding Neural Network Controllers to Behavior Trees without Destroying Performance Guarantees," in 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), 2022, pp. 3989-3996.
[34]
Ö. Özkahraman and P. Ögren, "Collaborative Navigation-Aware Coverage in Feature-Poor Environments," in International Conference on Intelligent Robots and Systems (IROS), 2022, 2022.
[35]
Ö. Özkahraman et al., "Data-Driven Damage Detection and Control Adaptation for an Autonomous Underwater Vehicle," in 61st IEEE Conference on Decision and Control (CDC), 2022, 2022.
[36]
M. Pallin, J. Rashid and P. Ögren, "A Decentralized Asynchronous Collaborative Genetic Algorithm for Heterogeneous Multi-agent Search and Rescue Problems," in 2021 IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2021, 2021, pp. 1-8.
[37]
Ö. Özkahraman and P. Ögren, "Efficient Navigation Aware Seabed Coverage using AUVs," in Proceedings of 2021 IEEE International Conference on Safety, Security, and Rescue Robotics (SSRR), October 25-27 2021, New York, USA., 2021.
[38]
M. Pallin, J. Rashid and P. Ögren, "Formulation and Solution of the Multi-agent Concurrent Search and Rescue Problem," in IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2021, 2021, pp. 27-33.
[39]
E. Scukins and P. Ögren, "Using Reinforcement Learning to Create Control Barrier Functions for Explicit Risk Mitigation in Adversarial Environments," in 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021.
[40]
S. Bhat et al., "A Cyber-Physical System for Hydrobatic AUVs : System Integration and Field Demonstration," in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020, 2020.
[41]
S. Bhat et al., "A Cyber-Physical System for Hydrobatic AUVs: System Integration and Field Demonstration," in IEEE OES Autonomous Underwater Vehicles Symposium, St. Johns, Newfoundland, Canada, 2020, 2020.
[42]
Ö. Özkahraman and P. Ögren, "Combining Control Barrier Functions and Behavior Trees for Multi-Agent Underwater Coverage Missions," in Proceedings of 59th Conference on Decision and Control, 2020, 2020.
[43]
C. Sprague, D. Izzo and P. Ögren, "Learning Dynamic-Objective Policies from a Class of Optimal Trajectories," in Proceedings of the IEEE Conference on Decision and Control, 2020, pp. 597-602.
[44]
C. Sprague and P. Ögren, "Learning How to Learn Bathymetry," in 2020 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2020, 2020.
[45]
Ö. Özkahraman and P. Ögren, "Underwater Caging and Capture for Autonomous Underwater Vehicles," in Global Oceans 2020 : Singapore - U.S. Gulf Coast, 2020.
[46]
M. Colledanchise, D. Almeida and P. Ögren, "Towards Blended Reactive Planning and Acting using Behavior Trees," in 2019 International Conference on Robotics And Automation (ICRA), 2019, pp. 8839-8845.
[47]
C. Sprague et al., "Improving the Modularity of AUV Control Systems using Behaviour Trees," in AUV 2018 - 2018 IEEE/OES Autonomous Underwater Vehicle Workshop, Proceedings, 2018.
[48]
R. Parasuraman, P. Ögren and B.-C. Min, "Kalman Filter Based Spatial Prediction of Wireless Connectivity for Autonomous Robots and Connected Vehicles," in 2018 IEEE 88TH VEHICULAR TECHNOLOGY CONFERENCE (VTC-FALL), 2018.
[49]
F. Båberg and Ö. Petter, "Formation Obstacle Avoidance using RRT and Constraint Based Programming," in 2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR), 2017.
[50]
S. Caccamo et al., "RCAMP : A Resilient Communication-Aware Motion Planner for Mobile Robots with Autonomous Repair of Wireless Connectivity," in 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2017, pp. 2010-2017.
[51]
M. Colledanchise, R. M. Murray and P. Ögren, "Synthesis of Correct-by-Construction Behavior Trees," in 2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2017, pp. 6039-6046.
