Heuristic-based Incremental Probabilistic Roadmap for Efficient UAV Exploration in Dynamic Environments

Autonomous exploration in dynamic environments necessitates a planner that can proactively respond to changes and make efficient and safe decisions for robots. Although plenty of sampling-based works have shown success in exploring static environments, their inherent sampling randomness and limited utilization of previous samples often result in sub-optimal exploration efficiency. Additionally, most of these methods struggle with efficient replanning and collision avoidance in dynamic settings. To overcome these limitations, we propose the Heuristic-based Incremental Probabilistic Roadmap Exploration (HIRE) planner for UAVs exploring dynamic environments. The proposed planner adopts an incremental sampling strategy based on the probabilistic roadmap constructed by heuristic sampling toward the unexplored region next to the free space, defined as the heuristic frontier regions. The heuristic frontier regions are detected by applying a lightweight vision-based method to the different levels of the occupancy map. Moreover, our dynamic module ensures that the planner dynamically updates roadmap information based on the environment changes and avoids dynamic obstacles. Simulation and physical experiments prove that our planner can efficiently and safely explore dynamic environments

Low computational-cost detection and tracking of dynamic obstacles for mobile robots with RGB-D cameras

Deploying autonomous robots in crowded indoor environments usually requires them to have accurate dynamic obstacle perception. Although plenty of previous works in the autonomous driving field have investigated the 3D object detection problem, the usage of dense point clouds from a heavy LiDAR and their high computation cost for learning-based data processing make those methods not applicable to small robots, such as vision-based UAVs with small onboard computers. To address this issue, we propose a lightweight 3D dynamic obstacle detection and tracking (DODT) method based on an RGB-D camera, which is designed for low-power robots with limited computing power. Our method adopts a novel ensemble detection strategy, combining multiple computationally efficient but low-accuracy detectors to achieve real-time high-accuracy obstacle detection. Besides, we introduce a new feature-based data association method to prevent mismatches and use the Kalman filter with the constant acceleration model to track detected obstacles. In addition, our system includes an optional and auxiliary learning-based module to enhance the obstacle detection range and dynamic obstacle identification. The users can determine whether or not to run this module based on the available computation resources. The proposed method is implemented in a small quadcopter, and the experiments prove that the algorithm can make the robot detect dynamic obstacles and navigate dynamic environments safely.Comment: 8 pages, 12 figures, 2 table

A vision-based autonomous UAV inspection framework for unknown tunnel construction sites with dynamic obstacles

Tunnel construction using the drill-and-blast method requires the 3D measurement of the excavation front to evaluate underbreak locations. Considering the inspection and measurement task's safety, cost, and efficiency, deploying lightweight autonomous robots, such as unmanned aerial vehicles (UAV), becomes more necessary and popular. Most of the previous works use a prior map for inspection viewpoint determination and do not consider dynamic obstacles. To maximally increase the level of autonomy, this paper proposes a vision-based UAV inspection framework for dynamic tunnel environments without using a prior map. Our approach utilizes a hierarchical planning scheme, decomposing the inspection problem into different levels. The high-level decision maker first determines the task for the robot and generates the target point. Then, the mid-level path planner finds the waypoint path and optimizes the collision-free static trajectory. Finally, the static trajectory will be fed into the low-level local planner to avoid dynamic obstacles and navigate to the target point. Besides, our framework contains a novel dynamic map module that can simultaneously track dynamic obstacles and represent static obstacles based on an RGB-D camera. After inspection, the Structure-from-Motion (SfM) pipeline is applied to generate the 3D shape of the target. To our best knowledge, this is the first time autonomous inspection has been realized in unknown and dynamic tunnel environments. Our flight experiments in a real tunnel prove that our method can autonomously inspect the tunnel excavation front surface.Comment: 8 pages, 8 figure

