How can I generalize <a href="http://harablog.wordpress.com/2011/09/07/jump-point-search/" rel="noreferrer">Jump Point Search</a> to a 3D search volume?
So far, I have defined pruning rules for a 3D cube involving each of the three movements- straight (0,0,1), first-order diagonal (0,1,1) and second-order (1,1,1).
What I'm mostly concerned about is the optimal turning points defined in the <a href="http://users.cecs.anu.edu.au/~dharabor/data/papers/harabor-grastien-aaai11.pdf" rel="noreferrer">paper</a>. I've been unable to ascertain exactly how they were derived, and therefore how to derive my own for three dimensions.
Any suggestions as to how this can be done?
So far, I have defined pruning rules for a 3D cube involving each of the three movements- straight (0,0,1), first-order diagonal (0,1,1) and second-order (1,1,1).
What I'm mostly concerned about is the optimal turning points defined in the <a href="http://users.cecs.anu.edu.au/~dharabor/data/papers/harabor-grastien-aaai11.pdf" rel="noreferrer">paper</a>. I've been unable to ascertain exactly how they were derived, and therefore how to derive my own for three dimensions.
Any suggestions as to how this can be done?