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. 2023 Jan:175:103330.
doi: 10.1016/j.advengsoft.2022.103330. Epub 2022 Nov 25.

Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm

Daniel Vicente Rodrigo  1 , J Enrique Sierra-García  2 , Matilde Santos  3
Affiliations

Glasius bio-inspired neural networks based UV-C disinfection path planning improved by preventive deadlock processing algorithm

Daniel Vicente Rodrigo et al. Adv Eng Softw. 2023 Jan.

Abstract

The COVID-19 pandemic made robot manufacturers explore the idea of combining mobile robotics with UV-C light to automate the disinfection processes. But performing this process in an optimum way introduces some challenges: on the one hand, it is necessary to guarantee that all surfaces receive the radiation level to ensure the disinfection; at the same time, it is necessary to minimize the radiation dose to avoid the damage of the environment. In this work, both challenges are addressed with the design of a complete coverage path planning (CCPP) algorithm. To do it, a novel architecture that combines the glasius bio-inspired neural network (GBNN), a motion strategy, an UV-C estimator, a speed controller, and a pure pursuit controller have been designed. One of the main issues in CCPP is the deadlocks. In this application they may cause a loss of the operation, lack of regularity and high peaks in the radiation dose map, and in the worst case, they can make the robot to get stuck and not complete the disinfection process. To tackle this problem, in this work we propose a preventive deadlock processing algorithm (PDPA) and an escape route generator algorithm (ERGA). Simulation results show how the application of PDPA and the ERGA allow to complete complex maps in an efficient way where the application of GBNN is not enough. Indeed, a 58% more of covered surface is observed. Furthermore, two different motion strategies have been compared: boustrophedon and spiral motion, to check its influence on the performance of the robot navigation.

Keywords: Complete coverage path planning; Deadlocks; Escape routes; Mobile robot; UV-C.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Propagation of UV-C light according to the inverse square law .
Fig. 2
Fig. 2
UV radiation profile approximation (550μJ/cm2) .
Fig. 3
Fig. 3
Kinematic model of a differential robot in the Cartesian coordinate space .
Fig. 4
Fig. 4
System architecture diagram, which involves the complete coverage path planning and the simulation environment.
Fig. 5
Fig. 5
Motion strategy comparison in an empty map.
Fig. 6
Fig. 6
Dose received in an empty map, at the end of the disinfection process, with the UV distribution model proposed and lamp power DUV=550μJ/cm2.
Fig. 7
Fig. 7
Deadlock situation solved with a neural approach.
Fig. 8
Fig. 8
Deadlock situation solved using ERGA.
Fig. 9
Fig. 9
Deadlock situation solved by different approaches.
Fig. 10
Fig. 10
Working environments used in the simulation for the analysis of the disinfection process.
Fig. 11
Fig. 11
High obstacle distribution map simulation.
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