vb.net read usb barcode scanner FIGURE 14.9 in Software

Maker QR-Code in Software FIGURE 14.9

FIGURE 14.9
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Negotiating a mapped maze.
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//Get Node Nearest to Robot rGPS Tx,Ty gosub CalcNodeNumber RobotNode = NodeNumber if FirstTime then gosub BuildMapGraph //Build Graph if RobotNode <> GoalNode gosub FindPath //Search Graph else Found = True Sp = 0 endif if Found if Sp > 0 For I = 0 to Sp //for each node on path Dnn = Stack[I,0] //destination node number x = (Dnn#7 + 1)*100 y = (Dnn/7 + 1)*100 gosub GotoPoint next else x = (RobotNode#7+1)*100 y = (RobotNode/7+1)*100 gosub GotoPoint endif // go to the actual goal after reaching its // nearest junction x = Gx y = Gy if not rBumper() then gosub GotoPoint endif Return //============================================================= CalcNodeNumber: //Input----Tx and Ty //Output---NodeNumber Tj=Tx/100+Round(frac(Tx/100.0)) if Tj = 0 then Tj = 1 if Tj > 7 then Tj = 7 Ti=Ty/100+Round(frac(Ty/100.0)) if Ti = 0 then Ti = 1 if Ti > 5 then Ti = 5 NodeNumber = (Ti-1)*7+Tj-1 //Node Number NodeX = Tj*100 NodeY = Ti*100 dX = NodeX-Tx dY = NodeY-Ty if abs(dX) > 35 ReadPixel NodeX-sign(dX)*12,NodeY,Pc if Pc <> LnClr then NodeNumber = NodeNumber-sign(dX) endif if abs(dY) > 35 ReadPixel NodeX,NodeY-sign(dY)*12,Pc if Pc <> LnClr then NodeNumber = NodeNumber-sign(dY)*7 endif Return //============================================================= FindPath: Dim Stack[35,2]
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FIGURE 14.9
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NEGOTIATING A MAZE
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Dim Visited[50] MConstant Visited,0 //push on the stack Sp = 0 Stack[Sp,0] = RobotNode Stack[Sp,1] = 0 Found = false while true i = Stack[Sp,0] j = Stack[Sp,1] Visited[i] = 1 if MapGraph[i,j] = 1 and not Visited[j] Sp = Sp+1 //push on the stack Stack[Sp,0] = j Stack[Sp,1] = 0 If j = GoalNode //Goal found Found = true Break endif else while true Stack[Sp,1] = Stack[Sp,1]+1 if Stack[Sp,1] > 34 Sp = Sp-1 //pop the stack if Sp >= 0 then continue endif break wend If Sp < 0 then break //no more nodes on the stack endif wend Return //============================================================= GotoPoint: dx = x-rGpsX() dy = y-rGpsY() if dx=0 AND dy = 0 then return Theta = PolarA(dx,dy)*180/pi()+90-rCompass() if Theta > 180 then Theta = Theta-360 if Theta < -180 Then Theta = Theta+360 rTurn Theta Distance = Round(PolarR(dx,dy)) For GP_I = 1 to Distance if rBumper() & 4 then break rForward 1 next Return
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FIGURE 14.9
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If a path is found the routine uses the information in the created Stack[ ] array to follow the path. The goal may not be exactly over a junction and thus the routine causes the robot to make the extra moves necessary to reach the goal from the closest junction. If the robot s junction and goal s junction are the same then there is no need to call the FindPath routine and we bypass following a path, but we do forward the robot toward the nearest junction and then to the goal which may be a little further than the actual junction.
COMPLEX COMPOUND BEHAVIORS
Moving the robot to any point is achieved using the GotoPoint subroutine. You have encountered this routine in Chap. 4 and other chapters; it has not been modi ed. 14.4.5 FINDING A PATH The routine FindPath analyzes the array MapGraph[ ] to search for a path from the RobotNode to the GoalNode. Upon returning from the routine, if a path exists, the array Stack[ ] will hold the path. Also the variable Sp will hold the length of the path and the variable Found will be true if a path was found (false otherwise). The algorithm of this routine does not nd the shortest path; rather it nds a path. This path may not be the optimal path or even the most intelligent one. However the algorithm is simple to follow, so we examine it rst. In computer science this principle is called a depth- rst search. There are other ways to traverse a graph (like a breadth- rst search), but depth- rst is easier to understand. The idea is to begin at the start node and look in the graph array for the rst node that has a connection, then search that node for connecting nodes and so on until the end of nodes or the goal node is reached. If the goal node is not yet reached, we go back one node up to search for the next connected node and so on. This algorithm will nd a path if one exists. However, you can see that the path found may not be optimal due to the method of depth- rst searching. If breadth- rst searching (with some more logic) is used we would be able to nd the optimal path (see next section). 14.4.6 THE OPTIMAL PATH This algorithm simulates a real-life situation where a GPS system holds a map of the city and knowing where you are and your destination it tells you the shortest path through the city to your destination. Many of these GPS devices can also calculate the fastest path and can reroute you if there is a traf c accident. If you think of our maze as a plan view of a city with north-south and east-west roads then our robot looks like it is traveling through the city. Figure 14.10 shows a replacement for the FindPath subroutine in Fig. 14.9. This new routine implements a breadth- rst search through the graph. This method will nd the shortest path. The strategy is to look at the start node and then look at all the nodes that have a direct connection to it. These nodes are put in a queue to be considered in the same manner as the start node. This process is repeated until we reach the last reachable node of the last reachable node and so on. While doing this we build a list of immediate predecessors for all the nodes. Think of a queue as a line of people waiting to be served by a teller operator. The rst person at the head of the queue will be processed, while a new person joins the queue at its tail. A queue is a rst-in- rst-out structure, while a stack is a last-in- rst-out structure. You can see how the different structures enable the breadth- rst and the depth- rst searches of the graph. At the end of the traversal of the graph, we have a list showing what is the immediate predecessor for each node. A predecessor to a node is the node that leads to the node with only one hop while going from the start node to the goal node. You can see how this list can be useful in building a shortest path. All that is needed is to start at the goal node,
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