Autonomous Environment Disinfection Based on Dynamic UV-C Irradiation Map.

The COVID-19 pandemic has become a worldwide concern and has motivated the entire scientific community to join efforts to fight it. Studies have shown that SARS-CoV-2 remains viable onsurfaces for days, increasing the chances of human infection. Environmental disinfection is thus an important action to prevent the transmission of the virus. Despite the valuable contribution of the research community to the field of UV-C disinfection by robots, there still lacks a disinfection system that is fully autonomous and computes its trajectory in real-time and in unknown environments. To meet this need, we propose an autonomous UV-C disinfection strategy for indoor environments based on a dynamic Irradiation Map that indicates the amount of energy applied in each region. Our method was tested in different scenarios and compared with other disinfection strategies. Experiments show that our approach delivers better results, especially when targeting high ideal UV-C doses.

Semantic Active Visual Search System Based on Text Information for Large and Unknown Environments.

Different high-level robotics tasks require the robot to manipulate or interact with objects that are in an unexplored part of the environment or not already in its field of view. Although much works rely on searching for objects based on their colour or 3D context, we argue that text information is a useful and functional visual cue to guide the search. In this paper, we study the problem of active visual search (AVS) in large unknown environments. In this paper, we present an AVS system that relies on semantic information inferred from texts found in the environment, which allows the robot to reduce the search costs by avoiding not promising regions for the target object. Our semantic planner reasons over the numbers detected from door signs to decide either perform a goal-directed exploration towards unknown parts of the environment or carefully search in the already known parts. We compared the performance of our semantic AVS system with two other search systems in four simulated environments. First, we developed a greedy search system that does not consider any semantic information, and second, we invited human participants to teleoperate the robot while performing the search. Our results from simulation and real-world experiments show that text is a promising source of information that provides different semantic cues for AVS systems.