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Miloradović, B., Curuklu, B., Ekström, M. & Papadopoulos, A. (2019). Extended colored traveling salesperson for modeling multi-agent mission planning problems. In: ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems: . Paper presented at 8th International Conference on Operations Research and Enterprise Systems, ICORES 2019, 19 February 2019 through 21 February 2019 (pp. 237-244). SciTePress
Open this publication in new window or tab >>Extended colored traveling salesperson for modeling multi-agent mission planning problems
2019 (English)In: ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems, SciTePress , 2019, p. 237-244Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, multi-agent systems have been widely used in different missions, ranging from underwater to airborne. A mission typically involves a large number of agents and tasks, making it very hard for the human operator to create a good plan. A search for an optimal plan may take too long, and it is hard to make a time estimate of when the planner will finish. A Genetic algorithm based planner is proposed in order to overcome this issue. The contribution of this paper is threefold. First, an Integer Linear Programming (ILP) formulation of a novel Extensive Colored Traveling Salesperson Problem (ECTSP) is given. Second, a new objective function suitable for multi-agent mission planning problems is proposed. Finally, a reparation algorithm to allow usage of common variation operators for ECTSP has been developed. 

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Colored traveling salesperson (CTSP), Genetic algorithms, Multi-agent mission planning, Integer programming, Operations research, Software agents, Human operator, Integer Linear Programming, Mission planning, Mission planning problem, Objective functions, Traveling salesperson problem, Variation operator, Multi agent systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-43305 (URN)2-s2.0-85064712559 (Scopus ID)9789897583520 (ISBN)
Conference
8th International Conference on Operations Research and Enterprise Systems, ICORES 2019, 19 February 2019 through 21 February 2019
Available from: 2019-05-09 Created: 2019-05-09 Last updated: 2019-10-01Bibliographically approved
Trinh, L. A., Ekström, M. & Curuklu, B. (2019). Petri Net Based Navigation Planning with Dipole Field and Dynamic Window Approach for Collision Avoidance. In: International Conference on Control, Decision and Information Technologies CoDIT: . Paper presented at International Conference on Control, Decision and Information Technologies CoDIT, 23 Apr 2019, Paris, France (pp. 1013-1018). , Article ID 8820359.
Open this publication in new window or tab >>Petri Net Based Navigation Planning with Dipole Field and Dynamic Window Approach for Collision Avoidance
2019 (English)In: International Conference on Control, Decision and Information Technologies CoDIT, 2019, p. 1013-1018, article id 8820359Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel path planning system for multiple robots working in an uncontrolled environment in the presence of humans. The approach combines the use of Petri net to plan the movement of multiple robots to prevent the risk of congestion caused by routing several robots into a narrow region, together with a dipole field with dynamic window approach to avoid collisions of a robot with dynamic obstacles. By regarding the velocity and direction of both humans and robots as a source of magnetic dipole moment, the dipole-dipole interaction between the moving objects will generate repulsive forces to prevent collisions. The whole system is presented on robot operating system platform so that its implementation can be extendable into real robots. Experimental results with Gazebo simulator demonstrates the effectiveness of the proposed approach.

National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-43926 (URN)10.1109/CoDIT.2019.8820359 (DOI)2-s2.0-85072839041 (Scopus ID)9781728105215 (ISBN)
Conference
International Conference on Control, Decision and Information Technologies CoDIT, 23 Apr 2019, Paris, France
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2019-06-19 Created: 2019-06-19 Last updated: 2019-10-17Bibliographically approved
Castillejo, P., Curuklu, B., Fresco, R., Johansen, G., Bilbao-Arechabala, S., Martinez-Rodriguez, B., . . . Santic, M. (2019). The AFarCloud ECSEL Project. In: Proceedings - Euromicro Conference on Digital System Design, DSD 2019: . Paper presented at 22nd Euromicro Conference on Digital System Design, DSD 2019, 28 August 2019 through 30 August 2019 (pp. 414-419). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>The AFarCloud ECSEL Project
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2019 (English)In: Proceedings - Euromicro Conference on Digital System Design, DSD 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 414-419Conference paper, Published paper (Refereed)
Abstract [en]

