A Cutting-Edge Approach to Multi-UAV Mission Planning Using Enhanced Constraint Satisfaction
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| Publicado en: | Journal of Intelligent & Robotic Systems vol. 111, no. 3 (Sep 2025), p. 95 |
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| Autor principal: | |
| Otros Autores: | , |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 045 | 2 | |b d20250901 |b d20250930 | |
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| 100 | 1 | |a Ayvaz, Emre |u Konya Technical University, Department of Computer Engineering, Konya, Turkey (GRID:grid.505922.9) | |
| 245 | 1 | |a A Cutting-Edge Approach to Multi-UAV Mission Planning Using Enhanced Constraint Satisfaction | |
| 260 | |b Springer Nature B.V. |c Sep 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Multi-task planning for diverse UAVs and missions can be approached as a Constraint Satisfaction Problem (CSP) where the Temporal CSP (TCSP) method adds time-based sequential task modeling. The Enhanced Temporal Constraint Satisfaction Problem (ETCSP) method innovatively merges dynamic domain features with a MIQP (Mixed Integer Quadratic Programming) based scoring system to optimally assign UAVs to tasks, moving beyond traditional greedy algorithms. This approach includes an enhanced forward checking method that evaluates task suitability and UAV compatibility in real-time using dynamic programming, thus refining search precision. The ETCSP model was tested in two phases, initially assigning various tasks and then employing CSP methods to monitor task changes over time. Results show that the generic TCSP method requires 61 UAVs to complete 70 tasks, while the Enhanced TCSP achieves the same with only 48 UAVs—which is roughly a 21% reduction in UAV usage. Similarly, the Enhanced method completes the task package in about 3800 min and with 1142 L of fuel, compared to 4855 min and 1615 L for the TCSP method, translating to approximately a 22% reduction in time and a 29% reduction in fuel consumption. | |
| 653 | |a Dynamic programming | ||
| 653 | |a Mixed integer | ||
| 653 | |a Real time | ||
| 653 | |a Constraints | ||
| 653 | |a Fuel consumption | ||
| 653 | |a Energy consumption | ||
| 653 | |a Mission planning | ||
| 653 | |a Quadratic programming | ||
| 653 | |a Greedy algorithms | ||
| 700 | 1 | |a Atay, Yılmaz |u Gazi University, Department of Computer Engineering, Ankara, Turkey (GRID:grid.25769.3f) (ISNI:0000 0001 2169 7132) | |
| 700 | 1 | |a Babaoğlu, İsmail |u Konya Technical University, Department of Computer Engineering, Konya, Turkey (GRID:grid.505922.9) | |
| 773 | 0 | |t Journal of Intelligent & Robotic Systems |g vol. 111, no. 3 (Sep 2025), p. 95 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3241056326/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3241056326/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |