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Doherty, Patrick
Alternative names
Publications (10 of 220) Show all publications
Secolo, A., Santos, P., Doherty, P. & Sjanic, Z. (2023). Collaborative Qualitative Environment Mapping. In: AI 2023: Advances in Artificial Intelligence: . Paper presented at 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023 (pp. 3-15). Springer, 14472
Open this publication in new window or tab >>Collaborative Qualitative Environment Mapping
2023 (English)In: AI 2023: Advances in Artificial Intelligence, Springer, 2023, Vol. 14472, p. 3-15Conference paper, Published paper (Refereed)
Abstract [en]

This paper explores the use of LH Interval Calculus, a novel qualitative spatial reasoning formalism, to create a human-readable representation of environments observed by UAVs. The system simplifies data from multiple UAVs collaborating on environment mapping. Real UAV-captured data was used for evaluation. In tests involving two UAVs mapping an outdoor area, LH Calculus proved effective in generating a cohesive high-level description of the environment, contingent on consistent input data.

Place, publisher, year, edition, pages
Springer, 2023
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Keywords
Knowledge representation; multi-robot systems; mapping
National Category
Robotics
Identifiers
urn:nbn:se:liu:diva-201073 (URN)10.1007/978-981-99-8391-9_1 (DOI)001148049100001 ()2-s2.0-85178603926 (Scopus ID)9789819983902 (ISBN)9789819983919 (ISBN)
Conference
36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023
Note

Funding Agencies|CISB, Swedish-Brazilian Research and Innovation Center; Saab AB; Coordenacao de Aperfeicoamento de Pessoal em Nivel Superior - Brasil (CAPES) [001]

Available from: 2024-02-19 Created: 2024-02-19 Last updated: 2024-02-28Bibliographically approved
Doherty, P., Berger, C., Rudol, P. & Wzorek, M. (2021). Hastily formed knowledge networks and distributed situation awareness for collaborative robotics. Autonomous Intelligent Systems, 1(1), Article ID 16.
Open this publication in new window or tab >>Hastily formed knowledge networks and distributed situation awareness for collaborative robotics
2021 (English)In: Autonomous Intelligent Systems, E-ISSN 2730-616X, Vol. 1, no 1, article id 16Article in journal (Refereed) Published
Abstract [en]

In the context of collaborative robotics, distributed situation awareness is essential for  supporting collective intelligence in teams of robots and human agents where it can be used for both individual and collective decision support. This is particularly important in applications pertaining to emergency rescue and crisis management. During operational missions, data and knowledge are gathered incrementally and in different ways by heterogeneous robots and humans. We describe this as the creation of Hastily Formed Knowledge Networks (HFKNs). The focus of this paper is the specification and prototyping of a general distributed system architecture that supports the creation of HFKNs by teams of robots and humans. The information collected ranges from low-level sensor data to high-level semantic knowledge, the latter represented in part as RDF Graphs. The framework includes a synchronization protocol and associated algorithms that allow for the automatic distribution and sharing of data and knowledge between agents. This is done through the distributed synchronization of RDF Graphs shared between agents. High-level semantic queries specified in SPARQL can be used by robots and humans alike to acquire both knowledge and data content from team members. The system is empirically validated and complexity results of the proposed algorithms are provided. Additionally, a field robotics case study is described, where a 3D mapping mission has been executed using several UAVs in a collaborative emergency rescue scenario while using the full HFKN Framework.

Place, publisher, year, edition, pages
Springer, 2021
Keywords
Multi-robot collaboration; Unmanned aerial vehicles; Distributed knowledge representation; Distributed situation awareness; Semantic web technology; Knowledge synchronization; Multi-agent human/robot interaction
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-199031 (URN)10.1007/s43684-021-00016-w (DOI)2-s2.0-85143187288 (Scopus ID)
Note

Funding Agencies|ELLIIT Network Organization for Information and Communication Technology, Sweden (Project B09) and the Swedish Foundation for Strategic Research SSF (Smart Systems Project RIT15-0097). The first author is also supported by an RExperts Program Grant 2020A1313030098 from the Guangdong Department of Science and Technology, China in addition to a Sichuan Province International Science and Technology Innovation Cooperation Project Grant 2020YFH0160.

