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Publications (10 of 18) Show all publications
Barbosa, F. S., Duberg, D., Jensfelt, P. & Tumova, J. (2019). Guiding Autonomous Exploration with Signal Temporal Logic. IEEE Robotics and Automation Letters, 4(4), 3332-3339
Open this publication in new window or tab >>Guiding Autonomous Exploration with Signal Temporal Logic
2019 (English)In: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 4, no 4, p. 3332-3339Article in journal (Refereed) Published
Abstract [en]

Algorithms for autonomous robotic exploration usually focus on optimizing time and coverage, often in a greedy fashion. However, obstacle inflation is conservative and might limit mapping capabilities and even prevent the robot from moving through narrow, important places. This letter proposes a method to influence the manner the robot moves in the environment by taking into consideration a user-defined spatial preference formulated in a fragment of signal temporal logic (STL). We propose to guide the motion planning toward minimizing the violation of such preference through a cost function that integrates the quantitative semantics, i.e., robustness of STL. To demonstrate the effectiveness of the proposed approach, we integrate it into the autonomous exploration planner (AEP). Results from simulations and real-world experiments are presented, highlighting the benefits of our approach.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2019
Keywords
Mapping, motion and path planning, formal methods in robotics and automation, search and rescue robots
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-255721 (URN)10.1109/LRA.2019.2926669 (DOI)000476791300029 ()2-s2.0-85069437912 (Scopus ID)
Note

QC 20190813

Available from: 2019-08-13 Created: 2019-08-13 Last updated: 2019-08-13Bibliographically approved
Karlsson, J. & Tumova, J. (2018). Decentralized Dynamic Multi-Vehicle Routing via Fast Marching Method. In: 2018 European Control Conference, ECC 2018: . Paper presented at 16th European Control Conference, ECC 2018, Limassol, Cyprus, 12 June 2018 through 15 June 2018 (pp. 739-745). Institute of Electrical and Electronics Engineers (IEEE), Article ID 8550222.
Open this publication in new window or tab >>Decentralized Dynamic Multi-Vehicle Routing via Fast Marching Method
2018 (English)In: 2018 European Control Conference, ECC 2018, Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 739-745, article id 8550222Conference paper, Published paper (Refereed)
Abstract [en]

While centralized approaches to multi-vehicle routing problems typically provide provably optimal solutions, they do not scale well. In this paper, an algorithm for decentralized multi-vehicle routing is introduced that is often associated with significantly lower computational demands, but does not sacrifice the optimality of the found solution. In particular, we consider a fleet of autonomous vehicles traversing a road network that need to service a potentially infinite set of gradually appearing travel requests specified by their pick-up and drop-off points. The proposed algorithm synthesizes optimal assignment of the travel requests to the vehicles as well as optimal routes by utilizing Fast Marching Method (FMM) that restricts the search for the optimal assignment to a local subnetwork as opposed to the global road network. Several illustrative case studies are presented to demonstrate the effectiveness and efficiency of the approach.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-241391 (URN)10.23919/ECC.2018.8550222 (DOI)2-s2.0-85059822667 (Scopus ID)9783952426982 (ISBN)
Conference
16th European Control Conference, ECC 2018, Limassol, Cyprus, 12 June 2018 through 15 June 2018
Note

QC 20190121

Available from: 2019-01-21 Created: 2019-01-21 Last updated: 2019-01-21Bibliographically approved
Guo, M., Boskos, D., Tumova, J. & Dimarogonas, D. V. (2018). Distributed hybrid control synthesis for multi-agent systems from high-level specifications (475ed.). In: Control Subject to Computational and Communication Constraints: (pp. 241-260). Springer Verlag
Open this publication in new window or tab >>Distributed hybrid control synthesis for multi-agent systems from high-level specifications
2018 (English)In: Control Subject to Computational and Communication Constraints, Springer Verlag , 2018, 475, p. 241-260Chapter in book (Refereed)
Abstract [en]

