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D star light

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JesseWelling:
Found this. not quite sure what the 'light' is yet.

enjoy

Here is a make file btw:

--- Quote ---CFLAGS          = -O3
LFLAGS          = -O3
CC              = gcc

OBJ             = heap.o maze.o dstarlite.o
dstarlite:      $(OBJ)
                $(CC) $(LFLAGS) -o dstarlite $(OBJ)

.cc.o:
                $(CC) $(CFLAGS) -c $<

clean:
                rm *.o

--- End quote ---

rgcustodio:
Kewl. D* Lite is an incremental heuristic search method. Lookie here:
http://idm-lab.org/bib/abstracts/papers/aaai02b.pdf

Quite usable with maze solving robots.

Admin:
hmmm without reading through all the code carefully . . . what sensors does it expect you to use? i dont see a way to preprogram a maze in to the solver . . .

sdk32285:

--- Quote ---Found this. not quite sure what the 'light' is yet.
--- End quote ---
The D* light refers to being computationally light and more efficient than D*


--- Quote ---hmmm without reading through all the code carefully . . . what sensors does it expect you to use?
--- End quote ---

It appears to be indifferent to sensors it is just looking for an obstacle map. Interesting... you can feed it "fake" obstacles and see how it reacts.



JesseWelling:
The beauty is that you need *no* map to start with. Just a way to sense your coordinates (GPS or dead reckoning, or any combination of the two should work well.) and obstacles that can not be traversed.
 
In a fairly sparse environment a LOS Bug algorithm would work slightly faster but D* with localization is pretty good for teaching your robot how to go from any where in the house, to your refrigerator and back, and never have an exactly accurate map. And the D* algorithm won't have any problems with local minimums (known as wells) like a P-field might. Although it does need tweaking to make it work in an inexact environment, so I'm trying reading about a subsumption architecture that combines D* with a P-fieldesque approach for object avoidance.

slow going....ug....

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