Abstract-- Ultraviolet (UV) germicidal radiation is an established non-contact method for surface disinfection in medical environments. Traditional approaches require substantial human intervention to define disinfection areas, complicating automation, while deep learning-based methods often need extensive fine-tuning and large datasets, which can be impractical for large-scale deployment. Additionally, these methods often do not address scene understanding for partial surface disinfection, which is crucial for avoiding unintended UV exposure. We propose a solution that leverages foundation models to simplify surface selection for manipulator-based UV disinfection, reducing human involvement and removing the need for model training. Additionally, we propose a VLM-assisted segmentation refinement to detect and exclude thin and small non-target objects, showing that this reduces mis-segmentation errors. Our approach achieves over 92% success rate in correctly segmenting target and non-target surfaces, and real-world experiments with a manipulator and simulated UV light demonstrate its practical potential for real-world applications. The use of ultraviolet (UV) germicidal radiation as a non-contact approach for disinfection is well known and there is ample research in recent years that have proven their effectiveness to sterilise surfaces in medical environments [1, 2], especially since the COVID-19 pandemic.

The COVID-19 pandemic has severely affected public health, healthcare systems, and daily life, especially amid resource shortages and limited workers. This crisis has underscored the urgent need for automation in hospital environments, particularly disinfection, which is crucial to controlling virus transmission and improving the safety of healthcare personnel and patients. Ultraviolet (UV) light disinfection, known for its high efficiency, has been widely adopted in hospital settings. However, most existing research focuses on maximizing UV coverage while paying little attention to the impact of human activity on virus distribution. To address this issue, we propose a mobile robotic system for UV disinfection focusing on the virus hotspot. The system prioritizes disinfection in high-risk areas and employs an approach for optimized UV dosage to ensure that all surfaces receive an adequate level of UV exposure while significantly reducing disinfection time. It not only improves disinfection efficiency but also minimizes unnecessary exposure in low-risk areas. In two representative hospital scenarios, our method achieves the same disinfection effectiveness while reducing disinfection time by 30.7% and 31.9%, respectively. The video of the experiment is available at: https://youtu.be/wHcWzOcoMPM.

In this paper, we present a novel problem coined multi-robot, multi-objective, and multi-mode routing and scheduling (M^3RS). The formulation for M^3RS is introduced for time-bound multi-robot, multi-objective routing and scheduling missions where each task has multiple execution modes. Different execution modes have distinct resource consumption, associated execution time, and quality. M^3RS assigns the optimal sequence of tasks and the execution modes to each agent. The routes and associated modes depend on user preferences for different objective criteria. The need for M^3RS comes from multi-robot applications in which a trade-off between multiple criteria arises from different task execution modes. We use M^3RS for the application of multi-robot disinfection in public locations. The objectives considered for disinfection application are disinfection quality and number of tasks completed. A mixed-integer linear programming model is proposed for M^3RS. Then, a time-efficient column generation scheme is presented to tackle the issue of computation times for larger problem instances. The advantage of using multiple modes over fixed execution mode is demonstrated using experiments on synthetic data. The results suggest that M^3RS provides flexibility to the user in terms of available solutions and performs well in joint performance metrics. The application of the proposed problem is shown for a team of disinfection robots.} The videos for the experiments are available on the project website: https://sites.google.com/view/g-robot/m3rs/ .

Abstract-- Numerous mobile robots with mounted Ultraviolet-C (UV-C) lamps were developed recently, yet they cannot work in the same space as humans without irradiating them by UV-C. This paper proposes a novel modular and scalable Human-Aware Genetic-based Coverage Path Planning algorithm (GHACPP), that aims to solve the problem of disinfecting of unknown environments by UV-C irradiation and preventing human eyes and skin from being harmed. The system performance in effectiveness and human safety is validated and compared with one of the latest state-of-the-art online coverage path planning algorithms called SimExCoverage-STC. The experimental results confirmed both the high level of safety for humans and the efficiency of the developed algorithm in terms of decrease of path length (by 37.1%), number (39.5%) and size (35.2%) of turns, and time (7.6%) to complete the disinfection task, with a small loss in the percentage of area covered (0.6%), in comparison with the state-of-the-art approach. The irradiation-free area is marked in white. In the face of the COVID-19 world-girdling pandemic, B. Problem statement it has become apparent how important the disinfection of Nowadays, there are many types of effective path planning premises is to our lives.

The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets.

ADATA Technology has collaborated with researchers at Hsinchu National Taiwan University Hospital (NTUH) to introduce the C-Rob Autonomous Mobile Robots. These robots use Artificial Intelligence (AI) to reduce the workload of healthcare workers as Taiwan continues to combat the Covid-19 pandemic. Recently, an outbreak of Covid-19 struck Taiwan, and hospitals are prone to becoming hotspots for transmission. When Covid-infected patients enter hospitals, whether for testing or much-needed medical care, hospital staff will often prioritize these patients and devote less time to those visiting the hospital for non-Covid related reasons. On top of this, a clean environment must be maintained, with frequent disinfection to reduce the risk of transmission.

The Covid-19 pandemic has turned our world upside down. Since last year, the pandemic has induced changes that have both positively and negatively impacted the global economy. We are witnessing how healthcare providers and frontline workers are striving consistently to make the situation better for all of us. Disinfecting spaces are really important to stop the pandemic from spreading. Doing this manually might take a lot of time and it might be dangerous considering the highly infectious nature of this virus. The accelerated adoption of technology in recent years has made the development of robotics and artificial intelligence possible.

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.

BEIJING -- Gaoxian Automation Technology Development, which operates commercial cleaning robot brand Gaussian Robotics, has raised 150 million yuan ($22 million) in its Series B financing round.Founded in 2013, Gaoxian Automation has developed a broad array of cleaning robots for business use and become the first in the industry to offer robotic cleaning solutions using simultaneous localization and mapping, or SLAM, technology.When they operate in an environment where they cannot rely on GPS, such as indoors, robots can use SLAM to build their own maps as they work. The technology allows them to know their position by aligning sensor data they collect with that already collected to create a map for navigation.The company's army of robots can carry out various cleaning and sanitizing tasks, including wiping, sweeping, scrubbing, floor washing and disinfection.It currently offers six series of products. The Ebot Scrubber 75 series are scrubbing robots good at removing grease both indoors and outdoors, while the Ebot Scrubber 50 series are fully automated scrubbing machines for indoor tasks and are capable of medical-grade disinfection. The Ebot Polistar 60 series are indoor polishing robots for stone maintenance, while the Ecobot Sweep Vacuum Mini is an autonomous sweeper for multi-story buildings.

It has long been known that UV light has a sterilising effect because the radiation damages the genetic material of viruses and their ability to replicate. Most viruses - such as SARS-CoV-2 - are covered with a thin membrane that is easily broken apart by UV rays. Paul Hunter, a professor in medicine at University of East Anglia said: 'That UV light inactivates SARS-CoV-2 is not surprising. Indeed UV disinfection is widely used for disinfection of drinking water. 'Given the nature of coronaviruses we would expect them to be especially sensitive to disinfection by either hypochlorite (bleach) or UV light.'