Available from: 2023-11-07 Created: 2023-11-07 Last updated: 2023-11-16Bibliographically approved
Doherty, P. & Szalas, A. (2021). Rough set reasoning using answer set programs. International Journal of Approximate Reasoning, 130(March), 126-149
Open this publication in new window or tab >>Rough set reasoning using answer set programs
2021 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613X, E-ISSN 1873-4731, Vol. 130, no March, p. 126-149Article in journal (Refereed) Published
Abstract [en]

Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. Formal representations of uncertainty are numerous and highly varied due to different types of uncertainty intended to be modeled such as vagueness, imprecision and incompleteness. There is a rich body of theoretical results that has been generated for many of these approaches. It is often the case though, that pragmatic tools for reasoning with uncertainty lag behind this rich body of theoretical results. Rough set theory is one such approach for modeling incompleteness and imprecision based on indiscernibility and its generalizations. In this paper, we provide a pragmatic tool for constructively reasoning with generalized rough set approximations that is based on the use of Answer Set Programming (Asp). We provide an interpretation of answer sets as (generalized) approximations of crisp sets (when possible) and show how to use Asp solvers as a tool for reasoning about (generalized) rough set approximations situated in realistic knowledge bases. The paper includes generic Asp templates for doing this and also provides a case study showing how these techniques can be used to generate reducts for incomplete information systems. Complete, ready to run clingo Asp code is provided in the Appendix, for all programs considered. These can be executed for validation purposes in the clingo Asp solver.

Place, publisher, year, edition, pages
Elsevier, 2021
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-172791 (URN)10.1016/j.ijar.2020.12.010 (DOI)000632656800005 ()
Projects
ELLIITSmart Systems Project RIT15-0097
Note

Funding: ELLIIT Network Organization for Information and Communication Technology, Sweden; Swedish Foundation for Strategic Research SSF(Smart Systems Project) [RIT15-0097]; Jinan University (Zhuhai Campus); National Science Centre PolandNational Science Centre, Poland [2017/27/B/ST6/02018]

Available from: 2021-01-24 Created: 2021-01-24 Last updated: 2021-04-21Bibliographically approved
Andersson, O., Doherty, P., Lager, M., Lindh, J.-O., Persson, L., Topp, E. A., . . . Wahlberg, B. (2021). WARA-PS: a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation. Autonomous Intelligent Systems, 1(1), Article ID 9.
Open this publication in new window or tab >>WARA-PS: a research arena for public safety demonstrations and autonomous collaborative rescue robotics experimentation
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2021 (English)In: Autonomous Intelligent Systems, E-ISSN 2730-616X, Vol. 1, no 1, article id 9Article in journal (Refereed) Published
Abstract [en]

A research arena (WARA-PS) for sensing, data fusion, user interaction, planning and control of collaborative autonomous aerial and surface vehicles in public safety applications is presented. The objective is to demonstrate scientific discoveries and to generate new directions for future research on autonomous systems for societal challenges. The enabler is a computational infrastructure with a core system architecture for industrial and academic collaboration. This includes a control and command system together with a framework for planning and executing tasks for unmanned surface vehicles and aerial vehicles. The motivating application for the demonstration is marine search and rescue operations. A state-of-art delegation framework for the mission planning together with three specific applications is also presented. The first one concerns model predictive control for cooperative rendezvous of autonomous unmanned aerial and surface vehicles. The second project is about learning to make safe real-time decisions under uncertainty for autonomous vehicles, and the third one is on robust terrain-aided navigation through sensor fusion and virtual reality tele-operation to support a GPS-free positioning system in marine environments. The research results have been experimentally evaluated and demonstrated to industry and public sector audiences at a marine test facility. It would be most difficult to do experiments on this large scale without the WARA-PS research arena. Furthermore, these demonstrator activities have resulted in effective research dissemination with high public visibility, business impact and new research collaborations between academia and industry.

Place, publisher, year, edition, pages
Springer Singapore, 2021
Keywords
Autonomous Systems, Artificial Intelligence, Robotics
National Category
Robotics Computer Sciences Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-189021 (URN)10.1007/s43684-021-00009-9 (DOI)
Funder
Knut and Alice Wallenberg Foundation, WASPSwedish Research CouncilSwedish Foundation for Strategic Research, RIT15-0097ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2022-10-07 Created: 2022-10-07 Last updated: 2023-04-05
Andersson, O., Sidén, P., Dahlin, J., Doherty, P. & Villani, M. (2020). Real-Time Robotic Search using Structural Spatial Point Processes. In: 35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019): . Paper presented at Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, July 22-25, 2019 (pp. 995-1005). Association For Uncertainty in Artificial Intelligence (AUAI), 115
Open this publication in new window or tab >>Real-Time Robotic Search using Structural Spatial Point Processes
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2020 (English)In: 35TH UNCERTAINTY IN ARTIFICIAL INTELLIGENCE CONFERENCE (UAI 2019), Association For Uncertainty in Artificial Intelligence (AUAI) , 2020, Vol. 115, p. 995-1005Conference paper, Published paper (Refereed)
Abstract [en]

