and then there's this awesome thing:<p><a href="https://anvaka.github.io/ngraph.path.demo/" rel="nofollow">https://anvaka.github.io/ngraph.path.demo/</a><p><a href="https://github.com/anvaka/ngraph.path" rel="nofollow">https://github.com/anvaka/ngraph.path</a>
If anyone has a tougher path planning problem and needs a better heuristic, I wrote an article a few years ago that might help: <a href="http://razorcode.net/articles/vehicle-path-planning.html" rel="nofollow">http://razorcode.net/articles/vehicle-path-planning.html</a>
This is a very useful tool and helped me understand Jump Point Search for a hobby project, which as it turns out is significantly faster than A* for certain use cases.<p>Are there any path finding algorithms for orthogonal grids optimized for frequent changes in the environment, particularly moving opponents in a game where you can't predict their next move with certainty? A* and friends do well at finding shortest paths between a start and a goal, but what if the obstacles change after you start along that path?
I was surprised at how much slower the Jump Point Search variants were than vanilla A-star. I thought it was meant to be an order-of-magnitude optimisation over A-star?<p>Also, any idea what the 'Trace' algorithm at the bottom is? It seems significantly faster/more efficient than the others but I can't find it in the github repo and the live version of the library is minified.<p>Edit: Ugh, how do I escape an asterisk? >.<
A faster way to find paths on grids <a href="http://mikolalysenko.github.io/l1-path-finder/www/" rel="nofollow">http://mikolalysenko.github.io/l1-path-finder/www/</a>