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Doherty, Patrick
Publications (10 of 207) Show all publications
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)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: 2018-01-13Bibliographically 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
Doherty, P., Kvarnström, J., Rudol, P., Wzorek, M., Conte, G., Berger, C., . . . Stastny, T. (2016). A Collaborative Framework for 3D Mapping using Unmanned Aerial Vehicles. In: Baldoni, M., Chopra, A.K., Son, T.C., Hirayama, K., Torroni, P. (Ed.), PRIMA 2016: Principles and Practice of Multi-Agent Systems: . Paper presented at PRIMA 2016: Principles and Practice of Multi-Agent Systems (pp. 110-130). Springer Publishing Company
Open this publication in new window or tab >>A Collaborative Framework for 3D Mapping using Unmanned Aerial Vehicles
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2016 (English)In: PRIMA 2016: Principles and Practice of Multi-Agent Systems / [ed] Baldoni, M., Chopra, A.K., Son, T.C., Hirayama, K., Torroni, P., Springer Publishing Company, 2016, p. 110-130Conference paper, Published paper (Refereed)
Abstract [en]

This paper describes an overview of a generic framework for collaboration among humans and multiple heterogeneous robotic systems based on the use of a formal characterization of delegation as a speech act. The system used contains a complex set of integrated software modules that include delegation managers for each platform, a task specification language for characterizing distributed tasks, a task planner, a multi-agent scan trajectory generation and region partitioning module, and a system infrastructure used to distributively instantiate any number of robotic systems and user interfaces in a collaborative team. The application focusses on 3D reconstruction in alpine environments intended to be used by alpine rescue teams. Two complex UAV systems used in the experiments are described. A fully autonomous collaborative mission executed in the Italian Alps using the framework is also described.

Place, publisher, year, edition, pages
Springer Publishing Company, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 9862
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-130558 (URN)10.1007/978-3-319-44832-9_7 (DOI)000388796200007 ()978-3-319-44831-2 (ISBN)
Conference
PRIMA 2016: Principles and Practice of Multi-Agent Systems
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeVINNOVASwedish Research Council
Note

Accepted for publication.

Available from: 2016-08-16 Created: 2016-08-16 Last updated: 2018-02-20
Warnquist, H., Kvarnström, J. & Doherty, P. (2016). A Modeling Framework for Troubleshooting Automotive Systems. Applied Artificial Intelligence, 30(3), 257-296
Open this publication in new window or tab >>A Modeling Framework for Troubleshooting Automotive Systems
2016 (English)In: Applied Artificial Intelligence, ISSN 0883-9514, E-ISSN 1087-6545, Vol. 30, no 3, p. 257-296Article in journal (Refereed) Published
Abstract [en]

This article presents a novel framework for modeling the troubleshooting process for automotive systems such as trucks and buses. We describe how a diagnostic model of the troubleshooting process can be created using event-driven, nonstationary, dynamic Bayesian networks. Exact inference in such a model is in general not practically possible. Therefore, we evaluate different approximate methods for inference based on the Boyen–Koller algorithm. We identify relevant model classes that have particular structure such that inference can be made with linear time complexity. We also show how models created using expert knowledge can be tuned using statistical data. The proposed learning mechanism can use data that is collected from a heterogeneous fleet of modular vehicles that can consist of different components. The proposed framework is evaluated both theoretically and experimentally on an application example of a fuel injection system.

Place, publisher, year, edition, pages
Taylor & Francis, 2016
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-121499 (URN)10.1080/08839514.2016.1156955 (DOI)000374866700005 ()
Projects
ELLIITCADICS
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Note

The published article is a shorter version than the version in manuscript form. The status of this article was earlier Manuscript.

