[ompl-users] Re-using data from previous planning requests.

[Yoann S. <ys...@la...>]

Hi all,

We are working on the implementation of research work about dual-arm 
manipulation.Our goal is to develop in Moveit a new functionality able 
to execute complex closed kinematics chain motions in a 
cluttered environment. For now, we are able to plan motion with the RRT 
planner. However, the workspace is very complex, so that planning 
requests are almost impossible to solve in a short time (1-2s). 
Therefore, we want to take advantage of multi-query planners like PRM or 
SRS to compute offline a roadmap of the workspace in order to 
efficiently perform a motion request later.

In previous posts 
(https://sourceforge.net/p/ompl/mailman/message/32657097/, 
https://sourceforge.net/p/ompl/mailman/message/35074389/), Mark Moll 
explained that is was not possible to restart the planner using 
a previously constructed  roadmap without a lot of work in the code. 
However, he proposed experienced based planners.

I read the article "Experience-Based planning with Sparce Roadmap 
Spanners" and the LightningThunder demo. I'm thinking about using the 
Thunder framework inside Moveit, but I need to be sure if it will be in 
accordance to our requirements.
I have several question about the Thunder framework:
_ The exp-based planning is suited for robots with large number of 
invariant constraints. So, for our case, additional constraints 
generated by closed kinematic chains can be easily encoded in the 
experience database. This will lead to have a robust database. Am I right?
_ I see the size of the data base have a kind of theoretical limit. 
However, can I be sure that the database will have a size limit, even 
after months of running?
_ Our experimentation consists in planning motion for several objects to 
manipulate. During the experimentation the start and goal poses of the 
objects will be always the same for each object. The data base of 
Thunder grows at each time the planner from scratch (PFS) find a 
solution before the retreive-repair planner (RR). However, when the 
database contains the path, the PFS has a low probability to find a path 
first and maybe to improve the quality of the database in order to have 
more optimal path for future planning request. So, is it possible to 
force the PFS to generate trajectory in order to improve the database?
_ Last question, I see the experimentation in the article was perform 
with Moveit/OMPL. Is it possible to have more documentation about the 
implementation in Moveit?

Thanks in advance for your answers,
Regards,
Yoann.