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## SummarySample output from A* (grid below):This is a sample of the output generated by A*, to show how the algorithm analyzes its choices and determines the best route for the robot. In this example, the algorithm shows the robot in the orange square. The robot is trying to move to the yellow star in the patient’s room. Each square is given a value that is calculated based upon two factors: distance from the destination and difficulty from the originating square.Sample square: Originating square is colored orange        Surrounding squares: Blue number in the bottom left of each square is the difficulty/distance from the originating square. Green number in the bottom right of each square is the distance from the destination. Purple underlined number is the sum of the two numbers, and the square's score. Winning square The squares above and to the left of the originating square have a score of 40. The square diagonally to the left has a score of 34. The lowest score is used, so the square diagonally to the left is the next square in the path. The winning squares are colored red.Flowcharts:Click here to view map analyzer flowcharts for my accuracy/efficiency balancing algorithm.Click here to view path creator flowcharts for my accuracy/efficiency balancing algorithm.Robot Turning Variation Testing and Calculations:Trial data was collected from running an actual robot through the same course twenty times to determine the amount of variance that occurs after a turn. The same robot was used with a power line connected to the robot to avoid varying results due to low battery power. Robot test: Forward 60 cm -> turn 90 degrees -> forward 150 cmVariance measurement: distance of the ending position of the robot away from the expected position, measured in cm.Data:Trial: Distance (cm)1         6.22         15.13         14.14         24.85         -20.16         0.17         -9.88         -3.69         1.210         2.111         - 4.412         -9.513         -5.514         -6.515         -716         -5.617         -4.118         4.319         3.520         -1.3 1 Standard Deviation = 9.9 cm 1.5 Standard Deviation = 14.9 cm 90% of expected values are found within 1.645 standard deviation units (+/-) from the meanCalculation of turning variance in degrees: Tan (Deg. Variance) = 14.9 cm / 150 cm Deg. Variance = ArcTan (14.9 / 150) Deg. Variance = 5.67 degrees(Click for larger image)(Click for larger image) AI Simulation Variance Measurements:Game AI: 7 path segments, 6 turns Total length: 15.907 feet Variation of ending point from destination: 4.875 feetGame AI: 7 path segments, 6 turnsTotal length: 15.907 feet Variation of ending point from destination: 4.875 feetMy Algorithm: 6 path segments, 5 turns Total length: 30.064 feet Variation of ending point from destination: 3.125 feet

 Data
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