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To incorporate obstacle navigation to the Board Motion Planning class, you first need to add a few

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sets of instance variables inside of an entered function, so we head over to the ADD function.

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Scroll down and then add the full set of instance variables that are basically containers, the stores,

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the previous angle to tune, distance to go, and the port iteration.

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By storing this previous information about these particular values, we now have the information of

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whether these particular values has changed from the previous iteration to the current migration.

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And if they haven't changed, then we can be sure that we need to perform obstacle navigation because

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our robot is stuck somewhere on the map.

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Then we head on over to the next instance we're able that are basically outrageous and these are great

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as basically keeps track of how many iterations has elapsed since one of these values has not changed.

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That includes the angle, the distance, the goal, and then we have the counter that tells how much

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time has elapsed since we have backpedalling.

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And then we are the last set of instance variable that are basically state variables.

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The first is the trigger backpedalling.

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That is for the case when we are stuck on a world and we need to backpedal to try to reach the next

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meaning goal.

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And then the next step is basically the trigger next point.

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If we cannot reach a particular angle, then we need to activate the stick on next point to try to reach

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the next winning goal because that is reachable and both of these initialize to false.

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Then we head on over to the go to go function where we need to add in the functionality to check the

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go to assert us.

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To do that, we need to add a new function before the go to go function.

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And that would be.

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The check go to Google status.

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This takes it to input arguments.

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The first is the angle to tone, and the second is a distance to what?

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Here.

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The first thing that we do is basically compute the change in the angle to turn.

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And this can be done by simply taking the current angle to turn and subtracting it with the previous

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angle to turn datastore inside the instance variable.

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This gives us the change in the angle to turn, and if the angle has not changed, it is large and it

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does not change for a long time and we haven't performed the back panel.

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Then what we need to do, we are to simply increment the angle, not change our data.

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So once the angle of change reaches 200, we aim to trigger the backpedaling because the angle has not

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changed for a significant amount of time, indicating that the robot is stuck on a ball and it needs

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to backpedal to try to reach the next minute goal.

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Otherwise, we simply reset the angle, not change, and set the previous angle to turn to the current

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angle to turn.

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Then we address the case for where we have the change in distance, and that can be computed by simply

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subtracting the distance to go and the previous distance to go.

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This gives us the change in the distance and the same way as we were doing for the angle.

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We check if the distance is large enough and not changing and the figure backpedaling has not been set.

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Then we increment the distance, not change to what?

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And if the this is not change is greater than 200, that arbitrator has reached 200.

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Basically, a long time has elapsed since the distance between the court and the winning goal has not

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changed.

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Then we triggered the backpedaling because the car is stuck on a ball again and it needs to backpedal

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to try to reach the next main goal.

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Otherwise, we set the distance, start change and set the previous distance to go to the current,

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the current distance to go.

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Then we address that last case that can occur, and that is basically check if the goal or the winning

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goal has not changed for the last duration, and that can be done by simply subtracting the previous

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fourth alteration with the current iteration.

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And this gives us the change in goal.

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And if the changing goal of the goal has not changed and the distance to go is basically less than 30,

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indicating that it is very relatively close to the car, then we implement the goal, not change outright.

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And if the goal not change is less is greater than 500.

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Basically a time has elapsed.

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Then we trigger the next point because we cannot reach the winning goal.

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And if the goal has not changed for a very long time, then we also trigger the next point because we

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cannot reach that particular goal.

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Otherwise we reset what these variables and then set the previous path over to the current path.

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And this way we have incorporated the check go to world status.

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So all we need to do is head on over to the go to goal function.

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We're computing the speed and angle printed out.

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We now check.

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Whether if served or Google not reflect.

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So if you haven't reached the final winning goal or basically have not reached the exit, then we can

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check by calling the self check.

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Go to go.

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This takes in doing what argument the first is angled to doing, and the second is a distance to go.

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So we check the go to goal status and if we have found a case where we need to perform obstacle navigation,

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either backpedalling or to the next point, then we need to address that particular case.

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So we need to define what should happen or how would backpedaling occur.

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So we scroll down where you are publishing the velocity before this, publishing the velocity that we

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updated, we need to perform the backpedaling definition.

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That is basically if the trigger backpedaling has been activated, what should we do?

