Prevention of Covid-19 affected patient using multi robot cooperation and Q-learning approach: a solution

Bandita Sahu (), Pradipta Kumar Das (), Manas Ranjan Kabat () and Raghvendra Kumar ()
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Bandita Sahu: VSSUT
Pradipta Kumar Das: VSSUT
Manas Ranjan Kabat: VSSUT
Raghvendra Kumar: GIET University

Quality & Quantity: International Journal of Methodology, 2022, vol. 56, issue 2, No 21, 793-821

Abstract: Abstract Combat with the novel corona virus (COVID-19) has become challenging for all the frontline warriors like, medic people, police and other service provider. Many technology and intelligent algorithms have been developed to set the boundary in its incremental growth. This paper proposed a concept to set the boundary on spreading of this disease among the medic people, who are directly exposed to the COVID-19 patient. To reduce their risk to be infected, we have designed the theoretical model of the medic robot to provide medical services to the confirmed case patient. This paper explains the deployment and execution of assigned work of medic robot for patient carrying, delivering food, medications and handling the emergency health services. The medic robots are divided into various group based on their works. The COVID-19 area is considered as a multi-robot environment, where multiple medic robots will work simultaneously. To achieve the multi-robot cooperation and collision avoidance we have implemented the simplest reinforcement learning approach i.e. the Q-learning approach. We have compared the result with respect to the improved-Q-learning approach. A comparative analysis based on parameters like simplicity, objective, deployed robot category and cooperation has been done with some other approaches mentioned in the literature. For simplicity as well as the time and space complexity purpose the results reveal that Q-learning approach is a better consideration. The proposed approach reduces the mortality rate by 2%.

Keywords: Corona virus; Medic-robot; Multi-robot environment; Multi-robot cooperation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11135-021-01155-1

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