Farming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate identification and proper quantification of pathogens affecting plant and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate FARming in the CLOUD) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labour costs. This platform will be integrated with farm management software and will support monitoring and decision-making solutions based on big data and real-time data mining techniques.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
autonomous and semi-autonomous vehicles, autonomy and cooperation, crop monitoring, Cyber-Physical Systems, farming robots, Livestock management, Smart & Precision Farming, Agriculture, Animals, Cost effectiveness, Cyber Physical System, Data mining, Embedded systems, Health risks, Productivity, Real time systems, Remotely operated vehicles, Systems analysis, Veterinary medicine, Wages, Distributed platforms, Domain specific, Economic challenges, Precision farming, Real-time data mining, Semi-autonomous vehicles, Decision making
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-46216 (URN)10.1109/DSD.2019.00066 (DOI)2-s2.0-85074928612 (Scopus ID)9781728128610 (ISBN)
Conference
22nd Euromicro Conference on Digital System Design, DSD 2019, 28 August 2019 through 30 August 2019
Available from: 2019-12-02 Created: 2019-12-02 Last updated: 2019-12-02Bibliographically approved
Frasheri, M., Curuklu, B., Ekström, M. & Papadopoulos, A. (2018). Adaptive Autonomy in a Search and Rescue Scenario. In: International Conference on Self-Adaptive and Self-Organizing Systems, SASO, Volume 2018-September, 15 January 2019: . Paper presented at 12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018; Trento; Italy; 3 September 2018 through 7 September 2018 (pp. 150-155).
Open this publication in new window or tab >>Adaptive Autonomy in a Search and Rescue Scenario
2018 (English)In: International Conference on Self-Adaptive and Self-Organizing Systems, SASO, Volume 2018-September, 15 January 2019, 2018, p. 150-155Conference paper, Published paper (Refereed)
Abstract [en]

Adaptive autonomy plays a major role in the design of multi-robots and multi-agent systems, where the need of collaboration for achieving a common goal is of primary importance. In particular, adaptation becomes necessary to deal with dynamic environments, and scarce available resources. In this paper, a mathematical framework for modelling the agents' willingness to interact and collaborate, and a dynamic adaptation strategy for controlling the agents' behavior, which accounts for factors such as progress toward a goal and available resources for completing a task among others, are proposed. The performance of the proposed strategy is evaluated through a fire rescue scenario, where a team of simulated mobile robots need to extinguish all the detected fires and save the individuals at risk, while having limited resources. The simulations are implemented as a ROS-based multi agent system, and results show that the proposed adaptation strategy provides a more stable performance than a static collaboration policy. 

National Category
Engineering and Technology Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-40254 (URN)10.1109/SASO.2018.00026 (DOI)000459885200016 ()2-s2.0-85061910844 (Scopus ID)9781538651728 (ISBN)
Conference
12th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, SASO 2018; Trento; Italy; 3 September 2018 through 7 September 2018
Available from: 2018-07-18 Created: 2018-07-18 Last updated: 2019-03-14Bibliographically approved
Frasheri, M., Curuklu, B. & Ekström, M. (2018). Analysis of perceived helpfulness in adaptive autonomous agent populations. In: Transactions on Computational Collective Intelligence XXVIII: . Paper presented at International Conference on Agents and Artificial Intelligence, ICAART 2016 and 2017 (pp. 221-252). Springer Verlag, 10780
Open this publication in new window or tab >>Analysis of perceived helpfulness in adaptive autonomous agent populations
2018 (English)In: Transactions on Computational Collective Intelligence XXVIII, Springer Verlag , 2018, Vol. 10780, p. 221-252Conference paper, Published paper (Refereed)
Abstract [en]

Adaptive autonomy allows agents to change their autonomy levels based on circumstances, e.g. when they decide to rely upon one another for completing tasks. In this paper, two configurations of agent models for adaptive autonomy are discussed. In the former configuration, the adaptive autonomous behavior is modeled through the willingness of an agent to assist others in the population. An agent that completes a high number of tasks, with respect to a predefined threshold, increases its willingness, and vice-versa. Results show that, agents complete more tasks when they are willing to give help, however the need for such help needs to be low. Agents configured to be helpful will perform well among alike agents. The second configuration extends the first by adding the willingness to ask for help. Furthermore, the perceived helpfulness of the population and of the agent asking for help are used as input in the calculation of the willingness to give help. Simulations were run for three different scenarios. (i) A helpful agent which operates among an unhelpful population, (ii) an unhelpful agent which operates in a helpful populations, and (iii) a population split in half between helpful and unhelpful agents. Results for all scenarios show that, by using such trait of the population in the calculation of willingness and given enough interactions, helpful agents can control the degree of exploitation by unhelpful agents. © Springer International Publishing AG, part of Springer Nature 2018.

Place, publisher, year, edition, pages
Springer Verlag, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10780
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-39195 (URN)10.1007/978-3-319-78301-7_10 (DOI)2-s2.0-85046355290 (Scopus ID)978-3-319-78301-7 (ISBN)
Conference
International Conference on Agents and Artificial Intelligence, ICAART 2016 and 2017
Note

This twenty-eight issue is a special issue with 11 selected papers from the International Conference on Agents and Artificial Intelligence, ICAART 2016 and 2017 editions.

