I am using this metrics to prove a C code and I don't understand what the second metric means. What is non-cyclic remark paths? Can someone give me an example how to use it?
Related
I've got the following set of data :
As you can see, I've got two plateau at 1987 and 3757. The problem is I can't find a good algorithm in order to extract them.
I've already tried this algo, without any success.
Any help will be welcomed.
Regards
I am a newbie. Could you please explain what would happen if a control flow graph consists of multiple start and/or stop nodes?
I searched for the answer but was not able to find a proper one
Thank You in advance
I am looking for a simple layman's term explanation or a reference to read on how do programs like Apache Airflow or Luigi determine the shortest path to complete a certain task and make it possible to parallelize it? And how does that, if any, relate to graph theory?
First post so bear with me.
I have hacked together an module for Oscommerce that figures the delivery cost for a pizza delivery.
I used the google distance matrix and got it working only to discover that it picks the fastest route and not the shortest. and there was no option to tell it to pick the shortest.
Oh well.. I poked arround and found that i could use the direction api instead
http://maps.googleapis.com/maps/api/directions/xml?origin=Svendborgvej%20323,%205260%20Odense%20S,%20Denmark&destination=Albanivej%2027,%205792%20%C3%85rslev&alternatives=true
now i have 2 routes in that XML and i need
pick the shortest one
extract the distance from it
my code so far: http://pastebin.com/t3KY5Qzn
i'm not a very good programmer and a bit stuck here... anyone that could help me out?
i used an online xml viewer to get a more human readable format and i can see the 2 blocks with 1 route in it and also the total distance in each..
its a simple as: echo min($result)
from there its simple to mod my module so it picks the shortest route of those google might give
I am writing code for Rapidly exploring trees for robotic arm movement. I have two doubts
i) what is the distance metric that I have to use to find the nearest node in the graph? If it is euclidean distance,how do I calculate it because there are two links in each arm configuration of the robot and I have no idea how to find the euclidean distance in that case.
How do I find the distance between ADE and ABC if ABC is the nearest config to ADE in the tree?
ii) How do I generate a random config towards the goal because my random configs never seem to reach goal even after 5000 iterations.
Thanks in advance.
Distance Metrics for the Two Revolute-Joint Arm
RRT is pretty robust to the (pseudo-) metric that you choose, but the quality of the trees (and consequently the paths) will be influenced if you've got something that isn't particularly good. To get good performance overall, the metric function is supposed to be fast, so I'd definitely try simpler things before you move onto something more complex.
In the case of robot arms a number of metrics are possible. Perhaps the simplest is simply to use the Euclidean distance between the end effector in two configurations. You'll almost certainly have to have this working already if you're testing the planning algorithm.
If you've got a full dynamics model of the system, then it is likely that other metrics based on the energy required to move the arm from one configuration to another will perform better.
Other metrics based on the (joint local) angle swept out at the joints, which can be derived from evaluating a path from an inverse kinematics solver may be acceptable - but I haven't tried this in practice. This may also be useful technique to know about if you need to implement your connect-configurations function.
Improving Convergence
Once you've got your metric function working correctly, RRT should just work. However, in practice, you'll almost always need to oversample near the goal configuration to encourage the algorithm to exploit the work done in the rest of the tree building stage. Most commonly, you do this by sampling the goal configuration state with about 5% probability.