A smart guidance navigation robot using petri net, database location, and radio frequency identification

The objective of this research is, to explain a new framework to navigate the movement of the robot towards a target goal. This involves the need for the robot to move from the initial position to 1 out of 30 rooms. Therefore the strategy used involves the combination of the room database stored in the RFID data using the petri net (PN) method to simulate and model the movement of the robot for navigation after which the dynamic behavior of the robot is moving to the desired location was analyzed. The process started from the creation of an environmental map determined by the user followed by modeling through PN and the result was used to produce a marking value which explains and navigates the movement of the robot towards the selected room. The marking value was also used as the database for the robot's movement and later substituted with the RFID to be used as the sensor input in the implementation stage. It was concluded that the robot has the ability to move to the target position according to the database stored in RFID and designed to move forward and turn left and right. For example, it followed the marking value M1 M2 M3M13M12 M11 M10 M9 M8 to Room 1 and M1 M2 M46 M47 to Room 29.

Outdoor SLAM Using Monocular Vision-Based Localization with LIDAR-Aided Mapping for service robot in Highway

This paper designed an intelligent service robot system in highway based on multi-sensor fusion. The mobile robot attempts to fuse the lidar information and monocular vision information to estimate the pose of itself and obtain an environmental map. It adapts a new SLAM method which combines lidar and vision information. Lidar is used to obtain the 2D occupancy grid map and the monocular vision SLAM algorithm uses the Extended Kalman Filter (EKF) to magnify the pose estimation. The 3-DOF pose provided by lidar is obtained through Cartographer algorithm and the monocular vision SLAM who offers the 6-DOF pose is realized with ORB-SLAM. The experimental results show that the system is effective in application as an intelligent service robot of highway.

PINUS ELDARICA MEDW. AS INDICATOR OF VEHICULAR POLLUTION

This paper presents a study of developmental stability of Pinus eldarica Medw. in conditions of airborne traffic pollution. The study has been performed over the period of 3 years (from 2017 to 2019) in the Absheron Peninsula, Azerbaijan. The purpose of the study was to assess the morphophysiological changes in the species Pinus eldarica Medw. Exposed to pollution from motor vehicles and thus identify the bioindicator properties of pine trees. The study used morphological, physiological and statistical methods. The indicating data on fluctuating asymmetry and necrosis in Pinus eldarica Medw. was obtained in areas with varying degree of atmospheric pollution. Based on these data, ecological zoning was carried out using GIS technologies, and an environmental map of the studied territories was compiled. Species Pinus eldaric a Medw. can be used as a phytometer for the purpose of environmental monitoring.

A study of children’s environmental awareness and discovery of hidden geographies based on the award-winning maps from the Asahikawa environmental map contest

In this paper, we explored children’s environmental awareness and their discovering of hidden geographies. We employed a secondary data analysis using the 2018 and 2019 award-winning maps from the Asahikawa Environmental Map Contest, and an earlier study on the 2009 award-winning maps. Recently, most of the maps submitted to the contest have explored the cultural environment, whereas the number of maps related to the natural environment and environmental problems have decreased in comparison with the contest 10 years ago. This indicates that the natural environment is not so familiar to children because many of them live in urban areas. The maps showed children’s considerable scientific skills. Some children demonstrated an outstanding ability to map hidden geographies discovered through vivid sensory experience.

Environment map generation in forest using field robot

We are developing an autonomous field robot to save labor in forest operation. About half of Japan's artificial forest area is already available as wood. However, trees are not harvested and forest resources are not effectively used, because the labor and costs are not sufficient. The employment rate of young people in forestry tends to decline, and the unmanaged forest area is expected to increase in the future. Therefore, in our laboratory we propose an autonomous field robot with all terrain vehicles (ATV) that focuses on the automation of work. The robot we are developing automates weeding and observation for all trees in the forest. In this research, we introduced Robot Operating System (ROS) developed in recent years to this robot. In addition, we observed trees by generating an environmental map in the forest using Simultaneous Localization and Mapping (SLAM).