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Abstract Fires in petrochemical complexes are inaccessible because of the intense radiant heat from flames. Therefore, water cannon robots require radiant heat countermeasures to perform firefighting safely. Conventional radiant heat countermeasures employ a self-spraying method, wherein the water cannon robot requires a water tank of capacity 1.5 m3 (= 1500 L) to function for 7–8 min in an environment with a 20 kW/m2 radiation heat. However, the water cannon robot has size limitations because it is transported on one transport vehicle (10 t truck) to the site, and only a tank of capacity ~ 0.02 m3 can be installed on the robot. To overcome these drawbacks, this study proposes a method that utilizes a mountable radiant heat-resistant exterior cover that works with a small amount of water. The cover is made of radiant heat-shielding fireproof clothing with an aluminum coating that reflects 90% of the radiant heat on the surface and a mist nozzle that sprays water on the back surface. The remaining 10% is removed by the heat of vaporization of water sprayed on the back of the clothing and natural convection. The amount of water required for cooling was reduced to 1/80th of that compared to self-spraying because of the use of the developed cover. The proposed method of radiation reflection via vaporization and natural convection can be employed to protect firefighting robots.

Abstract Citizen science programs using organism photographs have become popular, but there are two problems related to photographs. One problem is the low quality of photographs. It is laborious to identify species in photographs taken outdoors because they are out of focus, partially invisible, or under different lighting conditions. The other is difficulty for non-experts to identify species. Organisms usually have interspecific similarity and intraspecific variation, which hinder species identification by non-experts. Deep learning solves these problems and increases the availability of organism photographs. We trained a deep convolutional neural network, Xception, to identify bee species using various quality of bee photographs that were taken by citizens. These bees belonged to two honey bee species and 10 bumble bee species with interspecific similarity and intraspecific variation. We investigated the accuracy of species identification by biologists and deep learning. The accuracy of species identification by Xception (83.4%) was much higher than that of biologists (53.7%). When we grouped bee photographs by different colors resulting from intraspecific variation in addition to species, the accuracy of species identification by Xception increased to 84.7%. The collaboration with deep learning and experts will increase the reliability of species identification and their use for scientific researches.

Distal radius fracture is a common injury, especially in elderly people, and internal fixation with volar locking plate (VLP) is becoming an increasingly popular technique for the management of displaced and/or unstable distal radius fractures. One of the most common complications of this treatment is the flexor tendon rupture, mostly of the flexor pollicis longus (FPL). While the rupture of flexor digitorum tendons to the index (FDI) mostly occurs concomitantly with the rupture of FPL after the treatment using volar plating for distal radial fracture, sole rupture of the FDI without FPL rupture is very rare. Here, we report a case of the sole rupture of FDI after volar locking plating and analyze its pathogenesis indicating that the lift-up of the distal ulnar edge of the plate related to the malcorrection of the fracture site is the culprit for this specific complication.

Abstract The objective of this study was to achieve simultaneous localization and mapping (SLAM) of firefighter robots for petrochemical complexes. Consistency of the SLAM map is important because human operators compare the map with aerial images and identify target positions on the map. The global positioning system (GPS) enables increased consistency. Therefore, this paper describes two Rao-Blackwellized particle filters (RBPFs) based on GPS and light detection and ranging (LIDAR) as SLAM solutions. Fast-SLAM 1.0 and Fast-SLAM 2.0 were used in grid maps for RBPFs in this study. We herein propose the use of Fast-SLAM to combine GPS and LIDAR. The difference between the original Fast-SLAM and the proposed method is the use of the log-likelihood function of GPS; the proposed combination method is implemented using a probabilistic mathematics formulation. The proposed methods were evaluated using sensor data measured in a real petrochemical complex in Japan ranging in size from 550–380 m. RTK-GPS data was used for the GPS measurement and had an availability of 56%. Our results showed that Fast-SLAM 2.0 based on GPS and LIDAR in a dense grid map produced the best results. There was significant improvement in alignment to aerial data, and the mean square root error was 0.65 m. To evaluate the mapping consistency, accurate 3D point cloud data measured by Faro Focus 3D (± 3 mm) was used as the ground truth. Building sizes were compared; the minimum mean errors were 0.17 and 0.08 m for the oil refinery and management building area and the area of a sparse building layout with large oil tanks, respectively. Consequently, a consistent map, which was also consistent with an aerial map (from Google Maps), was built by Fast-SLAM 1.0 and 2.0 based on GPS and LIDAR. Our method reproduced map consistency results for ten runs with a variance of ± 0.3 m. Our method reproduced map consistency results with a global accuracy of 0.52 m in a low RTK-Fix-GPS environment, which was a factory with a building layout similar to petrochemical complexes with 20.9% of RTK-Fix-GPS data availability.

Following publication of the original article [1], the authors reported that one of the authors’ name is spelled incorrectly. In this Correction the incorrect and correct author name are shown. The original publication of this article has been corrected.

This chapter introduces cyber-enhanced rescue canines that digitally strengthen the capability of search and rescue (SAR) dogs using robotics technology. A SAR dog wears a cyber-enhanced rescue canine (CRC) suit equipped with sensors (Camera, IMUs, and GNSS). The activities of the SAR dog and its surrounding view and sound are measured by the sensors mounted on the CRC suit. The sensor data are used to visualize the viewing scene of the SAR dog, its trajectory, its behavior (walk, run, bark, among others), and its internal state via cloud services (Amazon Web Services (AWS), Google Maps, and camera server). The trajectory can be plotted on an aerial photograph captured by flying robots or disaster response robots. The visualization results can be confirmed in real time via the cloud servers on the tablet terminal located in the command headquarters and with the handler. We developed various types of CRC suits that can measure the activities of large- and mediumsize SAR dogs through non-invasive sensors on the CRC suits, and we visualized the activities from the sensor data. In addition, a practical CRC suit was developed with a company and evaluated using actual SAR dogs certified by the Japan Rescue Dog Association (JRDA). Through the ImPACT Tough Robotics Challenge, tough sensing technologies for CRC suits are developed to visualize the activities of SAR dogs. The primary contributions of our research include the following six topics. (1) Lightweight CRC suits were developed and evaluated. (2) Objects left by victims were automatically found using images from a camera mounted on the CRC suits. A deep neural network was used to find suitable features for searching for objects left by victims. (3) The emotions (positive as well as negative) of SAR dogs were estimated from their heart rate variation, which was measured by CRC inner suits. (4) The behaviors of SAR dogs were estimated from an IMU sensor mounted on the CRC suit. (5) The visual SLAM and inertial navigation systems for SAR dogs were developed to estimate trajectory in non-GNSS environments. These emotions, movements, and trajectories are used to visualize the search activities of the SAR dogs. (6) The dog was trained to search an area by controlling the dog with the laser light sources mounted on the CRC suit. [ABSTRACT FROM AUTHOR]