Current control applications necessitate in many cases the consideration of systems with multiple interconnected components. These components/agents may need to fulfill high-level tasks at a discrete planning layer and also coupled constraints at the continuous control layer. Toward this end, the need for combined decentralized control at the continuous layer and planning at the discrete layer becomes apparent. While there are approaches that handle the problem in a top-down centralized manner, decentralized bottom-up approaches have not been pursued to the same extent. We present here some of our results for the problem of combined, hybrid control and task planning from high-level specifications for multi-agent systems in a bottom-up manner. In the first part, we present some initial results on extending the necessary notion of abstractions to multi-agent systems in a distributed fashion. We then consider a setup where agents are assigned individual tasks in the form of linear temporal logic (LTL) formulas and derive local task planning strategies for each agent. In the last part, the problem of combined distributed task planning and control under coupled continuous constraints is further considered.

Place, publisher, year, edition, pages
Springer Verlag, 2018 Edition: 475
Series
Lecture Notes in Control and Information Sciences, ISSN 0170-8643 ; 475
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-238382 (URN)10.1007/978-3-319-78449-6_12 (DOI)2-s2.0-85048162969 (Scopus ID)978-3-319-78448-9 (ISBN)
Funder
Swedish Research CouncilEU, Horizon 2020Knut and Alice Wallenberg Foundation
Note

QC 20181119

Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2018-11-19Bibliographically approved
Menghi, C., Garcia, S., Pelliccione, P. & Tumova, J. (2018). Multi-robot LTL planning under uncertainty. In: 22nd International Symposium on Formal Methods, FM 2018 Held as Part of the Federated Logic Conference, FloC 2018: . Paper presented at 22nd International Symposium on Formal Methods, FM 2018 Held as Part of the Federated Logic Conference, FloC 2018, Oxford, United Kingdom, 15 July 2018 through 17 July 2018 (pp. 399-417). Springer, 10951
Open this publication in new window or tab >>Multi-robot LTL planning under uncertainty
2018 (English)In: 22nd International Symposium on Formal Methods, FM 2018 Held as Part of the Federated Logic Conference, FloC 2018, Springer, 2018, Vol. 10951, p. 399-417Conference paper, Published paper (Refereed)
Abstract [en]

Robot applications are increasingly based on teams of robots that collaborate to perform a desired mission. Such applications ask for decentralized techniques that allow for tractable automated planning. Another aspect that current robot applications must consider is partial knowledge about the environment in which the robots are operating and the uncertainty associated with the outcome of the robots’ actions. Current planning techniques used for teams of robots that perform complex missions do not systematically address these challenges: (1) they are either based on centralized solutions and hence not scalable, (2) they consider rather simple missions, such as A-to-B travel, (3) they do not work in partially known environments. We present a planning solution that decomposes the team of robots into subclasses, considers missions given in temporal logic, and at the same time works when only partial knowledge of the environment is available. We prove the correctness of the solution and evaluate its effectiveness on a set of realistic examples.

Place, publisher, year, edition, pages
Springer, 2018
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743 ; 10951
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:kth:diva-233741 (URN)10.1007/978-3-319-95582-7_24 (DOI)2-s2.0-85050342512 (Scopus ID)9783319955810 (ISBN)
Conference
22nd International Symposium on Formal Methods, FM 2018 Held as Part of the Federated Logic Conference, FloC 2018, Oxford, United Kingdom, 15 July 2018 through 17 July 2018
Note

QC 20180829

Available from: 2018-08-29 Created: 2018-08-29 Last updated: 2018-08-29Bibliographically approved
Karlsson, J., Vasile, C.-I., Tumova, J., Karaman, S. & Rus, D. (2018). Multi-vehicle motion planning for social optimal mobility-on-demand. In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA): . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA (pp. 7298-7305). IEEE COMPUTER SOC
Open this publication in new window or tab >>Multi-vehicle motion planning for social optimal mobility-on-demand
Show others...
2018 (English)In: 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), IEEE COMPUTER SOC , 2018, p. 7298-7305Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we consider a fleet of self-driving cars operating in a road network governed by rules of the road, such as the Vienna Convention on Road Traffic, providing rides to customers to serve their demands with desired deadlines. We focus on the associated motion planning problem that tradesoff the demands' delays and level of violation of the rules of the road to achieve social optimum among the vehicles. Due to operating in the same environment, the interaction between the cars must be taken into account, and can induce further delays. We propose an integrated route and motion planning approach that achieves scalability with respect to the number of cars by resolving potential collision situations locally within so-called bubble spaces enclosing the conflict. The algorithms leverage the road geometries, and perform joint planning only for lead vehicles in the conflict and use queue scheduling for the remaining cars. Furthermore, a framework for storing previously resolved conflict situations is proposed, which can be use for quick querying of joint motion plans. We show the mobility-on-demand setup and effectiveness of the proposed approach in simulated case studies involving up to 10 selfdriving vehicles.