A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera

The real-time dynamic environment perception has become vital for autonomous robots in crowded spaces. Although the popular voxel-based mapping methods can efficiently represent 3D obstacles with arbitrarily complex shapes, they can hardly distinguish between static and dynamic obstacles, leading to the limited performance of obstacle avoidance. While plenty of sophisticated learning-based dynamic obstacle detection algorithms exist in autonomous driving, the quadcopter's limited computation resources cannot achieve real-time performance using those approaches. To address these issues, we propose a real-time dynamic obstacle tracking and mapping system for quadcopter obstacle avoidance using an RGB-D camera. The proposed system first utilizes a depth image with an occupancy voxel map to generate potential dynamic obstacle regions as proposals. With the obstacle region proposals, the Kalman filter and our continuity filter are applied to track each dynamic obstacle. Finally, the environment-aware trajectory prediction method is proposed based on the Markov chain using the states of tracked dynamic obstacles. We implemented the proposed system with our custom quadcopter and navigation planner. The simulation and physical experiments show that our methods can successfully track and represent obstacles in dynamic environments in real-time and safely avoid obstacles

Transformable Plasmonic Helix with Swinging Gold Nanoparticles

Control over multiple optical elements that can be dynamically rearranged to yield substantial three-dimensional structural transformations is of great importance to realize reconfigurable plasmonic nanoarchitectures with sensitive and distinct optical feedback. In this work, we demonstrate a transformable plasmonic helix system, in which multiple gold nanoparticles (AuNPs) can be directly transported by DNA swingarms to target positions without undergoing consecutive stepwise movements. The swingarms allow for programmable AuNP translocations in large leaps within plasmonic nanoarchitectures, giving rise to tailored circular dichroism spectra. Our work provides an instructive bottom-up solution to building complex dynamic plasmonic systems, which can exhibit prominent optical responses through cooperative rearrangements of the constituent optical elements with high fidelity and programmability

Risk factors and the value of microbiological examinations of COVID-19 associated pulmonary aspergillosis in critically ill patients in intensive care unit: the appropriate microbiological examinations are crucial for the timely diagnosis of CAPA

IntroductionDuring the Omicron pandemic in China, a significant proportion of patients with Coronavirus Disease 2019 (COVID-19) associated pulmonary aspergillosis (CAPA) necessitated admission to intensive care unit (ICU) and experienced a high mortality. To explore the clinical risk factors and the application/indication of microbiological examinations of CAPA in ICU for timely diagnosis are very important.MethodsThis prospective study included patients with COVID-19 admitted to ICU between December 1, 2022, and February 28, 2023. The clinical data of influenza-associated pulmonary aspergillosis (IAPA) patients from the past five consecutive influenza seasons (November 1, 2017, to March 31, 2022) were collected for comparison. The types of specimens and methods used for microbiological examinations were also recorded to explore the efficacy in early diagnosis.ResultsAmong 123 COVID-19 patients, 36 (29.3%) were diagnosed with probable CAPA. CAPA patients were more immunosuppressed, in more serious condition, required more advanced respiratory support and had more other organ comorbidities. Solid organ transplantation, APACHEII score ≥20 points, 5 points ≤SOFA score <10 points were independent risk factors for CAPA. Qualified lower respiratory tract specimens were obtained from all patients, and 84/123 (68.3%) patients underwent bronchoscopy to obtain bronchoalveolar lavage fluid (BALF) specimens. All patients’ lower respiratory tract specimens underwent fungal smear and culture; 79/123 (64.2%) and 69/123 (56.1%) patients underwent BALF galactomannan (GM) and serum GM detection, respectively; metagenomic next-generation sequencing (mNGS) of the BALF was performed in 62/123 (50.4%) patients. BALF GM had the highest diagnostic sensitivity (84.9%), the area under the curve of the mNGS were the highest (0.812).ConclusionThe incidence of CAPA was extremely high in patients admitted to the ICU. CAPA diagnosis mainly depends on microbiological evidence owing to non-specific clinical manifestations, routine laboratory examinations, and CT findings. The bronchoscopy should be performed and the BALF should be obtained as soon as possible. BALF GM are the most suitable microbiological examinations for the diagnosis of CAPA. Due to the timely and accuracy result of mNGS, it could assist in early diagnosis and might be an option in critically ill CAPA patients

Enabling the ability of Li storage at high rate as anodes by utilizing natural rice husks-based hierarchically porous SiO2/N-doped carbon composites

