Introduction:
In order to achieve its mission and reach the GPS goal safely; without crashing into obstacles or leaving the lane, Jo-Car2 uses three main sources of information; GPS with magnetometer, Camera and Laser Range Finder.
Using the combination between the GPS and magnetometer readings, we can determine the desired direction of the robot.
The Camera is used to detect the path lines and also the obstacles, by applying the image processing algorithm described in chapter 9. While the Laser Range Finder is used also to detect obstacles.
The three sensors are then fused into one source of information; The Cost Matrix, that Jo-Car2 depends basically on for its artificial intelligence and path planning. Look at the following diagram.
The Cost Matrix:
The Cost Matrix is a 7×7 matrix, whose values represent the risk value for each position on the camera image plane; i.e. it has large values where obstacles or path lines exist, and low values in safe positions.
Each sequence of matrix elements represents a path; which has its own cost (the sum of elements values), the path of lowest cost is the best path.
For thorough understanding of this method, we provide in the following lines a step by step explanation.
Step by Step Explanation:
** Read the full explaination in the attached document below.
Summary:
Jo-Car2 uses a path planning algorithm called cost matrix algorithm; it depends on fusing all sensors into one 7×7 matrix, then evaluating alternative paths on it by their costs.
The matrix is generated through six steps; 1 converting the original image from camera to binary image by image processing algorithm, 2 detecting and removing the noise, then 3 generating the matrix by averaging pixels, 4 fusing all sensors, after that 5 evaluating pre-defined paths, and finally 6 selecting the best path with the lowest cost.
Path Planning.pdf