GBNN algorithm enhanced by movement planner for UV-C disinfection

Abstract

In order to maintain adequate levels of cleanliness and sanitation in public facilities, pre-vent the buildup of viruses and other harmful pathogens, and ensure health and safety, health and labor authorities have repeatedly warned of the need to adhere to proper dis-infection protocols in the workplace. This is particularly important in public places where food is handled, where there are more vulnerable people, including hospitals and healthcare centers, or where there is a large concentration of people. One promising approach is the combination of ultraviolet-C (UV-C) light and mobile robots to automate disinfection processes. Being this technology effective for disinfection, an excessive dose of UV can damage the materials, limiting its applicability. Therefore, a major challenge for automatic disinfection is to find a route that covers the entire surface, ensures cleanliness, and provides the correct radiation dose while preventing environmental materials from being damaged. To achieve this, in this paper a novel intelligent control approach is pro-posed. A bio-inspired Glasius neural network with a motion planner, an UV estimation module, a speed regulator, and pure pursuit controller are combined into one intelligent system. The motion planner proposes a sequence of movements to go through the space in the most efficient way possible, avoiding obstacles of the environment. The speed controller adjusts the dose of UV-C radiation and the pure pursuit regulator ensures the following of the path. This approach has been tested in various simulation scenarios of increasing complexity and in four different areas of dosing requirements. In simulation, a44% reduction of the maximum dose is achieved, 17% less distance travelled by the robot and, what is more important, 229% more locations with the appropriate dose.