Available from: 2018-05-11 Created: 2018-05-11 Last updated: 2018-07-18Bibliographically approved
Frasheri, M., Curuklu, B. & Ekström, M. (2018). Comparison Between Static and Dynamic Willingness to Interact in Adaptive Autonomous Agents. In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence: . Paper presented at 10th International Conference on Agents and Artificial Intelligence ICAART'18, 16 Jan 2018, Funchal, Madeira, Portugal (pp. 258-267). , 1
Open this publication in new window or tab >>Comparison Between Static and Dynamic Willingness to Interact in Adaptive Autonomous Agents
2018 (English)In: Proceedings of the 10th International Conference on Agents and Artificial Intelligence, 2018, Vol. 1, p. 258-267Conference paper, Published paper (Refereed)
Abstract [en]

Adaptive autonomy (AA) is a behavior that allows agents to change their autonomy levels by reasoning on their circumstances. Previous work has modeled AA through the willingness to interact, composed of willingness to ask and give assistance. The aim of this paper is to investigate, through computer simulations, the behavior of agents given the proposed computational model with respect to different initial configurations, and level of dependencies between agents. Dependency refers to the need for help that one agent has. Such need can be fulfilled by deciding to depend on other agents. Results show that, firstly, agents whose willingness to interact changes during run-time perform better compared to those with static willingness parameters, i.e. willingness with fixed values. Secondly, two strategies for updating the willingness are compared, (i) the same fixed value is updated on each interaction, (ii) update is done on the previous calculated value. The maximum number of completed tasks which need assistance is achieved for (i), given specific initial configurations.

Keywords
Adaptive Autonomy, Multi-agent Systems, Collaborative Agents
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-38963 (URN)10.5220/0006648002580267 (DOI)2-s2.0-85047721508 (Scopus ID)978-989-758-275-2 (ISBN)
Conference
10th International Conference on Agents and Artificial Intelligence ICAART'18, 16 Jan 2018, Funchal, Madeira, Portugal
Available from: 2018-05-08 Created: 2018-05-08 Last updated: 2018-07-18Bibliographically approved
Trinh, L., Ekström, M. & Curuklu, B. (2018). Toward Shared Working Space of Human and Robotic Agents Through Dipole Flow Field for Dependable Path Planning. Frontiers in Neurorobotics, 12, Article ID 28.
Open this publication in new window or tab >>Toward Shared Working Space of Human and Robotic Agents Through Dipole Flow Field for Dependable Path Planning
2018 (English)In: Frontiers in Neurorobotics, ISSN 1662-5218, Vol. 12, article id 28Article in journal (Refereed) Published
Abstract [en]

Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i.e., locations, they are also aware of the movements of other entities to find a collision-free path. Thus, this paper proposes a dependable, i.e, safe, reliable and effective, path planning algorithm for a group of agents that share their working space with humans. Firstly, the method employs the Theta* algorithm to initialize the paths from a starting point to a goal for a set of agents. As Theta* algorithm is computationally heavy, it only reruns when there is a significant change of the environment. To deal with the movements of the agents, a static flow field along the configured path is defined. This field is used by the agents to navigate and reach their goals even if the planned trajectories are changed. Secondly, a dipole field is calculated to avoid the collision of agents with other agents and human subjects. In this approach, each agent is assumed to be a source of a magnetic dipole field in which the magnetic moment is aligned with the moving direction of the agent. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. The effectiveness of the proposed approach has been evaluated with extensive simulations. The results show that the static flow field is able to drive agents to the goals with a small number of requirements to update the path of agents. Meanwhile, the dipole flow field plays an important role to prevent collisions. The combination of these two fields results in a safe path planning algorithm, with a deterministic outcome, to navigate agents to their desired goals.

Place, publisher, year, edition, pages
FRONTIERS MEDIA SA, 2018
Keywords
navigation field. Theta star algorithm, dependability, multiple agents, path planning, dynamic environment
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-39973 (URN)10.3389/fnbot.2018.00028 (DOI)000434293100001 ()29928198 (PubMedID)2-s2.0-85048966124 (Scopus ID)
Available from: 2018-06-21 Created: 2018-06-21 Last updated: 2019-06-18Bibliographically approved
Miloradovic, B., Çürüklü, B. & Ekström, M. (2017). A genetic mission planner for solving temporal multi-agent problems with concurrent tasks. In: Lecture Notes in Computer Science, vol. 10386: . Paper presented at 8th International Conference on Swarm Intelligence, ICSI 2017; Fukuoka; Japan; 27 July 2017 through 1 August 2017 (pp. 481-493). Springer Verlag
Open this publication in new window or tab >>A genetic mission planner for solving temporal multi-agent problems with concurrent tasks
2017 (English)In: Lecture Notes in Computer Science, vol. 10386, Springer Verlag , 2017, p. 481-493Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a centralized mission planner is presented. The planner employs a genetic algorithm for the optimization of the temporal planning problem. With the knowledge of agents’ specification and capabilities, as well as constraints and parameters for each task, the planner can produce plans that utilize multi-agent tasks, concurrency on agent level, and heterogeneous agents. Numerous optimization criteria that can be of use to the mission operator are tested on the same mission data set. Promising results and effectiveness of this approach are presented in the case study section. 

