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EN
Land-use change analysis is a key tool for studying the effects of planning and economic-political decisions on land-use. The aim of this study was to identify land-use changes in the Straconka district of Bielsko-Biała (southern Poland) using GIS tools. Historical and contemporary orthophotomaps of study area were analysed. Five land use classes were included: roads, loose buildings, compact buildings, green areas, forest areas. The significant changes in the urbanisation of the once typical village, now an integral district of the city of Bielsko-Biała, were demonstrated. In the period from 1998 to 2022, the area of compact development has clearly increased - from 23.2 to 31.2%. A consequence of the urbanisation development was also an increase in the length of hardened traffic routes - from 21.7 to 35.3 km. Due to the densification of development, the proportion of the area occupied by loose housing decreased. Urbanisation has also resulted in a reduction in the area of green spaces - from 27.4 to 14.6%. These were used as meadows for livestock feed. At present, with few exceptions, they are not used for agriculture. They are often areas of a recreational nature, but are just as often subject to uncontrolled forest succession. Forest areas have slightly increased their share in the spatial development structure of the district - from 46 to 49%. This is the result of both forest recovery and deliberate forest cultivation.
EN
This paper presents application of a neural network in the task of planning a mobile robot trajectory. First part contains a review of literature focused on the mobile robots’ orientation and overview of artificial neural networks’ application in area of robotics. In these sections devices and approaches for collecting data of mobile robots environment have been specified. In addition, the principle of operation and use of artificial neural networks in trajectory planning tasks was also presented. The second part focuses on the mobile robot that was designed in a 3D environment and printed with PLA material. The main onboard logical unit is Arduino Mega. Control system consist of 8-bits microcontrollers and 13 Mpix camera. Discussion in part three describes the system positioning capability using data from the accelerometer and magnetometer with overview of data filtration and the study of the artificial neural network implementation to recognize given trajectories. The last chapter contains a summary with conclusions.
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