Aerial robots hold great potential for aiding Search and Rescue (SAR) efforts over large areas, such as during natural disasters. Traditional approaches typically search an area exhaustively, thereby ignoring that the density of victims varies based on predictable factors, such as the terrain, population density and the type of disaster. We present a probabilistic model to automate SAR planning, with explicit minimization of the expected time to discovery. The proposed model is a spatial point process with three interacting spatial fields for i) the point patterns of persons in the area, ii) the probability of detecting persons and iii) the probability of injury. This structure allows inclusion of informative priors from e.g. geographic or cell phone traffic data, while falling back to latent Gaussian processes when priors are missing or inaccurate. To solve this problem in real-time, we propose a combination of fast approximate inference using Integrated Nested Laplace Approximation (INLA), and a novel Monte Carlo tree search tailored to the problem. Experiments using data simulated from real world Geographic Information System (GIS) maps show that the framework outperforms competing approaches, finding many more injured in the crucial first hours.

Place, publisher, year, edition, pages
Association For Uncertainty in Artificial Intelligence (AUAI), 2020
Series
Proceedings of Machine Learning Research (PMLR), E-ISSN 2640-3498 ; 115
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-159698 (URN)000722423500092 ()2-s2.0-85084016675 (Scopus ID)
Conference
Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, July 22-25, 2019
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

Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2023-04-05Bibliographically approved
Andersson, O. & Doherty, P. (2019). Deep RL for Autonomous Robots: Limitations and Safety Challenges. In: : . Paper presented at European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 489-495). ESANN
Open this publication in new window or tab >>Deep RL for Autonomous Robots: Limitations and Safety Challenges
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

With the rise of deep reinforcement learning, there has also been a string of successes on continuous control problems using physics simulators. This has lead to some optimism regarding use in autonomous robots and vehicles. However, to successful apply such techniques to the real world requires a firm grasp of their limitations. As recent work has raised questions of how diverse these simulation benchmarks really are, we here instead analyze a popular deep RL approach on toy examples from robot obstacle avoidance. We find that these converge very slowly, if at all, to safe policies. We identify convergence issues on stochastic environments and local minima as problems that warrant more attention for safety-critical control applications.

Place, publisher, year, edition, pages
ESANN, 2019
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-164581 (URN)2-s2.0-85071326616 (Scopus ID)9782875870650 (ISBN)
Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Swedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2020-03-26 Created: 2020-03-26 Last updated: 2023-04-05
Nilsson, M., Kvarnström, J. & Doherty, P. (2018). Planning with Temporal Uncertainty, Resources and Non-Linear Control Parameters. In: Mathijs de Weerdt, Sven Koenig, Gabriele Röger, Matthijs Spaan (Ed.), Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS): . Paper presented at The Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS), Delft, The Netherlands, June 24-29, 2018 (pp. 180-189). Palo Alto, California USA: AAAI Press
Open this publication in new window or tab >>Planning with Temporal Uncertainty, Resources and Non-Linear Control Parameters
2018 (English)In: Proceedings of the Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS) / [ed] Mathijs de Weerdt, Sven Koenig, Gabriele Röger, Matthijs Spaan, Palo Alto, California USA: AAAI Press, 2018, p. 180-189Conference paper, Published paper (Refereed)
Abstract [en]

We consider a general and industrially motivated class of planning problems involving a combination of requirements that can be essential to autonomous robotic systems planning to act in the real world: Support for temporal uncertainty where nature determines the eventual duration of an action, resource consumption with a non-linear relationship to durations, and the need to select appropriate values for control parameters that affect time requirements and resource usage. To this end, an existing planner is extended with support for Simple Temporal Networks with Uncertainty, Timed Initial Literals, and temporal coverage goals. Control parameters are lifted from the main combinatorial planning problem into a constraint satisfaction problem that connects them to resource usage. Constraint processing is then integrated and interleaved with verification of temporal feasibility, using projections for partial temporal awareness in the constraint solver.

Place, publisher, year, edition, pages
Palo Alto, California USA: AAAI Press, 2018
Series
International Conference on Automated Planning and Scheduling, E-ISSN 2334-0835
Keywords
automated planning, temporal networks, constraint satisfaction problem, temporal uncertainty, STNU, Simple Temporal Networks, Resources, Control Parameters
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-151663 (URN)000492986200022 ()978-1-57735-797-1 (ISBN)
Conference
The Twenty-Eighth International Conference on Automated Planning and Scheduling (ICAPS), Delft, The Netherlands, June 24-29, 2018
Projects
NFFP6CADICSELLIITSSF Smart Systems
Funder
Vinnova, NFFP6Swedish Research Council, CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Foundation for Strategic Research , RIT 15-0097
Available from: 2018-09-28 Created: 2018-09-28 Last updated: 2019-11-11
Doherty, P. & Szalas, A. (2018). Signed Dual Tableaux for Kleene Answer Set Programs. In: Golińska-Pilarek J., Zawidzki M. (Ed.), Ewa Orłowska on Relational Methods in Logic and Computer Science: (pp. 233-252). Cham: Springer
Open this publication in new window or tab >>Signed Dual Tableaux for Kleene Answer Set Programs
2018 (English)In: Ewa Orłowska on Relational Methods in Logic and Computer Science / [ed] Golińska-Pilarek J., Zawidzki M., Cham: Springer, 2018, p. 233-252Chapter in book (Refereed)
Abstract [en]