Place, publisher, year, edition, pages
Springer Verlag, 2017
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 10386
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-36246 (URN)10.1007/978-3-319-61833-3_51 (DOI)000439782400051 ()2-s2.0-85026782436 (Scopus ID)9783319618326 (ISBN)
Conference
8th International Conference on Swarm Intelligence, ICSI 2017; Fukuoka; Japan; 27 July 2017 through 1 August 2017
Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2018-08-17Bibliographically approved
Li, N., Cürüklü, B., Bastos, J., Sucasas, V., Fernandez, J. A. & Rodriguez, J. (2017). A probabilistic and highly efficient topology control algorithm for underwater cooperating AUV networks. Sensors, 17(5), Article ID 1022.
Open this publication in new window or tab >>A probabilistic and highly efficient topology control algorithm for underwater cooperating AUV networks
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2017 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 5, article id 1022Article in journal (Refereed) Published
Abstract [en]

The aim of the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) project is to make autonomous underwater vehicles (AUVs), remote operated vehicles (ROVs) and unmanned surface vehicles (USVs) more accessible and useful. To achieve cooperation and communication between different AUVs, these must be able to exchange messages, so an efficient and reliable communication network is necessary for SWARMs. In order to provide an efficient and reliable communication network for mission execution, one of the important and necessary issues is the topology control of the network of AUVs that are cooperating underwater. However, due to the specific properties of an underwater AUV cooperation network, such as the high mobility of AUVs, large transmission delays, low bandwidth, etc., the traditional topology control algorithms primarily designed for terrestrial wireless sensor networks cannot be used directly in the underwater environment. Moreover, these algorithms, in which the nodes adjust their transmission power once the current transmission power does not equal an optimal one, are costly in an underwater cooperating AUV network. Considering these facts, in this paper, we propose a Probabilistic Topology Control (PTC) algorithm for an underwater cooperating AUV network. In PTC, when the transmission power of an AUV is not equal to the optimal transmission power, then whether the transmission power needs to be adjusted or not will be determined based on the AUV’s parameters. Each AUV determines their own transmission power adjustment probability based on the parameter deviations. The larger the deviation, the higher the transmission power adjustment probability is, and vice versa. For evaluating the performance of PTC, we combine the PTC algorithm with the Fuzzy logic Topology Control (FTC) algorithm and compare the performance of these two algorithms. The simulation results have demonstrated that the PTC is efficient at reducing the transmission power adjustment ratio while improving the network performance.

Place, publisher, year, edition, pages
MDPI AG, 2017
Keywords
AUV, Probabilistic, Topology control, Transmission power adjustment, Underwater network, Fuzzy logic, MESH networking, Scattering parameters, Topology, Unmanned surface vehicles, Vehicles, Wireless sensor networks, Autonomous underwater vehicles (AUVs), Reliable communication, Topology control algorithms, Transmission power, Underwater environments, Underwater networks, Autonomous underwater vehicles
National Category
Communication Systems
Identifiers
urn:nbn:se:mdh:diva-35525 (URN)10.3390/s17051022 (DOI)000404553300083 ()28471387 (PubMedID)2-s2.0-85019062118 (Scopus ID)
Available from: 2017-06-01 Created: 2017-06-01 Last updated: 2019-06-18Bibliographically approved
Curuklu, B., Martínez, J.-F. & Fresco, R. (2017). Adaptive Autonomy Paves the Way for Disruptive Innovations in Advanced Robotics. ERCIM News, 25-26
Open this publication in new window or tab >>Adaptive Autonomy Paves the Way for Disruptive Innovations in Advanced Robotics
2017 (English)In: ERCIM News, ISSN 0926-4981, E-ISSN 1564-0094, p. 25-26Article in journal (Refereed) Published
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-37335 (URN)000418742300012 ()
Projects
DPAC - Dependable Platforms for Autonomous systems and Control
Available from: 2017-11-28 Created: 2017-11-28 Last updated: 2019-06-26Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5224-8302

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