Funding agencies: Scania CV AB; FFI - Strategic Vehicle Research and Innovation; Excellence Center at Linkoping and Lund in Information Technology (ELLIIT); Research Council (VR) Linnaeus Center CADICS

Available from: 2015-09-22 Created: 2015-09-22 Last updated: 2018-01-11Bibliographically approved
Doherty, P. & Szalas, A. (2016). An Entailment Procedure for Kleene Answer Set Programs. In: Sombattheera C., Stolzenburg F., Lin F., Nayak A. (Ed.), Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016.: . Paper presented at Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016. (pp. 24-37). Springer, 10053
Open this publication in new window or tab >>An Entailment Procedure for Kleene Answer Set Programs
2016 (English)In: Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016. / [ed] Sombattheera C., Stolzenburg F., Lin F., Nayak A., Springer, 2016, Vol. 10053, p. 24-37Conference paper, Published paper (Refereed)
Abstract [en]

Classical Answer Set Programming is a widely known knowledge representation framework based on the logic programming paradigm that has been extensively studied in the past decades. Semantic theories for classical answer sets are implicitly three-valued in nature, yet with few exceptions, computing classical answer sets is based on translations into classical logic and the use of SAT solving techniques. In this paper, we introduce a variation of Kleene three-valued logic with strong connectives, R3" role="presentation">R3, and then provide a sound and complete proof procedure for R3" role="presentation">R3 based on the use of signed tableaux. We then define a restriction on the syntax of R3" role="presentation">R3 to characterize Kleene ASPs. Strongly-supported models, which are a subset of R3" role="presentation">R3 models are then defined to characterize the semantics of Kleene ASPs. A filtering technique on tableaux for R3" role="presentation">R3 is then introduced which provides a sound and complete tableau-based proof technique for Kleene ASPs. We then show a translation and semantic correspondence between Classical ASPs and Kleene ASPs, where answer sets for normal classical ASPs are equivalent to strongly-supported models. This implies that the proof technique introduced can be used for classical normal ASPs as well as Kleene ASPs. The relation between non-normal classical and Kleene ASPs is also considered.

Place, publisher, year, edition, pages
Springer, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10053
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-134767 (URN)10.1007/978-3-319-49397-8_3 (DOI)000389332100003 ()978-3-319-49396-1 (ISBN)978-3-319-49397-8 (ISBN)
Conference
Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2016.
Projects
CADICSELLIITCUASSymbiCloudNFFP6
Available from: 2017-02-24 Created: 2017-02-24 Last updated: 2018-01-13
Häger, G., Bhat, G., Danelljan, M., Khan, F. S., Felsberg, M., Rudol, P. & Doherty, P. (2016). Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV. In: Proceedings of the 12th International Symposium on Advances in Visual Computing: . Paper presented at International Symposium on Advances in Visual Computing.
Open this publication in new window or tab >>Combining Visual Tracking and Person Detection for Long Term Tracking on a UAV
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2016 (English)In: Proceedings of the 12th International Symposium on Advances in Visual Computing, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Visual object tracking performance has improved significantly in recent years. Most trackers are based on either of two paradigms: online learning of an appearance model or the use of a pre-trained object detector. Methods based on online learning provide high accuracy, but are prone to model drift. The model drift occurs when the tracker fails to correctly estimate the tracked object’s position. Methods based on a detector on the other hand typically have good long-term robustness, but reduced accuracy compared to online methods.

Despite the complementarity of the aforementioned approaches, the problem of fusing them into a single framework is largely unexplored. In this paper, we propose a novel fusion between an online tracker and a pre-trained detector for tracking humans from a UAV. The system operates at real-time on a UAV platform. In addition we present a novel dataset for long-term tracking in a UAV setting, that includes scenarios that are typically not well represented in standard visual tracking datasets.

