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Creating of a surroundings map for a remote controlled biomass transportation vehicle

Typiak Andrzej, Typiak Rafał

Journal of Research and Applications in Agricultural Engineering

2019

Vol. 64, nr 4

34--38

This paper presents a design concept of an environment map building system for a baled biomass collecting and transporting remote controlled vehicle. A typical remotely operated vehicle is usually equipped with cameras which may provide insufficient information about the vehicle’s surrounding thus leading to the operator having difficulties in driving such a vehicle in unknown environment. This paper presents a concept of a close area map building system based on additional sensors. The concept assumes equipping the vehicle with laser range find scanners, an inclinometer and radar speed sensors. Combining information from these devices allows to build a map which helps an operator to drive the vehicle in a safer manner and more efficiently.

W artykule przedstawiono koncepcję projektowania mapy otoczenia dla zdalnie sterowanego pojazdu zbierającego i transportującego bele z biomasy. Typowy zdalnie sterowany pojazd jest zwykle wyposażony w kamery, które dają niewystarczające informacje o najbliższym otoczeniu, a operator ma trudności z prowadzeniem takiego pojazdu w nieznanym środowisku. W artykule rozważany jest problem budowy mapy najbliższego otoczenia pojazdu na podstawie dodatkowych urządzeń. Pojazd wyposażony jest w lasery SICK LMS, inklinometr i radary. Łączenie informacji z urządzeń pozwala zbudować mapę, która pomaga operatorowi wydajniej sterować pojazdem.

Real-time Video Vectorization Method for the Purpose of Surroundings Recognition and Mobile Robot Navigation

Zachara M., Tadeusiewicz R.

Journal of Automation Mobile Robotics and Intelligent Systems

2008

Vol. 2, No. 2

10-17

In thise paper a very simple method of the visual information interpretation for recognition and navigation purposes is presented and discussed. The proposed method consists of six steps: image acquisition, edge detection, fast edge vectorization using a high number of short preliminary vectors, aggregation of the preliminary vectors into the form of final vectors. The next stages of the visualthe visual information interpretation for recognition and navigation purposes will be description of the objects’ shapes by means of the final vectors, object recognition and/or robot navigation on the base of comparison between actual shape description and templates memorized during the programming/training process, but they are not discussed in this paper. The main advantage of the proposed method is a simple and time-effective algorithm, which can be performed in real time also by a simple and cheap processor, working as a “brain” of the considered robot.