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What we need to do is simply check if the seller backpedaling article is equal to zero, then we say

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that we need to go to the next point.

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This means that we need to load the next winning goal.

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And then we simply said the linear component of our willingness to object to negative, to indicate

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to our card that it needs to move in reverse.

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And then we set the angle to angle and then we increment the back pedaling our greater by one.

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So if this backpedaling goes on for a significant amount of time until just backpedaling after two 100,

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then we reset the trigger backpedaling with 72 falls, and we reset the backpedaling outright to zero.

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And the backpedaling has done, and the car can now reach the next winning goal.

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So once we have defined the case for the backpedaling, we need to define the case for what should happen

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when the trigger next point has been activated.

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So when the trigger next is activated, we don't need to reach the current winning goal.

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We can simply try to reach the next winning goal.

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So this condition where we are simply checking if the company goal is within the reach and we have reached

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that particular meaning goal can simply be added with.

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If self-taught.

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Trigger point has been activated or not.

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So the trigger next point is activated.

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Then we don't need to use that current meaning we can simply activate the next memory go so we can simply

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incorporate the next condition or simply incorporate the condition to trigger the next point before

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encountering that particular condition or calling that political function.

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We need to define the function that triggers the next point so we can scroll up and for the go to go

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function and adding that functionality.

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That is to get suitable.

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Next point.

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So once we have defined this functionality of getting the next suitable next point for the kids, we

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are stuck behind the wheel.

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You can now call this functionality inside the case where we were approaching the next meaning goal.

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So we defined that functionality of where if we have set the trigger next point activated and if we

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were backpedaling indicating that we are stuck on a goal and we cannot reach that particular meaning,

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then we need to get the suitable next point because the next closest next point might not be accessible

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anymore.

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So we need to get the suitable next point by calling in the function, by providing the current board

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location and the current path that we have computed in the planning stage.

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So this means that we have completely incorporated the obstacle navigation to our board motion planner

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class and hopefully our missile board will be able to solve the more complex means to.

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All right, guys, it's the moment of truth.

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Will our Minnesota board be able to solve the more complex maze, too?

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After adding an obstacle navigation feature to aid in the analysis in the view of the missile robot

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is seen in front of it.

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This will give us the on site information of what kind of obstacles our Mr. Robot is navigating around.

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What we can be added by just getting a new subscriber to subscribe to the bot camera slash image roll

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and passes on this information to the process data more function than inside the process data about

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function.

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We take that data and convert it to save it to image format and save it inside the body variable.

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Then we head on over to the misfolding function where we simply overlay our bot view on the left hand

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side of the frame display to have both the top view and the bar view on the same screen.

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So we head on over to the terminal, build the project.

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And then launch the maze to simulation and once the mate to simulation starts up.

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It will run the Mets solver.

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So the first output that we get is basically the output of the localization state, that is the MES

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occupancy grid shown on top of the screen.

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Then you press spacebar and we get the final output.

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That is the maze being solved live by our main robot.

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And on the left hand side of the screen we have the board view being displayed on on in front of us

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and we can also display on the right hand side of the screen the maze reference image that basically

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displays the maze in the form reference there that is basically oriented having the maze entry right

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on top.

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Then the shortest path is this being displayed on this maze reference image in the form of a color line.

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And we also have the board location being displayed on the red circle, on the maze reference image.

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And the green circle represents the many goals that have already been completed and the audience represents

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the winning goal that the board is currently trying to navigate.

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So you can see the board is trying to solve this particular maze and it is orienting itself to the next

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mini goal that is very far from it and this is why it is moving very fast.

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So as it approaches the first of its major obstacle, that is basically a very narrow corridor and this

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is a position where it got stuck earlier and let's see if it is able to navigate this time around after

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adding an obstacle navigation feature.

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So it is approaching the mini goal and this mini Google approach more and more closer to the wall.

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So finally, you will see that one of these mini goals is right on top of the wall, and this is why

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our may solve board might get stuck at this position.

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So you can see the board is turning towards the wall in the left hand side of the screen where you can

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see the board view is just seeing the wall at this moment.

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And if you look at our simulation, you can see the car is stuck in this particular location.

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And it is stuck right over here and it is trying to approach that.

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You go.

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So let's see if one of our check what Google status is being triggered and the car navigates around

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this condition, but it is stuck behind the wheel.