Place, publisher, year, edition, pages
IEEE COMPUTER SOC, 2018
Series
IEEE International Conference on Robotics and Automation ICRA, ISSN 1050-4729
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-237168 (URN)000446394505081 ()2-s2.0-85063127749 (Scopus ID)978-1-5386-3081-5 (ISBN)
Conference
IEEE International Conference on Robotics and Automation (ICRA), MAY 21-25, 2018, Brisbane, AUSTRALIA
Funder
Swedish Research Council
Note

QC 20181024

Available from: 2018-10-24 Created: 2018-10-24 Last updated: 2019-08-20Bibliographically approved
Nikou, A., Boskos, D., Tumova, J. & Dimarogonas, D. V. (2018). On the timed temporal logic planning of coupled multi-agent systems. Automatica, 97, 339-345
Open this publication in new window or tab >>On the timed temporal logic planning of coupled multi-agent systems
2018 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 97, p. 339-345Article in journal (Refereed) Published
Abstract [en]

This paper presents a fully automated procedure for controller synthesis for multi-agent systems under coupling constraints. Each agent is modeled with dynamics consisting of two terms: the first one models the coupling constraints and the other one is an additional bounded control input. We aim to design these inputs so that each agent meets an individual high-level specification given as a Metric Interval Temporal Logic (MITL). First, a decentralized abstraction that provides a space and time discretization of the multi agent system is designed. Second, by utilizing this abstraction and techniques from formal verification, we propose an algorithm that computes the individual runs which provably satisfy the high-level tasks. The overall approach is demonstrated in a simulation example conducted in MATLAB environment.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2018
Keywords
Multi-agent systems, Cooperative control, Hybrid systems, Formal verification, Timed logics, Abstractions, Discrete event systems
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-238112 (URN)10.1016/j.automatica.2018.08.023 (DOI)000447568400039 ()2-s2.0-85052902827 (Scopus ID)
Note

QC 20190110

Available from: 2019-01-10 Created: 2019-01-10 Last updated: 2019-01-10Bibliographically approved
Menghi, C., García, S., Pelliccione, P. & Tumova, J. (2018). Poster: Towards multi-robot applications planning under uncertainty. In: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings: . Paper presented at 40th ACM/IEEE International Conference on Software Engineering, ICSE 2018, Gothenburg, Sweden, 27 May 2018 through 3 June 2018 (pp. 438-439). IEEE Computer Society
Open this publication in new window or tab >>Poster: Towards multi-robot applications planning under uncertainty
2018 (English)In: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings, IEEE Computer Society, 2018, p. 438-439Conference paper, Published paper (Refereed)
Abstract [en]

Novel robotic applications are no longer based on single robots. They rather require teams of robots that collaborate and interact to perform a desired mission. They must also be used in contexts in which only partial knowledge about the robots and their environment is present. To ensure mission achievement, robotic applications require the usage of planners that compute the set of actions the robots must perform. Current planning techniques are often based on centralized solutions and hence they do not scale when teams of robots are considered, they consider rather simple missions, and they do not work in partially known environments. To address these challenges, we present a planning solution that decomposes the team of robots into subclasses, considers complex high-level missions given in temporal logic, and at the same time works when only partial knowledge of the environment is available.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
Proceedings - International Conference on Software Engineering, ISSN 0270-5257
National Category
Robotics
Identifiers
urn:nbn:se:kth:diva-238224 (URN)10.1145/3183440.3195046 (DOI)000450109000186 ()2-s2.0-85049670520 (Scopus ID)9781450356633 (ISBN)
Conference
40th ACM/IEEE International Conference on Software Engineering, ICSE 2018, Gothenburg, Sweden, 27 May 2018 through 3 June 2018
Note