[52]
B. Christalin et al., "Synthesis of reactive control protocols for switch electrical power systems for commercial application with safety specifications," in 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, 2017.
[53]
F. Båberg et al., "Adaptive object centered teleoperation control of a mobile manipulator," in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 455-461.
[54]
F. Båberg et al., "Free Look UGV Teleoperation Control Tested in Game Environment : Enhanced Performance and Reduced Workload," in International Symposium on Safety,Security and Rescue Robotics, 2016.
[55]
M. Colledanchise and P. Ögren, "How Behavior Trees Generalize the Teleo-Reactive Paradigm and And-Or-Trees," in Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, 2016, 2016.
[56]
M. Colledanchise et al., "The advantages of using behavior trees in multi-robot systems," in 47th International Symposium on Robotics, ISR 2016, 2016, pp. 23-30.
[57]
C. L. R. McGhan et al., "Towards architecture-wide analysis, verification, and validation for total system stability during goal-seeking space robotics operations," in AIAA Space and Astronautics Forum and Exposition, SPACE 2016, 2016.
[58]
Y. Wang et al., "Cooperative control of a serial-to-parallel structure using a virtual kinematic chain in a mobile dual-arm manipulation application," in Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, 2015, pp. 2372-2379.
[59]
S. Caccamo et al., "Extending a UGV Teleoperation FLC Interface with Wireless Network Connectivity Information," in 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2015, pp. 4305-4312.
[60]
I. Lundberg, M. Björkman and P. Ögren, "Intrinsic camera and hand-eye calibration for a robot vision system using a point marker," in IEEE-RAS International Conference on Humanoid Robots, 2015, pp. 59-66.
[61]
Y. Wang et al., "A Distributed Convergent Solution to the Ambulance Positioning Problem on a Streetmap Graph," in The 19th IFAC world congress, Cape Town, August 24-29, 2014, 2014, pp. 9190-9196.
[62]
Y. Wang et al., "Dual Arm Manipulation using ConstraintBased Programming," in Proceedings of the 19th World CongressThe International Federation of Automatic Control, 2014, pp. 311-319.
[63]
M. Colledanchise and P. Ögren, "How Behavior Trees Modularize Robustness and Safety in Hybrid Systems," in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2014), 2014, pp. 1482-1488.
[64]
M. Colledanchise, A. Marzinotto and P. Ögren, "Performance Analysis of Stochastic Behavior Trees," in ICRA 2014, 2014.
[65]
M. Colledanchise, D. V. Dimarogonas and P. Ögren, "Robot navigation under uncertainties using event based sampling," in Decision and Control (CDC), 2014 IEEE 53rd Annual Conference on, 2014, pp. 1438-1445.
[66]
A. Marzinotto et al., "Towards a Unified Behavior Trees Framework for Robot Control," in Robotics and Automation (ICRA), 2014 IEEE International Conference on , 2014, pp. 5420-5427.
[67]
Y. Karayiannidis et al., "Interactive perception and manipulation of unknown constrained mechanisms using adaptive control," in ICRA 2013 Mobile Manipulation Workshop on Interactive Perception, 2013.
[68]
E. Salling et al., "Learning air combat parameters using differential evolution," in Frontiers in Artificial Intelligence and Applications : Twelfth Scandinavian Conference on Artificial Intelligence, 2013, pp. 225-234.
[69]
Y. Karayiannidis et al., "Model-free robot manipulation of doors and drawers by means of fixed-grasps," in 2013 IEEE International Conference on Robotics and Automation (ICRA), 2013, pp. 4485-4492.
[70]
M. Colledanchise, D. Dimarogonas and P. Ögren, "Obstacle avoidance in formation using navigation-like functions and constraint based programming," in Proceedings of the International Conference on Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ, 2013, pp. 5234-5239.
