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. 2024 Dec 5;9(12):744.
doi: 10.3390/biomimetics9120744.

Route Optimization for UVC Disinfection Robot Using Bio-Inspired Metaheuristic Techniques

Mario Peñacoba  1 , Eduardo Bayona  1 , Jesús Enrique Sierra-García  1 , Matilde Santos  2
Affiliations

Route Optimization for UVC Disinfection Robot Using Bio-Inspired Metaheuristic Techniques

Mario Peñacoba et al. Biomimetics (Basel). .

Abstract

The COVID-19 pandemic highlighted the urgent need for effective surface disinfection solutions, which has led to the use of mobile robots equipped with ultraviolet (UVC) lamps as a promising technology. This study aims to optimize the navigation of differential mobile robots equipped with UVC lamps to ensure maximum efficiency in disinfecting complex environments. Bio-inspired metaheuristic algorithms such as the gazelle optimization algorithm, whale optimization algorithm, bat optimization algorithm, and particle swarm optimization are applied. These algorithms mimic behaviors of biological beings such as the evasive maneuvers of gazelles, the spiral hunting patterns of whales, the echolocation of bats, and the collective behavior of flocks of birds or schools of fish to optimize the robot's trajectory. The optimization process adjusts the robot's coordinates and the time it takes to stops at key points to ensure complete disinfection coverage and minimize the risk of excessive UVC exposure. Experimental results show that the proposed algorithms effectively adapt the robot's trajectory to various environments, avoiding obstacles and providing sufficient UVC radiation exposure to deactivate target microorganisms. This approach demonstrates the flexibility and robustness of these solutions, with potential applications extending beyond COVID-19 to other pathogens such as influenza or bacterial contaminants, by tuning the algorithm parameters. The results highlight the potential of bio-inspired metaheuristic algorithms to improve automatic disinfection and achieve safer and healthier environments.

Keywords: Gazelle optimization algorithm (GOA); bat optimization algorithm (BA); bio-inspired algorithms; disinfection; mobile robots; particle swarm optimization (PSO); ultraviolet radiation (UVC); whale optimization algorithm (WOA).

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A 207J High-energy pulse UVC lamp.
Figure 2
Figure 2
A 207J High-energy pulse UVC lamp with radiation measured at 1 m distance.
Figure 3
Figure 3
Hussar Robot; side view (a), front view (b), and isometric view (c).
Figure 4
Figure 4
Workflow of the optimized trajectory execution process.
Figure 5
Figure 5
System architecture.
Figure 6
Figure 6
Kinematic model of the Hussar robot [38].
Figure 7
Figure 7
Speed profiles of the robot (red: longitudinal; blue: angular) [40].
Figure 8
Figure 8
Radiation beam simulation. Black rectangles are obstacles. White color indicates no radiation. All other colors indicate radiation.
Figure 9
Figure 9
State diagram; (right) state machine; and (left) states [41].
Figure 10
Figure 10
Optimization methodology.
Figure 11
Figure 11
Quadrants for less complex (a) and highly complex (b) environments.
Figure 12
Figure 12
Simulation scenarios: low complexity (a) and high complexity (b) [38].
Figure 13
Figure 13
Simple environment cost function evolution.
Figure 14
Figure 14
Low-complexity environment. Initial and optimized trajectory with the GOA (a), initial and optimized trajectory with the WOA (b), initial and optimized trajectory with the BA (c), and initial and optimized trajectory with the PSO (d).
Figure 15
Figure 15
Low-complexity environment. Three-dimensionalradiation with the GOA (a), 3D radiation with the WOA (b), 3D radiation with the BA (c), and 3D radiation with the PSO (d).
Figure 15
Figure 15
Low-complexity environment. Three-dimensionalradiation with the GOA (a), 3D radiation with the WOA (b), 3D radiation with the BA (c), and 3D radiation with the PSO (d).
Figure 16
Figure 16
High-complexity environment. Initial and optimized trajectory with the GOA (a), initial and optimized trajectory with the WOA (b), initial and optimized trajectory with the BA (c), and initial and optimized trajectory with the PSO (d).
Figure 17
Figure 17
High-complexity environment. Three-dimensionalradiation with the GOA (a), 3D radiation with the WOA (b), 3D radiation with the BA (c), and 3D radiation with the PSO (d).
Figure 17
Figure 17
High-complexity environment. Three-dimensionalradiation with the GOA (a), 3D radiation with the WOA (b), 3D radiation with the BA (c), and 3D radiation with the PSO (d).
Figure 18
Figure 18
Complex environment cost functions (a), Zoom 1 area (b) and Zoom 2 area (c).
See this image and copyright information in PMC

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