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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Zusammenfassung: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.
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ISSN:2076-0825
2076-0825
DOI:10.3390/act12010012