ece4580:module_autonav
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| ece4580:module_autonav [2017/04/12 19:38] – [Week #4: Consistent Operation] pvela | ece4580:module_autonav [2024/08/20 21:38] (current) – external edit 127.0.0.1 | ||
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| You may have found the gap method to sometimes jitter, sometimes crash, sometimes just do slightly the wrong thing. One reason was using too small of a threshold for the distance so that the robot would react to late, while at the same time having a miserable view-able area for maneuvering. Making the gap threshold distance larger helps with that, but still may exhibit some of the behavior above (just less frequent or less drastic). The persistence of those behaviors is a function of the noise in the sensor, the small field of view of the camera, and the lack of memory regarding parts of the world that leave the field of view. Here, we want to incorporate some kind of memory into the algorithm for smoother behavior, and better operation when navigating through a gap. | You may have found the gap method to sometimes jitter, sometimes crash, sometimes just do slightly the wrong thing. One reason was using too small of a threshold for the distance so that the robot would react to late, while at the same time having a miserable view-able area for maneuvering. Making the gap threshold distance larger helps with that, but still may exhibit some of the behavior above (just less frequent or less drastic). The persistence of those behaviors is a function of the noise in the sensor, the small field of view of the camera, and the lack of memory regarding parts of the world that leave the field of view. Here, we want to incorporate some kind of memory into the algorithm for smoother behavior, and better operation when navigating through a gap. | ||
| - | Create a state machine for the system as it navigates the gaps. There will be a gap scan and go to goal state, a go through gap state. Roughly, we have the following: | + | Create a state machine for the system as it navigates the gaps. There will be a gap scan and go to goal behavior, a go through gap behavior. They may map to more than 2 states. Roughly, we have the following: |
| - While not gap is perceived, go to the goal $p_{goal}$ while continually evaluating for a gap. | - While not gap is perceived, go to the goal $p_{goal}$ while continually evaluating for a gap. | ||
| - When a gap is perceived, instantiate a new goal state being the gap center location, $p_{gap}$, then drive to that goal state. | - When a gap is perceived, instantiate a new goal state being the gap center location, $p_{gap}$, then drive to that goal state. | ||
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| - One way to drive through the gap is to set up a secondary goal position that is beyond the goal along the normal $\vec n = (n_1, n_2)$ to the line by a distance $2 d_\tau$, as in $p_{past} = p_{gap} + 2 d_\tau \vec n$. When you get to the transition line (from negative to positive), then switch to this new goal and drive towards it until going a distance of $d_\tau$ past the transition line. Switch back to the go to goal state. | - One way to drive through the gap is to set up a secondary goal position that is beyond the goal along the normal $\vec n = (n_1, n_2)$ to the line by a distance $2 d_\tau$, as in $p_{past} = p_{gap} + 2 d_\tau \vec n$. When you get to the transition line (from negative to positive), then switch to this new goal and drive towards it until going a distance of $d_\tau$ past the transition line. Switch back to the go to goal state. | ||
| - | Hopefully that makes sense. | + | Hopefully that makes sense. |
| + | You will have to implement a closed loop control scheme, like discussed in the [[Turtlebot: | ||
| + | Use your odometry to estimate where you are and where the intermediate goal positions are. Some of the above may need to be properly integrated with the " | ||
| ===== Module #3: Dynamic Window Approach ===== | ===== Module #3: Dynamic Window Approach ===== | ||
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ece4580/module_autonav.1492040288.txt.gz · Last modified: 2024/08/20 21:38 (external edit)