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Test - Relationship between Accuracy and Angle
Hi, This blog will cover the results gained from testing the accuracy of the robot successfully hitting the puck at varying angles for both sides. Below is a graph of the results. Relationship between successfully hitting the puck at varying angles for both table sides. The robot had the most success hitting the puck when it bounced off the right side of the table greater than the left side for all three categories. Each category had a sample size of 10. The performance of the robot decreases with increasing angle. However, as the speed was not taken into consideration when conducting this test, some of the results may be a result of the robot not having enough time to react. For both sides, the robot had the most success of hitting the puck at an angle less than 50 ° a s the puck bounced multiple times, reducing its velocity and allowing the robot enough time to react with 83% success rate at right side and 50% on the left side. A possible reason for the difference betwee...
Linear Regression - Bounce Trajectory 2
Hi, In the previous method, I described a method for handling bounce trajectories using Linear Regression. Below is an image showcasing another way to predict a bounce without swapping the axis and the need of creating new coordinates from the point that the slope intercepts the Y-axis. Code explained: 1. Calculate slope between atleast two points, as soon as the puck changes direction and is moving towards the ABB. 2. Using the slope, I predict the y position of the puck at the centre of the table. 3. If the prediction is greater than the table height,the error is calculated. 4. A new trajectory called Yc is created by reflecting the error from the end of the table. Video of two softbots playing with each other using two different methods Immanuel