National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:liu:diva-137897 (URN)10.1007/978-3-319-50835-1_50 (DOI)2-s2.0-85007039301 (Scopus ID)978-3-319-50834-4 (ISBN)978-3-319-50835-1 (ISBN)
Conference
International Symposium on Advances in Visual Computing
Available from: 2017-05-31 Created: 2017-05-31 Last updated: 2018-01-13Bibliographically approved
Nilsson, M., Kvarnström, J. & Doherty, P. (2016). Efficient Processing of Simple Temporal Networks with Uncertainty: Algorithms for Dynamic Controllability Verification. Acta Informatica, 53(6-8), 723-752
Open this publication in new window or tab >>Efficient Processing of Simple Temporal Networks with Uncertainty: Algorithms for Dynamic Controllability Verification
2016 (English)In: Acta Informatica, ISSN 0001-5903, E-ISSN 1432-0525, Vol. 53, no 6-8, p. 723-752Article in journal (Refereed) Published
Abstract [en]

Temporal formalisms are essential for reasoning about actions that are carried out over time. The exact durations of such actions are generally hard to predict. In temporal planning, the resulting uncertainty is often worked around by only considering upper bounds on durations, with the assumption that when an action happens to be executed more quickly, the plan will still succeed. However, this  assumption is often false: If we finish cooking too early, the dinner will be cold before everyone is ready to eat. 

Using Simple Temporal Networks with Uncertainty (STNU), a planner can correctly take both lower and upper duration bounds into  account. It must then verify that the plans it generates are executable regardless of the actual outcomes of the uncertain durations. This is captured by the property of dynamic controllability (DC), which should be verified incrementally during plan generation. 

Recently a new incremental algorithm for verifying dynamic controllability was proposed: EfficiantIDC, which can verify if an STNU that is DC remains DC after the addition or tightening of a constraint (corresponding to a new action being added to a plan). The algorithm was shown to have a worst case complexity of O(n4) for each addition or tightening. This can be amortized over the construction of a whole STNU for an amortized complexity in O(n3). In this paper we improve the EfficientIDC algorithm in a way that prevents it from having to reprocess nodes. This improvement leads to a lower worst case complexity in O(n3).

Place, publisher, year, edition, pages
Springer Publishing Company, 2016
Keywords
Temporal Networks, Simple Temporal Networks with Uncertainty, Dynamic Controllability
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-121949 (URN)10.1007/s00236-015-0248-8 (DOI)000383702800007 ()
Funder
Swedish Research Council, CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Foundation for Strategic Research , CUASEU, FP7, Seventh Framework Programme, SHERPAVINNOVA, NFFP6 2013-01206
Available from: 2015-10-13 Created: 2015-10-13 Last updated: 2018-01-11
Doherty, P., Kvarnström, J. & Szalas, A. (2016). Iteratively-Supported Formulas and Strongly Supported Models for Kleene Answer Set Programs. In: Michael, Loizos; Kakas, Antonis (Ed.), Logics in Artificial Intelligence: 15th European Conference, JELIA 2016, Larnaca, Cyprus, November 9-11, 2016, Proceedings. Paper presented at 15th European Conference on Logics in Artificial Intelligence, JELIA 2016; Larnaca; Cyprus; 9 November 2016 through 11 November 2016 (pp. 536-542). Springer Publishing Company
Open this publication in new window or tab >>Iteratively-Supported Formulas and Strongly Supported Models for Kleene Answer Set Programs
2016 (English)In: Logics in Artificial Intelligence: 15th European Conference, JELIA 2016, Larnaca, Cyprus, November 9-11, 2016, Proceedings, Springer Publishing Company, 2016, p. 536-542Conference paper, Published paper (Refereed)
Abstract [en]

In this extended abstract, we discuss the use of iteratively-supported formulas (ISFs) as a basis for computing strongly-supported models for Kleene Answer Set Programs (ASPK). ASPK programs have a syntax identical to classical ASP programs. The semantics of ASPK programs is based on the use of Kleene three-valued logic and strongly-supported models. For normal ASPK programs, their strongly supported models are identical to classical answer sets using stable model semantics.  For disjunctive ASPK programs, the semantics weakens the minimality assumption resulting in a classical interpretation for disjunction. We use ISFs to characterize strongly-supported models and show that they are polynomially bounded.

