Route Planning for Autonomous Mobile Robots Using a Reinforcement Learning Algorithm

This research suggests a new robotic system technique that works specifically in settings such as hospitals or emergency situations when prompt action and preserving human life are crucial. Our framework largely focuses on the precise and prompt delivery of medical supplies or medication inside a de...

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Veröffentlicht in:	Actuators Jg. 12; H. 1; S. 12
Hauptverfasser:	Talaat, Fatma M., Ibrahim, Abdelhameed, El-Kenawy, El-Sayed M., Abdelhamid, Abdelaziz A., Alhussan, Amel Ali, Khafaga, Doaa Sami, Salem, Dina Ahmed
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2023
Schlagworte:	Algorithms Automation autonomous robots collision avoidance Coronaviruses COVID-19 Disease transmission Emergency medical services Health services Machine learning Medical personnel mobile robots Modules Optimization Public health reinforcement learning Robots Route planning Route selection Routing (telecommunications) routing algorithm Software Topology Italy China
ISSN:	2076-0825, 2076-0825
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Abstract	This research suggests a new robotic system technique that works specifically in settings such as hospitals or emergency situations when prompt action and preserving human life are crucial. Our framework largely focuses on the precise and prompt delivery of medical supplies or medication inside a defined area while avoiding robot collisions or other obstacles. The suggested route planning algorithm (RPA) based on reinforcement learning makes medical services effective by gathering and sending data between robots and human healthcare professionals. In contrast, humans are kept out of the patients’ field. Three key modules make up the RPA: (i) the Robot Finding Module (RFM), (ii) Robot Charging Module (RCM), and (iii) Route Selection Module (RSM). Using such autonomous systems as RPA in places where there is a need for human gathering is essential, particularly in the medical field, which could reduce the risk of spreading viruses, which could save thousands of lives. The simulation results using the proposed framework show the flexible and efficient movement of the robots compared to conventional methods under various environments. The RSM is contrasted with the leading cutting-edge topology routing options. The RSM’s primary benefit is the much-reduced calculations and updating of routing tables. In contrast to earlier algorithms, the RSM produces a lower AQD. The RSM is hence an appropriate algorithm for real-time systems.
AbstractList	This research suggests a new robotic system technique that works specifically in settings such as hospitals or emergency situations when prompt action and preserving human life are crucial. Our framework largely focuses on the precise and prompt delivery of medical supplies or medication inside a defined area while avoiding robot collisions or other obstacles. The suggested route planning algorithm (RPA) based on reinforcement learning makes medical services effective by gathering and sending data between robots and human healthcare professionals. In contrast, humans are kept out of the patients’ field. Three key modules make up the RPA: (i) the Robot Finding Module (RFM), (ii) Robot Charging Module (RCM), and (iii) Route Selection Module (RSM). Using such autonomous systems as RPA in places where there is a need for human gathering is essential, particularly in the medical field, which could reduce the risk of spreading viruses, which could save thousands of lives. The simulation results using the proposed framework show the flexible and efficient movement of the robots compared to conventional methods under various environments. The RSM is contrasted with the leading cutting-edge topology routing options. The RSM’s primary benefit is the much-reduced calculations and updating of routing tables. In contrast to earlier algorithms, the RSM produces a lower AQD. The RSM is hence an appropriate algorithm for real-time systems.
Author	Alhussan, Amel Ali Ibrahim, Abdelhameed Salem, Dina Ahmed Abdelhamid, Abdelaziz A. Khafaga, Doaa Sami El-Kenawy, El-Sayed M. Talaat, Fatma M.
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Cites_doi	10.1016/S0140-6736(20)30185-9 10.1093/cid/ciaa255 10.1109/IROS.2016.7758092 10.1109/ACCESS.2022.3196660 10.1109/ACCESS.2022.3172954 10.1007/s10846-019-01112-z 10.1007/s10115-021-01649-2 10.1109/CEI52496.2021.9574467 10.1007/s10586-020-03089-z 10.1109/ACCESS.2021.3061058 10.1016/j.ejor.2021.01.019 10.1038/s41598-022-17684-0 10.1109/ACCESS.2022.3190508 10.1109/ACCESS.2020.3028012 10.3390/vehicles3030027 10.3390/math10162912 10.1109/JCN.2015.000008 10.1109/IROS40897.2019.8967904 10.1177/0278364910390537 10.1109/IROS.2018.8593885
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Copyright	2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SubjectTerms	Algorithms Automation autonomous robots collision avoidance Coronaviruses COVID-19 Disease transmission Emergency medical services Health services Machine learning Medical personnel mobile robots Modules Optimization Public health reinforcement learning Robots Route planning Route selection Routing (telecommunications) routing algorithm Software Topology
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Title	Route Planning for Autonomous Mobile Robots Using a Reinforcement Learning Algorithm
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