QC 20181121

Available from: 2018-11-21 Created: 2018-11-21 Last updated: 2019-05-20Bibliographically approved
Nikou, A., Tumova, J. & Dimarogonas, D. V. (2017). Probabilistic Plan Synthesis for Coupled Multi-Agent Systems. In: : . Paper presented at 20th World Congress of the International Federation of Automatic Control (IFAC WC), Toulouse, France, July 2017.
Open this publication in new window or tab >>Probabilistic Plan Synthesis for Coupled Multi-Agent Systems
2017 (English)Conference paper, Published paper (Refereed)
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-216959 (URN)
Conference
20th World Congress of the International Federation of Automatic Control (IFAC WC), Toulouse, France, July 2017
Note

QC 20171213

Available from: 2017-10-25 Created: 2017-10-25 Last updated: 2017-12-13Bibliographically approved
Nikou, A., Tumova, J. & Dimarogonas, D. V. (2017). Probabilistic Plan Synthesis for Coupled Multi-Agent Systems. IFAC-PapersOnLine, 50(1), 10766-10771
Open this publication in new window or tab >>Probabilistic Plan Synthesis for Coupled Multi-Agent Systems
2017 (English)In: IFAC-PapersOnLine, ISSN 2405-8963, Vol. 50, no 1, p. 10766-10771Article in journal (Refereed) Published
Abstract [en]

This paper presents a fully automated procedure for controller synthesis for multi-agent systems under the presence of uncertainties. We model the motion of each of the N agents in the environment as a Markov Decision Process (MDP) and we assign to each agent one individual high-level formula given in Probabilistic Computational Tree Logic (PCTL). Each agent may need to collaborate with other agents in order to achieve a task. The collaboration is imposed by sharing actions between the agents. We aim to design local control policies such that each agent satisfies its individual PCTL formula. The proposed algorithm builds on clustering the agents, MDP products construction and controller policies design. We show that our approach has better computational complexity than the centralized case, which traditionally suffers from very high computational demands.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-223073 (URN)10.1016/j.ifacol.2017.08.2280 (DOI)000423965100285 ()2-s2.0-85031771017 (Scopus ID)
Note

QC 20180215

Available from: 2018-02-15 Created: 2018-02-15 Last updated: 2018-03-05Bibliographically approved
Guo, M., Tumova, J. & Dimarogonas, D. V. (2016). Communication-Free Multi-Agent Control Under Local Temporal Tasks and Relative-Distance Constraints. IEEE Transactions on Automatic Control, 61(12), 3948-3962
Open this publication in new window or tab >>Communication-Free Multi-Agent Control Under Local Temporal Tasks and Relative-Distance Constraints
2016 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 61, no 12, p. 3948-3962Article in journal (Refereed) Published
Abstract [en]

We propose a distributed control and coordination strategy for multi-agent systems where each agent has a local task specified as a Linear Temporal Logic (LTL) formula and at the same time is subject to relative-distance constraints with its neighboring agents. The local tasks capture the temporal requirements on individual agents' behaviors, while the relative-distance constraints impose requirements on the collective motion of the whole team. The proposed solution relies only on relative-state measurements among the neighboring agents without the need for explicit information exchange. It is guaranteed that the local tasks given as syntactically co-safe or general LTL formulas are fulfilled and the relative-distance constraints are satisfied at all time. The approach is demonstrated with computer simulations.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
Agents and autonomous systems, cooperative control, hybrid systems, switched systems
National Category
Control Engineering Robotics
Identifiers
urn:nbn:se:kth:diva-199499 (URN)10.1109/TAC.2016.2527731 (DOI)000389891100018 ()2-s2.0-85004115747 (Scopus ID)
Note

QC 20170118

Available from: 2017-01-18 Created: 2017-01-09 Last updated: 2017-11-29Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4173-2593

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