One of the greatest challenges in developing SiO 2/C composites as anode materials in lithium ion batteries (LIBs) is to improve the ability of Li storage at high rate over long-term cycles. Herein, biomass rice husks-based hierarchically porous SiO 2/N-doped carbon composites (BM-RH-SiO 2/NC) were prepared by ball mill and thermal treatment. BM-RH-SiO 2/NC can still retain a reversible capacity of 556 mAh g −1 over 1000 cycles at a high current of 1.0 A g −1. At 5.0 A g −1 the capacity is kept as high as 402 mAh g −1. This impressively long-term cyclic performance and high-rate capability of BM-RH-SiO 2/NC can be ascribed to the synergetic effect between the natural SiO 2 nanoparticles (< 50 nm) and the NC layer. The coating NC layer can not only effectively mitigate the volume strain during charge-discharge process to offer stably cyclic performance but also improve the electrical conductivity. Furthermore, the hierarchical porosity and better electrolyte wettability offer the rapid Li + diffusion and electron transfer, which enhance the pseudocapacitive behavior of whole electrode material and then guarantee fast electrochemical kinetics. Importantly, the unique Li-storage mechanism of active SiO 2 in BM-RH-SiO 2/NC composite was formed and found, which further validates the improved electrochemical capability

Investigation and Evaluation of Winter Indoor Air Quality of Primary Schools in Severe Cold Weather Areas of China

The indoor air quality (IAQ) in classrooms has attracted more and more attention. Unfortunately, there is limited information relating to IAQ in the primary schools in severe cold weather areas of China. In this study, a field investigation on the IAQ of a primary school of Shenyang in northeast China was carried out by physical measurements and questionnaire surveys. The carbon dioxide (CO2) concentration in selected classrooms was continuously measured for a week, and the corresponding ventilation rate was calculated. Meanwhile, the perceptions of the IAQ, the purpose and the comfort degree of window opening have also been recorded from 106 pupils, aged 9&#8722;12. The results indicate the ventilation rate is considerably inadequate in about 99% of the class time due to the low frequency of window opening. The average daily CO2 concentration in these classrooms is 1510&#8722;3863 ppm, which is far higher than the recommended value of 1000 ppm. Most pupils understand that the purpose of opening windows in winter is to improve air quality. However, there are big differences between the measurement results and subjective judgments of indoor air quality. Contrary to the high measured CO2 concentration, around 70% pupils consider the air fresh, and only 3.7% pupils are dissatisfied and even very dissatisfied with IAQ in their classroom. It is necessary to change the existing manual window opening mode, because the pupils&#8217; subjective judgment affects the window opening behavior

Automatic Decision-Making Style Recognition Method Using Kinect Technology

In recent years, somatosensory interaction technology, represented by Microsoft's Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis

DEFT-Cache: A Cost-Effective and Highly Reliable SSD Cache for RAID Storage

This paper proposes a new SSD cache architecture, DEFT-cache, Delayed Erasing and Fast Taping, that maximizes I/O performance and reliability of RAID storage. First of all, DEFT-Cache exploits the inherent physical properties of flash memory SSD by making use of old data that have been overwritten but still in existence in SSD to minimize small write penalty of RAID5/6. As data pages being overwritten in SSD, old data pages are invalidated and become candidates for erasure and garbage collections. Our idea is to selectively delay the erasure of the pages and let these otherwise useless old data in SSD contribute to I/O performance for parity computations upon write I/Os. Secondly, DEFT-Cache provides inexpensive redundancy to the SSD cache by having one physical SSD and one virtual SSD as a mirror cache. The virtual SSD is implemented on HDD but using log-structured data layout, i.e. write data are quickly logged to HDD using sequential write. The dual and redundant caches provide a cost-effective and highly reliable write-back SSD cache. We have implemented DEFT-Cache on Linux system. Extensive experiments have been carried out to evaluate the potential benefits of our new techniques. Experimental results on SPC and Microsoft traces have shown that DEFT-Cache improves I/O performance by 26.81% to 56.26% in terms of average user response time. The virtual SSD mirror cache can absorb write I/Os as fast as physical SSD providing the same reliability as two physical SSD caches without noticeable performance loss