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The subroutine TestSensors in the listing in Fig. 9.3 (Lines 52 59) shows how this is achieved. Notice the use of the | (bOR) operator to create the nal number. Also notice how the for-loop creates the ve sensors at the correct angle. The subroutine will set up the variable Sensors to have the required value. So in place of using rSense() we call the subroutine TestSensors. Then we use the same kind of checking on the variable Sensors as described in the previous section. To allow the sensors to work correctly we will make the table white and the oor gray. But we will need to issue an rInvisible Gray command as you will see on Line 12. This is due to the fact that the robot will think that the gray color is an obstacle (see Sec. C.9 for a detailed discussion on the Robot color arrangement). If the gray color is set as an invisible color the robot will not consider it an obstacle and will be able to move into that area (dropping off the table). This way we would not be cheating in the simulation. Review the program in Fig. 9.3 and run it before proceeding with the following discussion. The program s main routine sets up the environment (SetTable), locates the robot, sets the required color parameters, and then calls the TableRoam subroutine (Lines 17 45) which will be executed continuously due to the while-loop. The TableRoam subroutine is not very different from the Con ne subroutine in Fig. 9.2. The action is very similar except for using Sensors [not rSense()] and the added obstacle avoidance. The elseif-statements (Lines 32 and 36) re ect the fact that there

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A delta CRL lists only incremental changes that have occurred since the preceding CRL. In this way, delta CRLs provide a way to significantly improve processing time for applications that store revocation information in a format other than the CRL structure. With this approach, such applications can add new changes to their local database while ignoring unchanged information already stored there. After an initial full CRL (base CRL) posting, an accurate list of revoked certificates is maintained through delta CRLs. As a result, delta CRLs can be issued much more often than can base CRLs. CAs use the Delta CRL Indicator extension to indicate the use of delta CRLs. In addition, a special value, the Remove from CRL value, can be used in the Reason Code extension to specify that an entry in the base CRL may now be removed. An entry might be removed because certificate validity has expired or the certificate is no longer suspended.

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n all the chapters so far, we have developed algorithms to make our robot react to its environment while achieving speci c tasks. The approach used was to give the robot speci ed parameters for how to react to speci c sensory inputs. On many occasions the robot was given randomness so it could vary its behavior suf ciently to avoid getting stuck in unanticipated dead-end situations. When the robot became stuck in a situation where it could not continue doing its work, randomness eventually created the right combination of parameters to enable the robot to escape. However, the robot had no way of learning from its experience. If the robot encountered a similar situation at a later time it had no means to recall and reapply the same parameters. If we give the robot a memory and the ability to record the parameters that made it succeed as well as the ones that led to it becoming stuck, the robot should be able to adapt its future behavior so that it can avoid the bad situations and favor the good ones. An example of this kind of behavior was seen in Chap. 14, where the robot learned from the rst pass through the corridor maze, how to negotiate the maze perfectly the second time. To achieve this we used memory (array) to save past behavior in order to in uence future behavior.

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