[71]
Y. Karayiannidis et al., ""Open Sesame!" Adaptive Force/Velocity Control for Opening Unknown Doors," in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, 2012, pp. 4040-4047.
[72]
Y. Karayiannidis et al., "Design of force-driven online motion plans for door opening under uncertainties," in Workshop on Real-time Motion Planning: Online, Reactive, and in Real-time, 2012.
[73]
P. Ögren et al., "A Multi Objective Control Approach to Online Dual Arm Manipulation," in Robot Control, 2012, pp. 747-752.
[74]
Y. Karayiannidis et al., "Adaptive force/velocity control for opening unknown doors," in Robot Control, Volume 10, Part 1, 2012, pp. 753-758.
[75]
P. Ögren, "Increasing Modularity of UAV Control Systems using Computer Game Behavior Trees," in AIAA Guidance, Navigation, and Control Conference 2012, 2012.
[76]
J. Thunberg, X. Hu and P. Ögren, "A Boolean Control Network Approach to Pursuit Evasion Problems in Polygonal Environments," in 2011 IEEE International Conference on obotics and Automation (ICRA), 2011, pp. 4506-4511.
[77]
J. Thunberg and P. Ögren, "An Iterative Mixed Integer Linear Programming Approach to Pursuit Evasion Problems in Polygonal Environments," in 2010 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), 2010, pp. 5498-5503.
[78]
P. Ögren and J. W.C. Robinson, "Receding Horizon Control of UAVs using Gradual Dense-Sparse Discretizations," in AIAA Conference on Guidance, Navigation and Control. Toronto, Canada. 2 - 5 Aug 2010, 2010.
[79]
J. Thunberg, D. Anisi and P. Ögren, "A comparative study of task assignment and path planning methods for multi-UGV missions," in OPTIMIZATION AND COOPERATIVE CONTROL STRATEGIES, 2009, pp. 167-180.
[80]
D. A. Anisi, T. Lindskog and P. Ögren, "Algorithms for the connectivity constrained unmanned ground vehicle surveillance problem," in European Control Conference (ECC), 2009.
[81]
D. A. Anisi, P. Ögren and X. Hu, "Communication constrained multi-UGV surveillance," in IFAC World Congress, 2008.
[82]
D. A. Anisi et al., "Cooperative Surveillance Missions with Multiple Unmanned Ground Vehicles (UGVs)," in 47TH IEEE CONFERENCE ON DECISION AND CONTROL, 2008 (CDC 2008), 2008, pp. 2444-2449.
[83]
P. Ögren, "Improved predictability of reactive robot control using Control Lyapunov Functions," in 2008 IEEE/RSJ INTERNATIONAL CONFERENCE ON ROBOTS AND INTELLIGENT SYSTEMS, VOLS 1-3, CONFERENCE PROCEEDINGS, 2008, pp. 1274-1279.
[84]
D. A. Anisi and P. Ögren, "Minimum time multi-UGV surveillance," in OPTIMIZATION AND COOPERATIVE CONTROL STRATEGIES, 2008, pp. 31-45.
[85]
P. Ögren and P. Svenmarck, "A New Control Mode for Teleoperated Differential Drive UGVs," in PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-14, 2007, pp. 446-471.
[86]
P. Ögren et al., "Autonomous UCAV Strike Missions using Behavior Control Lyapunov Functions," in AIAA Guidance, Navigation, and Control Conference, Keystone, CO, USA. 2006/08/21-2006/08/24, 2006.
[87]
P. Ögren et al., "Formulation and Solution of the UAV Paparazzi Problem," in AIAA, Guidance, Navigation and Control Conference. Keystone, CO, USA. 2006/08/21-2006/08/24, 2006.
[88]
D. Anisi, J. W.C. Robinson and P. Ögren, "On-line Trajectory planning for aerial vehicles : a safe approach with guaranteed task completion," in Collection of Technical Papers : AIAA Guidance, Navigation, and Control Conference 2006, 2006, pp. 914-938.
