Optimization techniques are nowadays one of the most important branches of computer science due to the limitations of the computing power availability especially in cases of mobile platforms. Algorithms which can be used on such units have to be optimized for memory and computing power. In the paper we focused on optimization techniques for image analysis algorithms by limiting the number of necessary operations. The optimized algorithm has been designed to detect and follow a specified marker, known a priori, and is based on the correlation coefficient match between the acquired template and the current image. The acquired template is taken from the previous processed frame. The match operation is based on Pearson's correlation coefficient, so the whole mechanism is therefore highly demanding in terms of computing power. Optimization is performed mainly using the Region of Interest (ROI) to exclude irrelevant parts of the image. The algorithm is optimized using Grünvald -Letnikov fractional -order backward difference to estimate the position of the marker in a sequence of images. This limits the number of operations required to maintain the precision of the algorithm. Based on the position of the object in previous frames, a fractional order mathematical tool is able to estimate at relatively low cost and with high accuracy the probable position of the object in the incoming image. Here, we explain the workflow of the template detection and following algorithm, as well as the mathematical basis of the fractional order derivative optimization tool. The processor load connected to the optimized algorithm was reduced by over 35%.INDEX TERMS autonomous mobile robots, fractional order difference, template tracking, prediction of moving object position

In this paper a double source images fusion algorithm is presented. Its task is to enhance temperature feature of objects located on the scene. Presented solution is design to be executed in real-time environment. It consists of three stages: in the first part the differences between acquired double source images are examined in order to determine their intersection. Then for each analysed image the contours of all objects located on the scene are determined. This operation is essential to solve disparition issue. In the last stage, based on determined contours and their match coefficient the images are fused. The enhanced temperature feature is displayed on one image acquired from day-light camera.

Robotic vehicles autonomy is the subject of many researches performed by various institutions. There is growing tendency to conduct research in the field of autonomous mobile units, unmanned vehicles and robots moving without operator supervision. Authors of this article presents the concept of the modularized subsystem implementing the functionality of the mobile platform navigation in an unknown environment. The concept has been implemented in the real mobile robot.

The authors in this publication present the concept of control system for the console operator to manage mobile robot platform and its components. Implemented functionality provides the ability to set the robot in different modes. The operator can control the state of robot using simple commands, and is also able to carry more complex tasks. The applied layered software architecture and Object-oriented programming (OOP) allows for easy management of software, maintenance, reusability and its further development.

This papers presents a concept of distributed computer control system of mobile platform. This system was designed and implemented at the Institute of Applied Computer Science (formerly the Computer Engineering Department) as a grant: "Autonomous robot for surveillance and mine detection tasks". The main task was to encapsulate the functionality of the system as independent modules. They cover: the physical layer control, acquisition and fusion of data from different sensors, communication between the modules, analysis of image data. Mentioned mechanisms are also designed to perform autonomous behaviors.