Publikationer från Malmö universitet
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Deep Reinforcement Learning-Based Multirestricted Dynamic-Request Transportation Framework
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT). Malmö universitet, Internet of Things and People (IOTAP). Computer Engineering Department, Bitlis Eren University, Bitlis, Türkiye.ORCID-id: 0000-0002-2223-3927
2023 (Engelska)Ingår i: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, s. 1-11Artikel i tidskrift (Refereegranskat) Epub ahead of print
Abstract [en]

Unmanned aerial vehicles (UAVs) are used in many areas where their usage is increasing constantly. Their popularity, therefore, maintains its importance in the technology world. Parallel to the development of technology, human standards, and surroundings should also improve equally. This study is developed based on the possibility of timely delivery of urgent medical requests in emergency situations. Using UAVs for delivering urgent medical requests will be very effective due to their flexible maneuverability and low costs. However, off-the-shelf UAVs suffer from limited payload capacity and battery constraints. In addition, urgent requests may be requested at an uncertain time, and delivering in a short time may be crucial. To address this issue, we proposed a novel framework that considers the limitations of the UAVs and dynamically requested packages. These previously unknown packages have source–destination pairs and delivery time intervals. Furthermore, we utilize deep reinforcement learning (DRL) algorithms, deep Q-network (DQN), proximal policy optimization (PPO), and advantage actor–critic (A2C) to overcome this unknown environment and requests. The comprehensive experimental results demonstrate that the PPO algorithm has a faster and more stable training performance than the other DRL algorithms in two different environmental setups. Also, we implemented an extension version of a Brute-force (BF) algorithm, assuming that all requests and environments are known in advance. The PPO algorithm performs very close to the success rate of the BF algorithm.

Ort, förlag, år, upplaga, sidor
Institute of Electrical and Electronics Engineers (IEEE), 2023. s. 1-11
Nyckelord [en]
Autonomous unmanned aerial vehicles (UAVs), deep reinforcement learning (DRL), delivery, Autonomous aerial vehicles, Drones, Trajectory, Hospitals, COVID-19, Transportation, Partitioning algorithms
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:mau:diva-64743DOI: 10.1109/tnnls.2023.3341471ISI: 001130328100001Scopus ID: 2-s2.0-85181568675OAI: oai:DiVA.org:mau-64743DiVA, id: diva2:1822675
Tillgänglig från: 2023-12-27 Skapad: 2023-12-27 Senast uppdaterad: 2024-01-16Bibliografiskt granskad

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Akin, Erdal

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Institutionen för datavetenskap och medieteknik (DVMT)Internet of Things and People (IOTAP)
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IEEE Transactions on Neural Networks and Learning Systems
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Totalt: 84 träffar
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