Search and Rescue (SAR) missions in harsh and unstructured Sub-Terranean (Sub-T) environments in the presence of aerosol particles have recently become the main focus in the field of robotics. Aerosol particles such as smoke and dust directly affect the performance of any mobile robotic platform due to their reliance on their onboard perception systems for autonomous navigation and localization in Global Navigation Satellite System (GNSS)-denied environments. Although obstacle avoidance and object detection algorithms are robust to the presence of noise to some degree, their performance directly relies on the quality of captured data by onboard sensors such as Light Detection And Ranging (LiDAR) and camera. Thus, this paper proposes a novel modular agnostic filtration pipeline based on intensity and spatial information such as local point density for removal of detected smoke particles from Point Cloud (PCL) prior to its utilization for collision detection. Furthermore, the efficacy of the proposed framework in the presence of smoke during multiple frontier exploration missions is investigated while the experimental results are presented to facilitate comparison with other methodologies and their computational impact. This provides valuable insight to the research community for better utilization of filtration schemes based on available computation resources while considering the safe autonomous navigation of mobile robots.

Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDARs), the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing acquired data from these sensors with data from Radio Detection and Rangings (RADARs) which can penetrate through such particles. Overall, this will improve the performance of localization and collision avoidance algorithms under such environmental conditions. This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. A detailed description of the onboard sensors and the environment, where the dataset is collected are presented to enable full evaluation of acquired data. Furthermore, the dataset contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format to facilitate the evaluation of navigation, and localization algorithms in such environments. In contrast to the existing datasets, the focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data. Therefore, to validate the dataset, a preliminary comparison of odometry from onboard LiDARs is presented.

Flushing a urinal may produce an "alarming upward flow" of inhalable coronavirus particles, increasing the need for a face mask when in a public restroom, according to a new study. The study, from Yangzhou University in China, which was published in the Physics of Fluid journal on Monday, showed that when flushed, a urinal will allow coronavirus particles to "travel faster and fly father" than a traditional toilet flush. "Urinal flushing indeed promotes the spread of bacteria and viruses," researcher Xiangdong Liu said in a press release (via USA Today). "Wearing a mask should be mandatory within public restrooms during the pandemic, and anti-diffusion improvements are urgently needed to prevent the spread of COVID-19." For the study, researchers measured urinal flushing with computer models, which estimated that in just five seconds into a flush, virus particles could reach a height of more than 2 feet above the ground.

If a poisonous gas were released in a bioterrorism attack, the ability to predict the path of its molecules -- through turbulent winds, temperature changes and unstable buoyancies -- could mean life or death. Understanding how a city will grow and change over a 20-year period could lead to more sustainable planning and affordable housing. Deriving equations to solve such problems -- adding up all of the relevant forces -- is, at best, difficult to the point of near-impossibility and, at worst, actually impossible. But machine learning can help. Using the motion of aerosol particles through a system in flux, researchers from the McKelvey School of Engineering at Washington University in St. Louis have devised a new model, based on a deep learning method, that can help researchers predict the behavior of chaotic systems, whether those systems are in the lab, in the pasture or anywhere else.

The tiny bird, named Obi, is a member of the second smallest parrot species The aim of the study was to gain insight into how birds generate enough lift to fly Obi was trained to fly through lasers illuminating tiny aerosol particles This enabled researchers to track the movement of the particles, and in turn, the air movement created by the bird's flapping wings This enabled researchers to track the movement of the particles, and in turn, the air movement created by the bird's flapping wings A brave bird named Obi was issued with a pair of special goggles before flying through lasers in a bizarre experiment. This created the clearest picture to date of the wake left by a flying animals, said the researchers. The REAL paleo diet: More than 9,000 plant remains reveal... So much for rise of the machines: Starship drone inventor is... Rare weasel species makes a comeback: Pacific fishers... Malaria caused'widespread death' in ancient Rome: DNA... The REAL paleo diet: More than 9,000 plant remains reveal...