Vision-based Autonomous Disinfection of High-Touch Surfaces in Indoor Environments

Sean, Roelofs,; Benoit, Landry; Jalil, Myra Kurosu; Martín, Adrián; Koppaka, Saisneha; Tang, Susan; Pavone, Marco

doi:10.23919/iccas52745.2021.9649848

Cited by 3 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, research on intelligent disinfection vehicles has become a hot topic. It has the aim of improving the efficiency and effectiveness of the intelligent disinfectionvehicle systems by enhancing the design, control, and path planning abilities [5,6]. However, despite some progress, there are still several key challenges that need to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Disinfection-Vehicle System Design and Its Global Path Planning

et al. 2023

View full text Add to dashboard Cite

We aimed to research the design and path-planning methods of an intelligent disinfection-vehicle system. A ROS (robot operating system) system was utilized as the control platform, and SLAM (simultaneous localization and mapping) technology was used to establish an indoor scene map. On this basis, a new path-planning method combining the A* algorithm and the Floyd algorithm is proposed to ensure the safety, efficiency, and stability of the path. Simulation results show that with the average shortest distance between obstacles and paths of 0.463, this algorithm reduces the average numbers of redundant nodes and turns in the path by 70.43% and 31.1%, respectively, compared to the traditional A* algorithm. The algorithm has superior performance in terms of safety distance, path length, and redundant nodes and turns. Additionally, a mask recognition and pedestrian detection algorithm is utilized to ensure public safety. The results of the study indicate that the method has satisfactory performance. The intelligent disinfection-vehicle system operates stably, meets the indoor mapping requirements, and can recognize pedestrians and masks.

show abstract

Section: Introductionmentioning

confidence: 99%