Optimized Coverage Planning for UV Surface Disinfection

TL;DR

This paper introduces a near-optimal coverage planning method for mobile UV disinfection robots that maximizes surface exposure efficiency while considering collision and occlusion constraints, improving disinfection coverage and speed.

Contribution

It presents a novel two-stage optimization scheme for UV coverage planning that accounts for occlusion and collision, with GPU acceleration for efficiency.

Findings

Achieves more surface coverage in less time compared to existing strategies.

Produces near-optimal disinfection plans validated through empirical results.

Enables comparison of different UV robot designs based on coverage efficiency.

Abstract

UV radiation has been used as a disinfection strategy to deactivate a wide range of pathogens, but existing irradiation strategies do not ensure sufficient exposure of all environmental surfaces and/or require long disinfection times. We present a near-optimal coverage planner for mobile UV disinfection robots. The formulation optimizes the irradiation time efficiency, while ensuring that a sufficient dosage of radiation is received by each surface. The trajectory and dosage plan are optimized taking collision and light occlusion constraints into account. We propose a two-stage scheme to approximate the solution of the induced NP-hard optimization, and, for efficiency, perform key irradiance and occlusion calculations on a GPU. Empirical results show that our technique achieves more coverage for the same exposure time as strategies for existing UV robots, can be used to compare UV robot designs, and produces near-optimal plans. This is an extended version of the paper originally contributed to ICRA2021.