Structural model of a training computer program for improving professional skills of a student in a role of a district polyclinic physician

An important feature of a medical education in the context of the COVID-19 pandemic is a migration of classes into an online or mixed format, which requires simulated-based teaching methods with elements of robotics and artificial intelligence. We analyzed computer programs for maintaining medical records of patients that are employed by various polyclinics of Kazan city. Based on the analysis, we summarized requirements for a training computer program that could develop medical records maintaining skills of university students. The proposed program focuses on modeling situations associated with pre-hospital stage processing of medical records for patients. The paper presents an improved architecture of the program and an example of its use

Automatic tool for Gazebo world construction: From a grayscale image to a 3D solid model

© 2020 IEEE. Robot simulators provide an easy way for evaluation of new concepts and algorithms in a simulated physical environment reducing development time and cost. Therefore it is convenient to have a tool that quickly creates a 3D landscape from an arbitrary 2D image or 2D laser range finder data. This paper presents a new tool that automatically constructs such landscapes for Gazebo simulator. The tool converts a grayscale image into a 3D Collada format model, which could be directly imported into Gazebo. We run three different simultaneous localization and mapping (SLAM) algorithms within three varying complexity environments that were constructed with our tool. A real-time factor (RTF) was used as an efficiency benchmark. Successfully completed SLAM missions with acceptable RTF levels demonstrated the efficiency of the tool. The source code is available for free academic use

On the Problems of SLAM Simulation for Mobile Robots in the Arctic Conditions

© 2020, Springer Nature Switzerland AG. Autonomous robots in the Arctic cover a number of strategically important tasks, including climate research, reconnaissance, transportation, material delivery, search and rescue. These goals require adapting standard navigation, localization and mapping algorithms to the harsh Arctic conditions, which do not allow their straightforward usage. The paper describes main problems of using simultaneous localization and mapping (SLAM) algorithms in the Arctic region and formulate requirements for the Arctic landscape simulator. With regard to these requirements we constructed Arctic terrains in Gazebo simulator, which implemented three of the eight proposed Arctic features, and studied behavior of ROS implementations of GMapping, Hector SLAM, ORB-SLAM2 and RTAB-Map SLAM algorithms within the obtained terrains

Artificial intelligence based framework for robotic search and rescue operations conducted jointly by international teams

© Springer Nature Singapore Pte Ltd 2020. Many countries suffer from various natural disasters, including heavy rains, that are associated with further flood and landslide disasters. Based on our experiences of different disasters response, we develop a joint international operation framework for a disaster site management with distributed heterogeneous robotic teams that consist of unmanned aerial, ground, surface, and underwater vehicles. The artificial intelligence-based information collection system, which is targeting to become a worldwide standard, contains interaction protocols, thematic mapping approaches, and map fusion processes. The project provides a new working framework and control strategies for heterogeneous robotic teams’ cooperative behavior in sensing, monitoring, and mapping of flood and landslide disaster areas. In this paper, we present an overview of the system and a first stage toward robot interaction protocols development and the system modeling within robot operating system’s Gazebo environment

Comparative analysis of ros-based centralized methods for conducting collaborative monocular visual slam using a pair of uavs

© CLAWAR Association Ltd. Unmanned Aerial Vehicle (UAV) is a flying robot that acts without a constant human pilot involvement. UAVs are applied in military and civilian areas, in search and rescue operations, 3D mapping, simultaneous localization and mapping (SLAM) and other tasks. SLAM approaches are based on various sensors usage including lidars and cameras. Visual SLAM approaches rely on visual sensing systems and successfully operate within GPS-denied environments. Further, applying several UAVs allows for complex tasks that cannot be handled by a single robot, minimizes exploration time and adds a security level for a case of a single robot failure. This paper presents a comparison of two most applicable vision-based collaborative monocular SLAM methods in Robot operating system, CORB-SLAM and CCM-SLAM, that run on a pair of UAVs. The evaluation is performed on preassembled datasets that correspond to a virtual environment in the Gazebo simulator. The error estimation in virtual experiments demonstrated that CCM-SLAM has a higher global localization accuracy than CORB-SLAM

Ultrasound sensor modeling in Gazebo simulator for diagnostics of abdomen pathologies

Ultrasound imaging is a widely used technique in medicine. It is a non-invasive medical procedure that uses sound waves to produce pictures of the inside of the human body. Ultrasound helps to diagnose conditions of soft tissues and detecting a wide range of medical diseases and pathologies. This paper presents a 3D model and a Gazebo plugin of a medical ultrasound sensor that performs ultrasound imaging of an abdomen surface. The ultrasound device is represented as an end-effector for a KUKA IIWA LBR manipulator model but can also be used for other manipulator models. We introduce an implementation of a complex abdomen 3D model that consists of fat, muscle, and intestine tissue layers. Each tissue has its unique parameters used by the Gazebo medical ultrasound plugin. The developed ultrasound sensor was successfully tested in the Gazebo simulator and was able to provide visualizing a structure of the abdomen internals for further diagnostics