Dual tableaux were introduced by Rasiowa and Sikorski (1960) as a cut free deduction system for classical first-order logic. In the current paper, a sound and complete proof procedure based on dual tableaux is proposed for

R3

which is the standard Kleene logic augmented with a weak negation connective and an implication connective proposed, in another context, by Shepherdson (1989).

R3

is used as a basis for defining Kleene Answer Set Programs (

ASPK

programs). The semantics for

ASPK

programs is based on strongly supported models. Both entailment procedures and model generation procedures for normal and non-normal

ASPK

programs are proposed based on the use of dual tableaux and a model filtering technique. The dual tableau proof procedure extended with a model filtering technique is shown to be sound and complete for

ASPK

programs, both normal and non-normal. Since there is a direct relationship between answer sets for classical ASP programs and

R3

models for

ASPK

programs, it can be shown that the sound and complete dual tableaux proof procedure with filtering for ASPK" role="presentation">ASPKprograms is also sound and complete for classical normal ASP programs. For classical non-normal ASP programs, the proof procedure is only sound, since an alternative semantics for disjunction is used in

ASPK

Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Outstanding Contributions to Logic ; 17
Keywords
Signed tableaux, Signed dual tableaux, Answer set programming, Kleene three-valued logic, Strongly supported model
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-153431 (URN)10.1007/978-3-319-97879-6_9 (DOI)9783319978789 (ISBN)9783319978796 (ISBN)
Available from: 2018-12-14 Created: 2018-12-14 Last updated: 2019-12-19Bibliographically approved
Andersson, O., Wzorek, M. & Doherty, P. (2017). Deep Learning Quadcopter Control via Risk-Aware Active Learning. In: Satinder Singh and Shaul Markovitch (Ed.), Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI): . Paper presented at Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017, San Francisco, February 4–9. (pp. 3812-3818). AAAI Press, 5
Open this publication in new window or tab >>Deep Learning Quadcopter Control via Risk-Aware Active Learning
2017 (English)In: Proceedings of The Thirty-first AAAI Conference on Artificial Intelligence (AAAI) / [ed] Satinder Singh and Shaul Markovitch, AAAI Press, 2017, Vol. 5, p. 3812-3818Conference paper, Published paper (Refereed)
Abstract [en]

Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.

Place, publisher, year, edition, pages
AAAI Press, 2017
Series
Proceedings of the AAAI Conference on Artificial Intelligence, ISSN 2159-5399, E-ISSN 2374-3468 ; 5
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-132800 (URN)000485630703119 ()978-1-57735-784-1 (ISBN)
Conference
Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017, San Francisco, February 4–9.
Projects
ELLIITCADICSNFFP6SYMBICLOUDCUGS
Funder
Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeCUGS (National Graduate School in Computer Science)Swedish Foundation for Strategic Research
Available from: 2016-11-25 Created: 2016-11-25 Last updated: 2023-04-05Bibliographically approved
Burdakov, O., Kvarnström, J. & Doherty, P. (2017). Optimal scheduling for replacing perimeter guarding unmanned aerial vehicles. Annals of Operations Research, 249(1), 163-174
Open this publication in new window or tab >>Optimal scheduling for replacing perimeter guarding unmanned aerial vehicles
2017 (English)In: Annals of Operations Research, ISSN 0254-5330, E-ISSN 1572-9338, Vol. 249, no 1, p. 163-174Article in journal (Refereed) Published
Abstract [en]

Guarding the perimeter of an area in order to detect potential intruders is an important task in a variety of security-related applications. This task can in many circumstances be performed by a set of camera-equipped unmanned aerial vehicles (UAVs). Such UAVs will occasionally require refueling or recharging, in which case they must temporarily be replaced by other UAVs in order to maintain complete surveillance of the perimeter. In this paper we consider the problem of scheduling such replacements. We present optimal replacement strategies and justify their optimality.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Scheduling problem, Optimal replacement strategies, Perimeter guarding, Unmanned aerial vehicles
National Category
Computer Sciences Computational Mathematics Information Systems
Identifiers
urn:nbn:se:liu:diva-126459 (URN)10.1007/s10479-016-2169-5 (DOI)000394151400010 ()2-s2.0-84961644607 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 600958VINNOVA, 2013-01206Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2016-03-27 Created: 2016-03-27 Last updated: 2018-01-10Bibliographically approved
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