Figure 1
- Refer to Figure 1 for the following questions:
- Convolve each of the following with the source image in Figure 1.
- [1 -1]
- [1 1 1 -1 -1 -1]
input: [1 2 3 4 5 6]
filter: [0 1]
output: [0 2 3 4 5 6] - In a one dimensional environment what does the convolution mask [1, -1] do? (This convolution mask is different from the one in class. We know this, but if you understand the process of convolution this will not be a problem.)
- How is the effect of the mask [1, 1, 1, -1, -1, -1] different?
- If you wished to detect only the indicated edge, how would you post process the output of the convolution of the original signal with the [1, 1, 1, -1, -1, -1] mask?
- Convolve each of the following with the source image in Figure 1.
- Convolve the following image with the given filters.
-
image:
- [ 5 5 5 9 9 9 8 7 6 5 -5 0 0 0 0 ]
- [ -1 1 ]
- [ 1 -1 ]
- [ 1 0 0 1 ]
- [ 1 0 0 -1 ]
filters: - [ 5 5 5 9 9 9 8 7 6 5 -5 0 0 0 0 ]
- For the closed loop response in Figure 2, draw the resulting response if the proportional gain were increased.
Describe in a few words what happens.
Figure 2 - For the sample environment in Figure 3, draw the resulting path from the
wavefront planner (in the continuous domain, ie. not with pixels) for the mobile robot using the L1 metric.
Remember to take into account the configuration space of the robot.
Print out (or recreate with a computer) multiple copies of the figure, and submit the following:
- One copy showing the configuration space.
- One copy showing the wavefronts (you don't have to draw every single one, just a representative amount)
- One copy showing the resulting path(s).
Figure 3 - For the configuration space generated from the previous problem, implement either the Bug1 or Bug2 planner to trace a path from start to goal.