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So you can see that a distance of this particular goal is not changing and it is going from 0 to 200.

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And if it reaches a 200 counter, then it will simply backpedal and then try to reach the next minute

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goal.

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Let's see if it does that.

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So if it reaches the 200 and you can see the backpedaling starts and the car back pedals until the backpedaling

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counter reaches 100.

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So it backpedaled, moves in reverse with the same angle that was moving, and then tries to reach the

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next minute goal.

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So at this moment it is approaching backpedaling for 200 counter and it is backpedaling and then it

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will try to reach the next minute goal.

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So backpedaling finishes and then it will start approaching its next minute goal.

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Well, you can see it is approaching the next minute goal right now.

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So if I display them, live me.

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Is that what was happening?

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So you can see that it is approaching the next minute goal, which is in.

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In reverse.

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You can see it is making a 180 degree turn to try to approach that eagle.

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And you can also observe it on the left hand side.

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But you try to.

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Hurry, hurry, hurry, hurry, hurry.

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So at this moment, it is approaching another very difficult terrain, what it means to navigate around

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irregular objects, shark dunes, and not get stuck in this location.

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And even if it gets stuck, it needs to get itself free and try to reach the end of this particular

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maze.

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So it is stuck at this particular location.

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And if you what condition is being triggered at this moment?

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You can see that the car is stuck at this location and angle is being changing because the car is actually

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moving, but its distance is not changing for its incrementing and the goal is also not changing.

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So this counter is also going from 0 to 40 at this moment.

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So the mini Goulds are very close to the car at this moment and this is why they are being triggered.

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And then we try to approach the next to go to.

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So at this moment, you can see the car is stuck on this very tight turn and it's trying to approach

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to next to go.

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So it reorients itself and then tries to approach the next minute go.

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But turning towards that particular winning goal.

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So it is making a 360 degree turn to orient yourself in the direction of that winning goal.

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So you can see on the left hand side the guys approaching the next many goals.

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In our screen.

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Oops.

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She?

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She?

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I.

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Hurry, hurry, hurry, hurry, hurry, hurry, hurry, hurry, hurry, hurry, hurry, hurry, hurry,

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hurry, hurry.

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She?

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So this is the last major obstacle with our God.

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We find much difficulty in trying to navigate around.

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The problem is that in this particular ball, the winning goal are very close to the ball and it is

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a particular card that is on the outside of its structure.

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Get stuck on this particular ball.

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So let's see which condition gets triggered first.

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So whether the goal has not changed for 200 iterations or the distance is not changed for that particular

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goal, but 200 iterations of the angle has not changed.

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So you can see the goal was not changed for 200 iterations and then it tries to reach the next winning

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goal.

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So the next trigger next point is being activated and it tries to meet the next minute goal.

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And on the right hand side of the screen, you can simply view of the car where the car is at its point

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in the shortest part as well.

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So it is approaching the shortest path to go and it is navigating around the shortest path, quite remarkably.

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I might say.

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So it is reorienting itself and then approaching the in goal.

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And then you can see its position on the right hand side of the screen and you can see on the left hand

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side of the screen, the board view is viewing the miss exit from this far away and it can see the goal

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in front of it.

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Right.

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Hi.

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Hi.

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Hurry.

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Hurry, hurry, hurry, hurry.

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Hurry.

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Hurry, hurry, hurry, hurry, hurry.

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She?

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I.

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Hurry, hurry, hurry, hurry.

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So you can see after the first few hurdles, everything else was a walk in the park for me and store

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bought.

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So it will just reach all those many goals and then approach the final few, many goals.

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And you can see at this moment the total many goals that the car had reached for 299 and it has already

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completed a total of eight many goals.

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So it is approaching the final few sets of many goals to reach the men's exit.

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And let's see if it does their job.

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Hurry, hurry, hurry.

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So it is approaching the men's exit and can see them is active right in front of it is orienting itself

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towards the exit right now.

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And you can see it looks a far into the void.

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So it is approaching the final minute goal and you can see.

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Hurry, hurry, hurry, hurry.

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And voila, it is reached the final minute goal.

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So indeed the obstacle navigation feature that we incorporated to our main solving what has made it

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solve the more complex me as to and made our may solver bought a much more reliable option for solving

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mazes.

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So that's it for today.

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Then, then.

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But.