Algorithm to Prevent Obstacle Collision

Algorithm to Prevent impedimenta CollisionDescription In this paper, we develop an algorithm to prevent bang with obstacles main(a) mobile automaton based on optic honoring of obstacles. The input to the algorithm is fed a instalment of images recorded by a camera on the zombie B21R in bowel driving. The selective information is then(prenominal) extracted from the ocular carry image sequence to be drug abused in the navigation algorithm. Optical range provides important information about the country of the environment approximately the automaton, such as the dis vex of the obstacles, the place of the robot, the m to collision and perspicaciousness.The st arraygy is to estimate the number of backsheeshs of the obstacles on the left(p) and in effect(p) side of the frame, this method acting all in allows the robot to move without colliding with obstacles. The reli efficiency of the algorithm is substantiate by some examples.Keywords opthalmic bunk, the dodgin g of balance, reduce of expansion, the quantify for communication, repealing obstacles.1. insane asylumThe term is used for visual navigation of robot motion view as based on analysis of data collected by visual sensors. Golf visual navigation is of particular importance is mainly due(p) to the vast amount of recorded video sensor materials.The aim of our feat is to develop algorithms that will be used for the visual navigation of autonomous mobile robot. The input consists of a sequence of images that argon constantly open navigation system while driving the robot. This sequence of images is provided by monocular reverie system.Then, the robot tries to understand their environment to extract data from a sequence of image data, in this case, opthalmic, and then uses this information as a unravel for the movement. The strategy adopted to avoid collisions with obstacles during movement a balance in the midst of the right and left optical flow vectors.The sample mobile rob ot toughies RWI-B21R. The robot is equipped with WATEC LCL-902 camera (see. Fig. 1). Visuals caught using Matrox Imaging cards at a rate of 30 frames per second.Fig. 1 The robot and the camera.Fig. 2 takes a flowchart of navigation.Fig. 2 Algorithm for prevention of obstacles.The first optical flow vectors are computed from image sequences. To make a decision about the taste of the robot, the calculation of the position of the image plane in the DMZ is essential because the control is transferred to the integrity with respect to the focus of expansion. Then, the depth map calculates the hold to an obstacle, to provide an immediate response to a short distance from the obstacle, or to give a signal to the robot to ignore obstacles.2. Otsenka movement gallery in the sequence of images obtained by camera induced movement of objects in 3-D scene and / or camera motion. 3-D objects and the camera motion is a 2-D motion image plane via the sound projection system. It is a 2-D move ments, alike called apparent motion or optical flow, and should be the starting prove of the intensity and color information of the video.Most of the existing methods of valuation movements are divided into four categories basic techniques of correlation, the basic methods of energy, basic methods of parametric model and the basic methods of differentiation. We chose the technique of differentiation, based on the intensity of the preservation of a moving mastermind for the calculation of the optical flow, for this usage, the standard method of honker and Schunck (Horn and Schunck manual, B., 1981). later calculating the optical flow, we use it for navigation solutions, such as trying to balance the number of left and right sides of the flow to avoid obstacles.3. The laws of optical flow and managementAs well as the remark point moves through the environment, and the sample beam, reflecting this point varies continuously generates an optical flow. . oneness way in which the ro bot may use this information to a movement to achieve a certain type of flow. For example, to maintain the penchant of the environment, the type of optical flow does not flow at all requests. If some flow is detected, the robot should change their susceptibility by producing their effectors (whether wings, wheels or legs). so as to minimize this flow, in accordance with the control law (Andrea, PD William H. Lelise PK, 1998) ..Thus, the change of the internal forces of the robot (as against external forces such as wind) is a range of changes in the optical flow (here from a lose of flow to the minimum flow) .. The optical flow contains information about the fixing of the surface and the direction of the observation point called the focus of expansion (CLE), the time to feeling (TTC), and depth.3.1. The focus of expansion (RF)For the translational movement of the camera, the motion pic is always directed apart from the only point of the corresponding projection of the vector transmission to the image plane. This point is called Focus Expansion (DF), it is calculate on the ground of the principle that the flux vectors are oriented in certain directions with respect to the focus of expansion.At full optical flow is the horizontal part of the DF horizontally located, in accordance with the situation in which the majority of the horizontal components of variance (Negahdaripour, S. Horn, CP 1989). It poop be estimated using a simple counting method, which counts the horizontal components of the signs, which focus on each point of the image. At the point where the maximum divergence, the difference between the number of RF components on the left of the right and the number of components must be minimized. Similarly, we discharge appreciate the vertical position of the FF by identifying the positions of most of the vertical components.Fig. 3 Calculation of the DF.Fig. 4 shows the result of calculating the risk factors in interior RF is shown as a red s quare in the image.Fig. 4 The result of the calculation of risk factors.We also use optical flow to estimate the remaining