vb.net read usb barcode scanner NEGOTIATING A MAZE in Software

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NEGOTIATING A MAZE
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FindPath: Dim Queue[35] QHead = 0 QTail = 0 Dim Visited[35,3] MConstant Visited,999999 Queue[QTail] = RobotNode //add to queue QTail = QTail+1 Visited[RobotNode,0] = 1 //visited Visited[RobotNode,1] = 0 //distance Visited[RobotNode,2] = -1 //predecessor while Visited[GoalNode,0]<>1 and QHead < QTail v = Queue[QHead] //deque QHead = QHead+1 for w = 0 to 34 if MapGraph[v,w] = 1 and Visited[w,0] <> 1 Visited[w,0]=1 Visited[w,1] = Visited[v,1]+1 Visited[w,2] = v Queue[QTail] = w //add to queue QTail = QTail+1 endif next wend if Visited[GoalNode,0] = 1 Found = true dim Stack[35] Sp = Visited[GoalNode,1] Stack[Sp] = GoalNode For FP_i = Sp-1 to 0 Stack[FP_i] = Visited[Stack[FP_i+1],2] next else Found = false endif Return //=============================================================
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FIGURE 14.10
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Shortest path- nding routine.
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go to its predecessor, then the predecessor s predecessor, and so on until we reach the start node. The path from the start node will be the reverse of this list, and it will be the shortest path. The proof of this assertion is a subject in computer science and is too complex to be considered here, but you will be able to see for yourself that it is a true assertion when you run the program. We wrote the new FindPath to use the same variables as before. In the routine of Fig. 14.9 a stack was used during the Depth-First search and the array Stack[ ] and its pointer Sp were set with the required path and its length as a result of the search. In this algorithm we do not use a stack and we end up creating a path list by reversing the travel from the goal node to the start node, visiting the predecessor of each node. This list should be called, for example, PathList. However this means we would have to change too many routines. Therefore we will maintain the same naming as before and use the array Stack[ ] to hold the path and Sp will be the length of that path, and both will be usable in SolveMaze as before along with Found. The routine also expects GoalNode and RobotNode to be set as before.
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14.5 Final Thoughts
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In this chapter the mazes were vertical and horizontal lines or tight corridors; this simpli ed the techniques for controlling the robot s movement. We concentrated more on the strategies for negotiating the maze. What if the lines were not straight north/ south-east/west lines What if the corridors were wide enough that the robot could not see all the exits at once with its infrared sensors What if the forks in the lines were not 90 turns, rather more or less, so instead of T(or ) junctions we have Y junctions, or N junctions, and so on These are problems that must be solved in many real-world situations. In the next chapter we will devise techniques for negotiating a home or of ce environment. You can think of these environments as corridor mazes, but the corridors are too wide and the robot has to avoid obstacles and nd the doorways. The techniques of this chapter have applications in many situations other than maze solving. The world is a maze. We humans are so adept at negotiating mazes that we don t even know when we are doing it. It is very satisfying to gain insight into our own thought processes. Trying to program a robot to do what we take for granted gives us an introspective view into our intelligence. The mapping and path-searching techniques we have introduced here are applicable to many situations with real-world signi cance such as: Airline scheduling Delivery truck routing Process management Communications networks GPS systems Cartography Arti cial intelligence (AI) Database systems As you can see, robotics is a very useful as well as entertaining pursuit.
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14.6 Summary
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In this chapter you have: Learned how to randomly generate line and corridor mazes. Explored various strategies for solving a maze. Seen how directed randomness can improve a totally random behavior. Seen how arrays can be used to store a history of actions, enabling the robot to learn from its past attempts. Learned to use arrays to generate a map for the robot to consult before trying to solve the maze.
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