AIICS

Olov Andersson

All Publications

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2021
[9] Olov Andersson, Patrick Doherty, Mårten Lager, Jens-Olof Lindh, Linnea Persson, Elin A. Topp, Jesper Tordenlid and Bo Wahlberg. 2021.
WARA-PS: a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation.
Autonomous Intelligent Systems, 1(1):????. Springer Singapore.
DOI: 10.1007/s43684-021-00009-9.
fulltext:print: http://liu.diva-portal.org/smash/get/div...
2020
[8] Olov Andersson. 2020.
Learning to Make Safe Real-Time Decisions Under Uncertainty for Autonomous Robots.
PhD Thesis. In series: Linköping Studies in Science and Technology. Dissertations #2051. Linköping University Electronic Press. 55 pages. ISBN: 9789179298890.
DOI: 10.3384/diss.diva-163419.
Fulltext: https://doi.org/10.3384/diss.diva-163419
preview image: http://liu.diva-portal.org/smash/get/div...
[7] Olov Andersson, Per Sidén, Johan Dahlin, Patrick Doherty and Mattias Villani. 2020.
Real-Time Robotic Search using Structural Spatial Point Processes.
In 35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019), pages 995–1005. In series: Proceedings of Machine Learning Research (PMLR) #115. Association For Uncertainty in Artificial Intelligence (AUAI).
Note: Funding: Wallenberg AI, Autonomous Systems and Software Program (WASP); WASP Autonomous Research Arenas - Knut and Alice Wallenberg Foundation; Swedish Foundation for Strategic Research (SSF)Swedish Foundation for Strategic Research; ELLIIT Excellence Center at Link opingLund for Information Technology
Link: http://auai.org/uai2019/proceedings/pape...
2019
[6] Olov Andersson and Patrick Doherty. 2019.
Deep RL for Autonomous Robots: Limitations and Safety Challenges.
In , pages 489–495. ESANN. ISBN: 9782875870650.
2018
[5] Full text  Olov Andersson, Oskar Ljungqvist, Mattias Tiger, Daniel Axehill and Fredrik Heintz. 2018.
Receding-Horizon Lattice-based Motion Planning with Dynamic Obstacle Avoidance.
In 2018 IEEE Conference on Decision and Control (CDC), pages 4467–4474. In series: Conference on Decision and Control (CDC) #2018. Institute of Electrical and Electronics Engineers (IEEE). ISBN: 9781538613955, 9781538613948, 9781538613962.
DOI: 10.1109/CDC.2018.8618964.
Note: This work was partially supported by FFI/VINNOVA, the Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) funded by Knut and Alice Wallenberg Foundation, the Swedish Foundation for Strategic Research (SSF) project Symbicloud, the ELLIIT Excellence Center at Linköping-Lund for Information Technology, Swedish Research Council (VR) Linnaeus Center CADICS, and the National Graduate School in Computer Science, Sweden (CUGS).
fulltext:postprint: http://liu.diva-portal.org/smash/get/div...
2017
[4] Olov Andersson. 2017.
Methods for Scalable and Safe Robot Learning.
Licentiate Thesis. In series: Linköping Studies in Science and Technology. Thesis #1780. Linköping University Electronic Press. 37 pages. ISBN: 9789176854907.
DOI: 10.3384/lic.diva-138398.
Fulltext: https://doi.org/10.3384/lic.diva-138398
cover: http://liu.diva-portal.org/smash/get/div...
preview image: http://liu.diva-portal.org/smash/get/div...
[3] Full text  Olov Andersson, Mariusz Wzorek and Patrick Doherty. 2017.
Deep Learning Quadcopter Control via Risk-Aware Active Learning.
In Satinder Singh and Shaul Markovitch, editors, Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI), pages 3812–3818. In series: Proceedings of the AAAI Conference on Artificial Intelligence #5. AAAI Press. ISBN: 978-1-57735-784-1.
2016
[2] Full text  Olov Andersson, Mariusz Wzorek, Piotr Rudol and Patrick Doherty. 2016.
Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization.
In IEEE International Conference on Robotics and Automation (ICRA), 2016, pages 4597–4604. In series: Proceedings of IEEE International Conference on Robotics and Automation #??. Institute of Electrical and Electronics Engineers (IEEE).
DOI: 10.1109/ICRA.2016.7487661.
2015
[1] Full text  Olov Andersson, Fredrik Heintz and Patrick Doherty. 2015.
Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization.
In Blai Bonet and Sven Koenig, editors, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), pages 2497–2503. AAAI Press. ISBN: 978-1-57735-698-1.
fulltext:print: http://liu.diva-portal.org/smash/get/div...