In this paper, we propose a novel algorithm based on Zidan's quantum computing model for remotely controlling the direction of a quantumcontrolled mobile robot equipped with n-movements. The proposed algorithm is based on the measurement of concurrence value for the different movements of the robot. Consider a faraway robot that moves in the deep space (e.g., moves toward a galaxy), and it is required to control the direction of this robot from a ground station by some person Alice. She sends an unknown qubit α |0 + β |1 via the teleportation protocol to the robot. Then, the proposed algorithm decodes the received unknown qubit into an angle θ, that determines the motion direction of the robot, based on the concurrence value. The proposed algorithm has been tested for four and eight movements. Two simulators have been tested; IBM Quantum composer and IBM's system, The two simulators achieved the same result approximately. The motion of any part of the robot is considered, if it has a pre-existing sensor system and a locomotive system,. We can use this technique in many places like in space robots (16 directions). The results show that the proposed technique can be easily used for a huge number of movements. However, increasing the number of movements of the robot will increase the number of qubits.
Line following robot (LFR) plays an important rule in the recent industries, where it can carriers the products from one manufacturing plant to another which are usually in different buildings or separate blocks. This paper presents preliminary development of the LFR which use a normal laptop with a web cam to guide itself through a track. The designed LFR is insensitive to environmental factors such as darkness, lighting, camera distortion or line color. This development is based on computer vision enhancement using digital image processing. It is accomplished through the following stages: Firstly, the acquired RGB image using the web cam is converted to another color coordinates for testing and comparing to choose the best color space. After that, the image contrast is enhanced using adaptive histogram equalization, and then the image is filtered using wiener filter. Finally morphological operations are carried out. The results are evaluated qualitatively and quantitatively from the points of peak signal-to-noise ratio (PSNR), entropy, and image smoothness. The results show that the proposed method is promising for vision enhancement of LFR.
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