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FIGURE 14.3
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Solving a line maze.
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NEGOTIATING A MAZE
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14.2 A Directed Random Solution
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If the robot were a mouse and the nal goal a block of cheese then we would expect that the mouse would be able to smell the cheese and tend to take turns that are more toward the direction of the cheese. This may enable the mouse to make luckier turns that lead to the goal faster or, depending on the maze, it may cause problems. The above strategy could get the mouse stuck in a dead-ended section of the maze because it is favoring a direction toward the cheese that has no path to the goal. In practice this simply means that the mouse should generally move toward the smell of the cheese, but it must occasionally make some random choices to prevent getting stuck in a deadend that faces the cheese. We can give our robot the same strategy. We will give the robot the ability to look for the goal when it is trying to decide how to turn at a junction. Its decision of how to turn will be in uenced by the relative direction of the goal and the possible turns that can be made. The subroutine MakeATurn in Fig. 14.4 is a replacement for the one in Fig. 14.1. This new routine implements the logic discussed above. Replace the routine and run
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01 MakeATurn: 02 for MT_i = 1 to 20 03 if not (rBumper()&4) 04 rForward 1 05 else 06 m=0 07 return 08 endif 09 next 10 m = 90 11 if rSense() <> 0 12 Prcnt = 5000 13 for MT_i = -45 to 45 14 if rLook(MT_i) = GoalClr 15 Prcnt = 500 16 break 17 endif 18 next 19 if random(10000) > Prcnt 20 m = 0 21 return 22 endif 23 endif 24 if S = 7 25 Prcnt = 5000 26 for MT_i = -135 to -45 27 if rLook(MT_i) = GoalClr 28 m = -90 29 Prcnt = 500 30 break 31 endif 32 next 33 if random(10000) < Prcnt then m = -m 34 elseif S = 6 35 m = -m 36 endif 37 Return
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FIGURE 14.4
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A directed random solution.
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the program and notice how the robot behaves. There is a de nite improvement in its ability to seek the goal. The robot does appear to be a lot more intelligent most of the time. The logic is that we look in the direction of the turn before deciding whether to turn or not. If the goal is in that direction we favor the turn 95 percent of the time. If the goal is not in that direction then we make the turn only 50 percent of the time. The 5 percent randomization prevents the robot from getting stuck in a dead-end that faces the goal. We only take the goal into consideration when trying to decide to turn, no other time. Additionally, we only look for the goal 45 from the direction of the turn. This means that the goal will not in uence the robot s decisions to turn when it is behind the robot, which prevents the robot from getting stuck. Lines 1 to 10 are exactly as before. Lines 13 to 18 look for the goal ahead of the robot ( 45 ). Notice that this version of the rLook() function is given an angle relative to the robot s heading, otherwise it is the same as you have seen in previous chapters. Read Sec. C.9 for details on this function. If the goal is seen then the robot continues ahead (no turn), but Lines 19 to 22 force a turn anyway 5 percent of the time due to Line 14. If the goal is not seen then the decision to continue ahead or turn is made on a 50 percent basis by Line 12. In Lines 24 to 33 we decide whether to make a left or right turn on a T or junction. Lines 26 to 32 look for the goal on the left. If it is found then a left turn is made, but Line 33 forces a right turn 5 percent of the time due to Line 29. Notice that if the goal is not there then 50 percent of the time the turn is changed from right to left due to Line 25. If the junction is of type (3) or (5) (see Fig. 14.2) then we have to make a turn regardless of the goal. If it is a left we make a left turn, otherwise a right turn is made by default and no further action is required. Notice that the randomness given to the robot to stop it from getting stuck can also cause it to make a wrong turn (5 percent of the time). Also since the randomness is 50 percent if the goal is not a consideration, the algorithm will perform no worse than the fully random one in Fig. 14.1. Run the program and try out different mazes and different combinations of robot and goal positions. Can you think of ways to improve the algorithm further We will explore one improvement in the next section.
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