A Nature-Inspired Algorithm to Adaptively Safe Navigation of a Covid-19 Disinfection Robot

Autonomous mobile robots have been extensively used in medical services. During the Covid-19 pandemic, ultraviolet type-C irradiation (UV-C) disinfection robots and spray disinfection robots have been deployed in hospitals and other public open spaces. However, adaptively safe navigation of disinfection robots and spray disinfection robots have not been adequately studied. In this paper, an adaptively safe navigation model of Covid-19 disinfection robots is proposed using a nature-inspired method, cuckoo search algorithm (CSA). A Covid-19 disinfection robot is adaptively navigated to decelerate in the vicinity of objects and obstacles thus it can sufficiently spray and illuminate around objects, which assures objects to be fully disinfected against SARS-CoV-2. In addition, the path smoothing scheme based on the \(\mathcal {B}\)-spline curve is integrated with adaptive-speed navigation to generate a safer and smoother trajectory at a reasonable distance from the obstacle. Simulation and comparative studies prove the effectiveness of the proposed model, which can plan a reasonable and short trajectory with obstacle avoidance, and show better performance than other meta-heuristic optimization techniques.