Modelling and animation of crawler UGV's caterpillars is a complicated task, which has not been completely resolved in ROS/Gazebo simulators. In this paper, we proposed an approximation of track-terrain interaction of a crawler UGV, perform modelling and simulation of Russian crawler robot "Engineer" within ROS/Gazebo and visualize its motion in ROS/RViz software. Finally, we test the proposed model in heterogeneous robot group navigation scenario within uncertain Gazebo environment.

Path planning for autonomous mobile robots is an important task within robotics field. It is common to use one of the two classical approaches in path planning: a global approach when an entire map of a working environment is available for a robot or local methods, which require the robot to detect obstacles with a variety of onboard sensors as the robot traverses the environment. In our previous work, a multi-criteria spline algorithm prototype for a global path construction was developed and tested in Matlab environment. The algorithm used the Voronoi graph for computing an initial path that serves as a starting point of the iterative method. This approach allowed finding a path in all map configurations whenever the path existed. During the iterative search, a cost function with a number of different criteria and associated weights was guiding further path optimization. A potential field method was used to implement some of the criteria. This paper describes an implementation of a modified spline-based algorithm that could be used with real autonomous mobile robots. Equations of the characteristic criteria of a path optimality were further modified. The obstacle map was previously presented as intersections of a finite number of circles with various radii. However, in real world environments, obstacles’ data is a dynamically changing probability map that could be based on an occupancy grid. Moreover, the robot is no longer a geometric point. To implement the spline algorithm and further use it with real robots, the source code of the Matlab environment prototype was transferred into C++ programming language. The testing of the method and the multi criteria cost function optimality was carried out in ROS/Gazebo environment, which recently has become a standard for programming and modeling robotic devices and algorithms. The resulting spline-based path planning algorithm could be used on any real robot, which is equipped with a laser rangefinder. The algorithm operates in real time and the influence of the objective function criteria parameters are available for dynamic tuning during a robot motion.

The COVID-19 pandemic keeps spreading across the world and, while national governments concentrate on lockdowns and restrictions to mitigate the disaster, advanced technologies could be employed more widely to fight the pandemic. This paper surveys existing robotic solutions that could be employed for pandemic care and presents a systematized description of desired robot properties based on a particular application area and target users. We propose a new generation infection hospital framework that integrates existing robotic tools toward pandemic mitigation and discuss ethical aspects of their use within the framework. GRAPHICAL ABSTRACT

A fiducial marker system is a system of unique 2D (planar) marker, which is placed in an environment and automatically will be detected with a camera with a help of a corresponding detection algorithm. Application areas of these markers include industrial systems, augmented reality, robots navigation, human-robot interaction and others. Marker system designed for such different applications must be robust to such factors as view angles, occlusions, changing distances, etc. This paper compares three existing systems of markers: ARTag, AprilTag, and CALTag. As a benchmark, we use their reliability and detection rate in presence of occlusions of various types and intensity. The paper presents experimental comparison of these markers. The marker detection was performed with a simple inexpensive Web camera.

Optimal path planning in dynamic environments for an unmanned vehicle is a complex task of mobile robotics that requires an integrated approach. This paper describes a path planning algorithm, which allows to build a preliminary motion trajectory using global information about environment, and then dynamically adjust the path in real-time by varying objective function weights. We introduce a set of key parameters for path optimization and the algorithm implementation in MATLAB. The developed algorithm is suitable for fast and robust trajectory tuning to a dynamically changing environment and is capable to provide efficient planning for mobile robots.