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Miloradović, B., Curuklu, B., Ekström, M. & Papadopoulos, A. (2020). A Genetic Algorithm Approach to Multi-Agent Mission Planning Problems. In: Parlier G.; Liberatore F.; Demange M. (Ed.), Operations Research and Enterprise Systems: (pp. 109-134). Springer, Cham
Open this publication in new window or tab >>A Genetic Algorithm Approach to Multi-Agent Mission Planning Problems
2020 (English)In: Operations Research and Enterprise Systems / [ed] Parlier G.; Liberatore F.; Demange M., Springer, Cham , 2020, p. 109-134Chapter in book (Other academic)
Abstract [en]

Multi-Agent Systems (MASs) have received great attention from scholars and engineers in different domains, including computer science and robotics. MASs try to solve complex and challenging problems (e.g., a mission) by dividing them into smaller problem instances (e.g., tasks) that are allocated to the individual autonomous entities (e.g., agents). By fulfilling their individual goals, they lead to the solution to the overall mission. A mission typically involves a large number of agents and tasks, as well as additional constraints, e.g., coming from the required equipment for completing a given task. Addressing such problem can be extremely complicated for the human operator, and several automated approaches fall short of scalability. This paper proposes a genetic algorithm for the automation of multi-agent mission planning. In particular, the contributions of this paper are threefold. First, the mission planning problem is cast into an Extended Colored Traveling Salesperson Problem (ECTSP), formulated as a mixed integer linear programming problem. Second, a precedence constraint reparation algorithm to allow the usage of common variation operators for ECTSP is developed. Finally, a new objective function minimizing the mission makespan for multi-agent mission planning problems is proposed.

Place, publisher, year, edition, pages
Springer, Cham, 2020
Keywords
Multi-Agent Systems, Multi-agent mission planning, Extended Colored Traveling Salesperson (ECTSP), Genetic algorithms
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-46604 (URN)10.1007/978-3-030-37584-3_6 (DOI)2-s2.0-85076881781 (Scopus ID)978-3-030-37584-3 (ISBN)
Projects
Future factories in the CloudAggregate Farming in the Cloud
Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2020-01-02Bibliographically approved
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
Hanna, A., Bengtsson, K., Dahl, M., Eros, E., Götvall, P.-L. -. & Ekström, M. (2019). Industrial Challenges when Planning and Preparing Collaborative and Intelligent Automation Systems for Final Assembly Stations. In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA: . Paper presented at 24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019, 10 September 2019 through 13 September 2019 (pp. 400-406). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Industrial Challenges when Planning and Preparing Collaborative and Intelligent Automation Systems for Final Assembly Stations
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2019 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 400-406Conference paper, Published paper (Refereed)
Abstract [en]

During the last five decades, automation and robotics have transformed the automotive industry by increasing efficiency and improving the product quality. However, future trucks that will be autonomous, electrical and connected will require a completely new type of flexibility and intelligence in the production systems, especially in the final assembly. To handle the increased complexity of the products, production processes and logistic systems, final assembly must be transformed into collaborative and intelligent automation systems. These systems will include collaborative and deliberative robots (cobots), advanced vision-based control, adaptive safety systems, online optimization and learning algorithms and connected and well-informed human operators. But it will be a huge undertaking to transform current trucks industry such that they can design, implement and maintain large scale collaborative and intelligent automation systems. This paper presents the challenges with current planning and preparation processes for final assembly as well as the requirement and possible solutions for the future processes. An industrial use case at Volvo Trucks based on Sequence Planner and ROS2 is used to evaluate the proposed planning and preparation processes.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
collaborative robot, intelligent system, Production planning, Production preparation, Adaptive control systems, Automobiles, Automotive industry, Factory automation, Intelligent robots, Intelligent systems, Online systems, Production control, Robot programming, Trucks, Visual servoing, Collaborative robots, Industrial challenges, Industrial use case, Intelligent automation systems, Online optimization, Vision based control, Assembly
National Category
Robotics
Identifiers
urn:nbn:se:mdh:diva-47119 (URN)10.1109/ETFA.2019.8869014 (DOI)2-s2.0-85074196485 (Scopus ID)9781728103037 (ISBN)
Conference
24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019, 10 September 2019 through 13 September 2019
Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-02-20Bibliographically approved
Valieva, I., Björkman, M., Åkerberg, J., Ekström, M. & Voitenko, I. (2019). Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio. In: Proceedings - IEEE Military Communications Conference MILCOM: . Paper presented at 2019 IEEE Military Communications Conference, MILCOM 2019, 12 November 2019 through 14 November 2019. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Multiple Machine Learning Algorithms Comparison for Modulation Type Classification for Efficient Cognitive Radio
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2019 (English)In: Proceedings - IEEE Military Communications Conference MILCOM, Institute of Electrical and Electronics Engineers Inc. , 2019Conference paper (Refereed)
Abstract [en]

In this paper the potential of improving channel utilization by signal modulation type classification based on machine learning algorithms has been studied. The classification has been performed between two popular digital modulations: BPSK and FSK in target application. Classification was based on three features available on a popular software defined radio transceiver AD9361: In-phase and quadrature components of the digital time domain signal and signal-to-noise ratio (SNR), measured as RSSI value. Data used for network training, validation and testing was generated by the Simulink model consisting mainly of modulator, transceiver AD9361 and AWGN to generate the signal with SNR ranging from 1 to 30 dB. Twenty-three supervised machine learning algorithms including K-nearest neighbor, Support Vector Machines, Decision Trees and Ensembles have been studied, evaluated and verified against the target application's requirements in terms of classification accuracy and speed. The highest average classification accuracy of 86.9% was achieved by Support Vector Machines with Fine Gaussian kernel, however with demonstrated classification speed of 790 objects per second it was considered unable to meet target application's real-time operation requirement of 2000 objects per second. Fine Decision Trees and Ensemble Boosted Trees have shown optimal performance in terms of both reaching classification speed of 1200000 objects per second and average classification accuracy of 86.0% and 86.3% respectively. Classification accuracy has been also studied as a function of SNR to determine the most accurate classifier for each SNR level. At the target application's demodulation threshold of 12 dB 87.0% classification accuracy has been observed for the Fine Decision Trees, 87.5% for both Fine Gaussian SVM and Coarse KNN. At SNR higher than 27 dB Fine Trees, Coarse KNN have reached 97.5% classification accuracy. The effects of data set size and number of classification features on classification speed and accuracy have been studied too. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
machine learning, modulation classification, signal-to-noise ratio, software defined radio, Analog circuits, Cognitive radio, Decision trees, Digital radio, Forestry, Learning algorithms, Learning systems, Military communications, Modulation, Nearest neighbor search, Radio, Radio transceivers, Signal to noise ratio, Software radio, Support vector machines, Classification accuracy, Classification features, Digital modulations, Modulation type classification, Quadrature components, Software-defined radios, Supervised machine learning, Classification (of information)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-47459 (URN)10.1109/MILCOM47813.2019.9020735 (DOI)2-s2.0-85082395256 (Scopus ID)9781728142807 (ISBN)
Conference
2019 IEEE Military Communications Conference, MILCOM 2019, 12 November 2019 through 14 November 2019
Note

Conference code: 158374; Export Date: 2 April 2020; Conference Paper; CODEN: PMICE

Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2020-04-02Bibliographically 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
Miloradović, B., Frasheri, M., Curuklu, B., Ekström, M. & Papadopoulos, A. (2019). TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model. In: PRIMA 2019: Principles and Practice of Multi-Agent Systems. Paper presented at The 22nd International Conference on Principles and Practice of Multi-Agent Systems PRIMA'19, 28 Oct 2019, Turin, Italy (pp. 478-486).
Open this publication in new window or tab >>TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model
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2019 (English)In: PRIMA 2019: Principles and Practice of Multi-Agent Systems, 2019, p. 478-486Conference paper, Published paper (Refereed)
Abstract [en]

Multi-robot task allocation (MRTA) problems have been studied extensively in the past decades. As a result, several classifications have been proposed in the literature targeting different aspects of MRTA, with often a few commonalities between them. The goal of this paper is twofold. First, a comprehensive overview of early work on existing MRTA taxonomies is provided, focusing on their differences and similarities. Second, the MRTA problem is modelled using an Entity-Relationship (ER) conceptual formalism to provide a structured representation of the most relevant aspects, including the ones proposed within previous taxonomies. Such representation has the advantage of (i) representing MRTA problems in a systematic way, (ii) providing a formalism that can be easily transformed into a software infrastructure, and (iii) setting the baseline for the definition of knowledge bases, that can be used for automated reasoning in MRTA problems.

National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-46316 (URN)10.1007/978-3-030-33792-6_32 (DOI)2-s2.0-85076411190 (Scopus ID)978-3-030-33791-9 (ISBN)
Conference
The 22nd International Conference on Principles and Practice of Multi-Agent Systems PRIMA'19, 28 Oct 2019, Turin, Italy
Projects
DPAC - Dependable Platforms for Autonomous systems and ControlUnicorn -Sustainable, peaceful and efficient robotic refuse handlingAggregate Farming in the Cloud
Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2020-01-02Bibliographically approved
Ahlberg, C., Leon, M., Ekstrand, F. & Ekström, M. (2019). Unbounded Sparse Census Transform using Genetic Algorithm. In: 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV): . Paper presented at 19th IEEE Winter Conference on Applications of Computer Vision (WACV), JAN 07-11, 2019, Waikoloa Village, HI (pp. 1616-1625). IEEE
Open this publication in new window or tab >>Unbounded Sparse Census Transform using Genetic Algorithm
2019 (English)In: 2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV), IEEE , 2019, p. 1616-1625Conference paper, Published paper (Refereed)
Abstract [en]

The Census Transform (CT) is a well proven method for stereo vision that provides robust matching, with respect to object boundaries, outliers and radiometric distortion, at a low computational cost. Recent CT methods propose patterns for pixel comparison and sparsity, to increase matching accuracy and reduce resource requirements. However, these methods are bounded with respect to symmetry and/or edge length. In this paper, a Genetic algorithm (GA) is applied to find a new and powerful CT method. The proposed method, Genetic Algorithm Census Transform (GACT), is compared with the established CT methods, showing better results for benchmarking datasets. Additional experiments have been performed to study the search space and the correlation between training and evaluation data.

Place, publisher, year, edition, pages
IEEE, 2019
Series
IEEE Winter Conference on Applications of Computer Vision, ISSN 2472-6737
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-44332 (URN)10.1109/WACV.2019.00177 (DOI)000469423400170 ()2-s2.0-85063571752 (Scopus ID)978-1-7281-1975-5 (ISBN)
Conference
19th IEEE Winter Conference on Applications of Computer Vision (WACV), JAN 07-11, 2019, Waikoloa Village, HI
Available from: 2019-06-20 Created: 2019-06-20 Last updated: 2019-12-18Bibliographically 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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-5832-5452

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