Closed kctoayo88 closed 4 years ago
Hi @kctoayo88. The best way to ensure you understand how that works is probably by following along with one if the examples.
Q
is a list of tuples q
indicating that q[0] edges of length q[1] should be added. This choice was made to allow for easily trying different amounts of different-length edges. For example, if we set Q = [[4, 1], [2, 8]]
, this would attempt to grow 4 edges of length 1, and 2 edges of length 8.
Starting with the rrt_2d.py
example, we set the length of new edges in Q = np.array([(8, 4)])
, corresponding to trying to grow 8 edges of length 4 on each iteration. Next, the search is done in rrt.py
, where you can see the looping over different edge combinations within Q
. For each of the tuples, it calls new_and_near
, with an edge length from the tuple.
Hope this helps.
Hello @SZanlongo,
Thanks for your reply, it's really helpful to me. I could understand and handle it now.
Unfortunately, I got another problem when I run the rrt_star_bid_2d.py
program.
I always defined the obstacles, init and goal position by myself in this program.
However, the program got some error in some specific cases.
I guessed that the reason of error occurring is related to obstacles and init, goal setting.
Hope you could help me solve this.
Seems like you hit an edge case. I've pushed what should be a fix to develop. Let me know if that works.
@SZanlongo ,
Thank you for your helps. The change solve the problem perfectly and work like a charm.
Hello,
I would like to know how to adjust the length of extending step when the RRT is executing. I saw you wrote this line in the readme, but I can't find the parameter in programs.
Hope you can give me some tips, thanks!