Maintaining connectivity for multi-UAV multi-target search using reinforcement learning

Name: Maintaining connectivity for multi-UAV multi-target search using reinforcement learning
Author: Güven, İslam, Adam, Evşen Yanmaz

İsim	Maintaining connectivity for multi-UAV multi-target search using reinforcement learning
Yazar	Güven, İslam, Adam, Evşen Yanmaz
Basım Tarihi:	2023-10-30
Basım Yeri	- Association for Computing Machinery, Inc
Konu	Convolutional neural networks, Drone networks, Maintaining connectivity, Multi-Agent reinforcement learning, Multi-UAV path planning, Reinforcement learning, Unmanned aerial vehicles
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-840070369-0
Kayıt Numarası	0a8dfe6a-d3b5-466e-b937-d6e405e28229
Lokasyon	Electrical & Electronics Engineering
Tarih	2023-10-30
Notlar	TÜBİTAK
Örnek Metin	We propose a dynamic path planner that uses a multi-Agent reinforcement learning (MARL) model with novel reward functions for multi-drone search and rescue (SAR) missions. We design a mission environment where a multi-drone team covers an area to detect randomly distributed targets and inform the ground base station (BS) by continuously forming relay chains between the targets and the BS. The training procedure of the agents includes a convolutional neural network (CNN) that uses images which represent trajectory histories and connectivity states of each environment entity such as drones, targets, BS. Agents take actions and get feedback from the environment until the mission is completed. The model is trained with multiple missions with randomized target locations. Our results show that the trained model successfully produces mission plans such that the multi-drone system searches the area efficiently while dynamically forming relay chains. The proposed dynamic method leads up to 45% better total detection and mission times in comparison to a pre-planned optimized path planner.
DOI	10.1145/3616392.3623414

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Maintaining connectivity for multi-UAV multi-target search using reinforcement learning

Yazar Güven, İslam, Adam, Evşen Yanmaz

Basım Tarihi 2023-10-30

Basım Yeri - Association for Computing Machinery, Inc

Konu Convolutional neural networks, Drone networks, Maintaining connectivity, Multi-Agent reinforcement learning, Multi-UAV path planning, Reinforcement learning, Unmanned aerial vehicles

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-840070369-0

Kayıt Numarası 0a8dfe6a-d3b5-466e-b937-d6e405e28229

Lokasyon Electrical & Electronics Engineering

Tarih 2023-10-30

Notlar TÜBİTAK

Örnek Metin We propose a dynamic path planner that uses a multi-Agent reinforcement learning (MARL) model with novel reward functions for multi-drone search and rescue (SAR) missions. We design a mission environment where a multi-drone team covers an area to detect randomly distributed targets and inform the ground base station (BS) by continuously forming relay chains between the targets and the BS. The training procedure of the agents includes a convolutional neural network (CNN) that uses images which represent trajectory histories and connectivity states of each environment entity such as drones, targets, BS. Agents take actions and get feedback from the environment until the mission is completed. The model is trained with multiple missions with randomized target locations. Our results show that the trained model successfully produces mission plans such that the multi-drone system searches the area efficiently while dynamically forming relay chains. The proposed dynamic method leads up to 45% better total detection and mission times in comparison to a pre-planned optimized path planner.

DOI 10.1145/3616392.3623414