Multi Target Task Distribution and Path Planning for Multi-Agents

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dc.authoridDönmez, Emrah/0000-0003-3345-8344
dc.authoridKocamaz, Adnan Fatih/0000-0002-7729-8322
dc.authorwosidDönmez, Emrah/W-2891-2017
dc.authorwosidKocamaz, Adnan Fatih/C-2820-2014
dc.contributor.authorDonmez, Emrah
dc.contributor.authorKocamaz, Adnan Fatih
dc.date.accessioned2024-08-04T20:45:50Z
dc.date.available2024-08-04T20:45:50Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.descriptionInternational Conference on Artificial Intelligence and Data Processing (IDAP) -- SEP 28-30, 2018 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractIn the field of robotics, there are basic subjects such as control design, machine vision, path planning, performing assigned tasks. It is widely focused on one robot systems in literature. There are also studies on multiple robots and multiple target / task sharing. In this study, a task sharing system for navigating multiple targets with multiple robots and a navigation algorithm for finding the appropriate route has been investigated. This study is similar with respect to the problem of MultiTraveling Salesman Problem (M-TSP). In task sharing system, task balancing is made according to passive or active states. In load balancing, the goal is to avoid overloading a robot. After assignment of tasks to the relevant robots, the target cluster appears as many as the number of robots. For each set, the robot position and the available targets are considered as one of the graph nodes. The distance matrix is created by making these formed nodes as fully connected. Then, the path plan is made based on the proximity cost to the target nodes from the initial position of the robot (which is the starting node). When the next node to be moved is considered as the new starting position, each node that is visited, it is extracted from the graph connectivity matrix. The target and robots are labeled with colored labels and the positions of the objects are calculated by color-based quantization and thresholding methods. It has been observed that the system can make the task sharing and creates the appropriate path plan successfully with the variable target number and the different target distributions.en_US
dc.description.sponsorshipInonu Univ, Comp Sci Dept,IEEE Turkey Sect,Anatolian Scien_US
dc.description.sponsorshipTUBITAK [116E568]en_US
dc.description.sponsorshipThis study is supported by TUBITAK with 116E568 project number. Experiments have been implemented in ROSE Lab (Robotic and Autonomous Systems Ensemble Laboratory). We thank computer engineering department of Inonu University.en_US
dc.identifier.isbn978-1-5386-6878-8
dc.identifier.scopus2-s2.0-85062560182en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/98707
dc.identifier.wosWOS:000458717400208en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 International Conference on Artificial Intelligence and Data Processing (Idap)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMulti targeten_US
dc.subjectpath planningen_US
dc.subjectM-TSPen_US
dc.subjecttask sharingen_US
dc.titleMulti Target Task Distribution and Path Planning for Multi-Agentsen_US
dc.typeConference Objecten_US

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