UV*: A Boustrophedon Pattern-Based Path Planning and Optimization Strategy for an Ultraviolet Disinfection Robot

COVID-19’s global spread had numerous negative consequences for the industry. At present, many antivirus products and technologies have been proposed, among which ultraviolet antivirus has been proven effective and does not pollute the environment. Most ultraviolet disinfection devices use fixed-point methods for disinfection, which is very labor-intensive and inflexible. Mobile ultraviolet disinfection robots are often not optimized because the movement trajectory of the robot is not optimized, resulting in insufficient disinfection areas. This study proposes a heuristic self-optimizing path planning algorithm called UV $^{\ast} $ . UV $^{\ast} $ generates a path trajectory based on the Boustrophedon pattern path planning to achieve full area coverage and then optimizes the path length of the robot’s movement trajectory through the path optimization procedure. This study compares the UV $^{\ast} $ algorithm, the Random method, the Boustrophedon pattern algorithm, the CIRCLES algorithm, and the genetic algorithm (GA). In UV $^{\ast} $ , we proposed a path optimization procedure that can optimize the existing path planning algorithms. The experimental results show that the UV $^{\ast} $ path planning algorithm can reach the goal of full coverage, and the total path length is shorter than that of the Random, CIRCLES, GA, and Boustrophedon pattern path planning algorithms.