[89]
D. A. Anisi, P. Ögren and J. W. C. Robinson, "Safe receding horizon control of an aerial vehicle," in PROCEEDINGS OF THE 45TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-14, 2006, pp. 57-62.
[90]
P. Ögren and M. Winstrand, "Combining Path Planning and Target Assignment to Minimize Risk in a SEAD Mission," in AIAA, Guidance, Navigation and Control Conference. San Francisco, USA. 15 - 18 Aug 2005, 2005.
[91]
P. Ögren, "Split and Join of Vehicle Formations doing Obstacle Avoidance," in 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS, 2004, pp. 1951-1955.
[92]
P. Ögren and N. Leonard, "Obstacle Avoidance in Formation,," in IEEE International Conference on Robotics and Automation, 2003.
[93]
P. Ögren and N. Leonard, "A Probably Convergent Dynamic Window Approach to Obstaclen Avoidance," in IFAC World Conference, 2002.
[94]
P. Ögren and N. Leonard, "A tractable convergent dynamic window approach to obstacle avoidance," in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002.
[95]
P. Ögren, E. Fiorelli and N. Leonard, "Formations with a Mission: Stable Coordination of Vehicle Group Maneuvers," in Proc. 15th International Symposium on Mathematical Theory of Networks and Systems, 2002.
[96]
P. Ögren, M. Egerstedt and X. Hu, "A Control Lyapunov Function Approach to Multi-Agent Coordination," in IEEE Conference on Decision and Control, 2001.
[97]
P. Ögren and C. Martin, "Optimal Vaccination Strategies for the Control of Epidemics in Highly Mobile Populations," in Proceedings of the IEEE Conference on Decision and Control, 2000.
[98]
P. Ögren, M. Egerstedt and X. Hu, "Reactive mobile manipulation using dynamic trajectory tracking," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),, 2000.
[99]
P. Ögren et al., "Reactive mobile manipulation using dynamic trajectory tracking: design and implementation," in Proceedings of the IEEE Conference on Decision and Control, 2000.
[100]
M. Egerstedt et al., "Toward Optimal Control of Switched Linear Systems," in Proceedings of the IEEE Conference on Decision and Control, 2000.
Books
[101]
P. Ögren and M. Colledanchise, Behavior Trees in Robotics and AI: An Introduction. NaNth ed. CRC Press, Florida, US : CRC Press, 2018.
Non-peer reviewed
Conference papers
[102]
D. Almeida et al., "Team KTH’s Picking Solution for the Amazon Picking Challenge 2016," in Warehouse Picking Automation Workshop 2017 : Solutions, Experience, Learnings and Outlook of the Amazon Robotics Challenge, 2017.
Chapters in books
[103]
D. Almeida et al., "Team KTH’s Picking Solution for the Amazon Picking Challenge 2016," in Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment, : Springer Nature, 2020, pp. 53-62.
Theses
[104]
P. Ögren, "Formations and Obstacle Avoidance in Mobile Robot Control," Doctoral thesis Stockholm : KTH, Matematik, Trita-MAT, 03-OS-06, 2003.
Other
[105]
Y. Wang and P. Ögren, "Singularity analysis of closed-loop inverse kinematics algorithms with respect to manipulator geometric uncertainties," (Manuscript).
[106]
Y. Wang et al., "Whole body control of a dual-arm mobile robot using a virtual kinematic chain," (Manuscript).
[107]
C. Sprague and P. Ögren, "An Extended Convergence Result for Behavior Tree Controllers," (Manuscript).
[108]
F. Båberg et al., "Extended version of Adaptive Object Centered Teleoperation Control of a Mobile Manipulator," (Manuscript).
[109]
D. Anisi, J. W.C. Robinson and P. Ögren, "Online Trajectory Planning for Aerial Vehicle : A Safe Approach with Guaranteed Task Completion," (Manuscript).
Latest sync with DiVA:
2024-11-20 00:20:13