- Use your favorite pseudo-random number generator to create a
sequence of 20 random configurations. List them below.
Generated usingSolution:One possible answer:
( 9.66, 4.41)
( 0.07, 9.11)
( 9.39, 5.82)
( 6.72, 0.84)
( 7.66, 2.37)
( 0.31, 7.89)
( 3.46, 6.23)
( 6.16, 1.49)
( 1.83, 1.14)
( 0.15, 4.87)
( 9.65, 0.65)
( 5.41, 4.66)
( 6.01, 0.89)
( 5.79, 2.70)
( 5.56, 6.45)
( 4.81, 3.55)
( 2.49, 9.34)
( 4.53, 5.30)
( 0.19, 5.08)
( 0.06, 1.44)Python's.random.randomfunction - Show the operation of the PRM algorithm with these samples, using
2 for the connection distance. What calls are made to the collision
checker? What edges are included in the graph?
Solution:Answers will depend on the specific random samples you used. In general, you should get one point collision check for each of the samples, and one segment collision check for each pair of samples that are closer than 2 units from each other.
- Did the algorithm solve the motion planning problem? If not, why not?
Solution:It's very likely that the answer will be No. To get a solution, you would almost certainly need to increase the number of samples. Increasing the connection distance may help as well.