Place, publisher, year, edition, pages
Springer Publishing Company, 2016
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10021
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-134419 (URN)10.1007/978-3-319-48758-8_36 (DOI)000389316500036 ()978-3-319-48757-1 (ISBN)978-3-319-48758-8 (ISBN)
Conference
15th European Conference on Logics in Artificial Intelligence, JELIA 2016; Larnaca; Cyprus; 9 November 2016 through 11 November 2016
Projects
CADICSELLIITCUASSymbiCloudSHERPANFFP6 KISA
Available from: 2017-02-10 Created: 2017-02-10 Last updated: 2018-01-13
Andersson, O., Wzorek, M., Rudol, P. & Doherty, P. (2016). Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization. In: IEEE International Conference on Robotics and Automation (ICRA), 2016: . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), 2016, Stockholm, May 16-21 (pp. 4597-4604). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization
2016 (English)In: IEEE International Conference on Robotics and Automation (ICRA), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 4597-4604Conference paper, Published paper (Refereed)
Abstract [en]

Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016
Series
Proceedings of IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Keywords
Robot Learning, Collision Avoidance, Robotics, Bayesian Optimization, Model Predictive Control
National Category
Robotics Computer Sciences
Identifiers
urn:nbn:se:liu:diva-126769 (URN)10.1109/ICRA.2016.7487661 (DOI)000389516203138 ()
Conference
IEEE International Conference on Robotics and Automation (ICRA), 2016, Stockholm, May 16-21
Projects
CADICSELLIITNFFP6CUASSHERPA
Funder
Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeSwedish Foundation for Strategic Research
Available from: 2016-04-04 Created: 2016-04-04 Last updated: 2018-01-10Bibliographically approved
Danelljan, M., Khan, F. S., Felsberg, M., Granström, K., Heintz, F., Rudol, P., . . . Doherty, P. (2015). A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems. In: Lourdes Agapito, Michael M. Bronstein and Carsten Rother (Ed.), Lourdes Agapito, Michael M. Bronstein and Carsten Rother (Ed.), COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I: . Paper presented at 13th European Conference on Computer Vision (ECCV) Switzerland, September 6-7 and 12 (pp. 223-237). Springer Publishing Company, 8925
Open this publication in new window or tab >>A Low-Level Active Vision Framework for Collaborative Unmanned Aircraft Systems
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2015 (English)In: COMPUTER VISION - ECCV 2014 WORKSHOPS, PT I / [ed] Lourdes Agapito, Michael M. Bronstein and Carsten Rother, Springer Publishing Company, 2015, Vol. 8925, p. 223-237Conference paper, Published paper (Refereed)
Abstract [en]

Micro unmanned aerial vehicles are becoming increasingly interesting for aiding and collaborating with human agents in myriads of applications, but in particular they are useful for monitoring inaccessible or dangerous areas. In order to interact with and monitor humans, these systems need robust and real-time computer vision subsystems that allow to detect and follow persons.

In this work, we propose a low-level active vision framework to accomplish these challenging tasks. Based on the LinkQuad platform, we present a system study that implements the detection and tracking of people under fully autonomous flight conditions, keeping the vehicle within a certain distance of a person. The framework integrates state-of-the-art methods from visual detection and tracking, Bayesian filtering, and AI-based control. The results from our experiments clearly suggest that the proposed framework performs real-time detection and tracking of persons in complex scenarios

Place, publisher, year, edition, pages
Springer Publishing Company, 2015
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 8925
Keywords
Visual tracking; Visual surveillance; Micro UAV; Active vision
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Identifiers
urn:nbn:se:liu:diva-115847 (URN)10.1007/978-3-319-16178-5_15 (DOI)000362493800015 ()978-3-319-16177-8 (ISBN)978-3-319-16178-5 (ISBN)
Conference
13th European Conference on Computer Vision (ECCV) Switzerland, September 6-7 and 12
Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2018-02-07Bibliographically approved
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