time of affair with the surface.3.2. Contact timeThe contact time (VC) can be cypher from the optical flow, which is extracted from monocular image sequences acquired during the movement. invigorate Image can be described as a function of the camera parameters and is divided into two periods depending on the rotation (Vt) and the translational components (Vr) at the camera further (V), respectively. The rotational part of the flow field can be mensural from the proprioceptive data (for example, the rotation of the camera) and the focal length. After global variable optical flow is calculated, (Vt) is determined by subtracting (Vr) from (V). From translational optical flow contact time may be calculated by the formula (Tresilian, J., 1990)Here? is the distance from the point in fountainhead (xi yi) on the image plane, the focus of expansion (FR).Note how th e flow rate indicating the length of vector lines ontogenys as the distance from the focus of the image expansion. In fact, this distance is divided at a constant speed, and is a sexual congress rate used to estimate the time of contact.In Fig. 5 we show the VC assessment transfer sequence. (A). The corresponding graph of VC (b) consistent with the theory.Fig. 5 valuation of the VC.3.3. Calculating the depth (intensity)Using the optical flow field is calculated from two successive images, we can find information about the depth of each flow vector calculation by combining VC and speed of the robot while taking pictures.where X depth, V is the speed of the robot, and T VC (calculated for each optical flow vector).Fig. 6 Calculation of depth.Fig. 6 shows an image depth, which is calculated by the VC. The darkest point is near, while the brightest point is the farthest from the scene, so the brightest point is the navigation area of the robot.3.4. Balance strategy for obstacle dodgeThe basic idea behind this strategy is offset (parallax) movement, the robot translates nighest objects rise to more rapid movement on the retina than more conflicting objects. He also takes advantage of the prospects that closer objects also occupy a large field of view, rejecting the average with respect to the associative flow. The robot turns away from the stronger flow. This control law is formulatedHere the difference in the strength of the two sides of the body of the robot, and Is the sum of the magnitude of the optical flow in the visual field of the hemispheres on one side of the header robot.We redeem implemented a strategy to balance our mobile robot. As we construct shown in Fig. 7, the left optical beam (699, 24) is greater than the right (372, 03), so the solution is to turn to the right to avoid obstacles. (A chair to the left of the robot).Fig. 7 The result of the strategy of balance.4. ExperimentsThe robot has been tested in our laboratory robotics, rob ot containing, office chairs, office furniture and computer equipment. In the next experiment, we test the ability of the robot to detect obstacles using only the strategy of balance.Fig. 8 Robot vision.Fig. 8 shows a view from a camera robot initial snapshot.Fig. 9 The first decision.Fig. 9 (a) shows the result of a strategy of balance in which robots have to turn right to avoid the warm obstacle (the board), and Fig. 9 (b) shows the corresponding depth image, which is calculated from the vector of the optical flow. We see that the brightest point is localized to the right of the image, which determines the navigation area of the robot.Fig. 10 shows a robot when it turns to the right.Fig. 10 Robot vision.Fig. 11 (a) shows the result of a balance strategy in which the robot must rotate to the left to avoid the walls, and Fig. 9 (b) shows the corresponding depth of the image in which the brightest point located on the left side of the image.Fig. 11 The second solution.Fig. 12 T he robot is in motion.Figure 12 (a) pokazyvet picture robot in motion in our laboratory, and Figure 12 (b) shows the path that passes by the movement of the robot. We notice that the robot found two principal positions, in which it changes the orientation, position (1) fit into the image and the position of the board (2) corresponds to the wall.Fig. 13 Schedule contact time.Figure 13 shows a graph of left and right optical flow. At the beginning of the stream picture left more than the right, so the robot turns to the right, which corresponds to Figure 12 (d), then right flow increases until it is larger than the left, because the robot is approaching closer to the wall than to the board and we see an increase in the two columns (left and right flow) through the structure 13 in Figure 5, and then the robot turns left, to prevent the wall, it corresponds to position 2 in Fig. 12 (g). It can be seen that the robot successfully wandering around the lab, avoiding obstacles however, we found that the lighting conditions critically important to detect obstacles, because the image produced by the camera is more noisy in low light and makes the optical flow estimation more wrong.5. ConclusionsThe article describes how the optical flow which provides the ability of the robot to avoid obstacles, use control laws, called strategy of balance, whose main purpose is to detect the presence of objects close to the robot on the basis of information on the movement of image brightness.The main difficulty in the use of optic flow to navigate, is that it is unclear what is causing the change of gray determine (motion vector or changing the lighting).Further improvement of the developed method is possible by connecting other sensors (sonar, infrared ), in cooperation with the sensor chamber.6. tie inAndrew, PD William, H. Lelise, PK (1998). Environmental Robotics. Adaptive behavior, Volume 6, No. 3/4, 1998Bergholm, F. Argyros, A. (1999). The gang of central and peripheral vi sion to navigate reactive robot, in the basis of IEEE data processor Society Conference on Computer Vision and phase Recognition, Vol. 2, October 1999, pp. 356-362.Horn, KP Schunck, BG (1981). 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