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For game maker you can use this tutorial


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The issue was that, the way I computed the neighbours was based on a normalized direction vector. But of course, those don't work for diagonals. Except, that they kinda did, in my case, but only in the positive directions. Because when I ask the A* grid to "getNode(Vector3f location)", it does this to the input vector: location.x -= location.x % nodeSize ...


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this line: if (q.movementCost < successor.movementCost + g) should look like: if (q.movementCost + g < succesor.movementCost) also remember that function getCost() should return heuristic + movementCost.


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With r being the circular area view radius; Make a mesh that covers the polygon where the nodes are at most 2r apart and connect the nodes with neighbours. Based on the distance and wind/water current between each node, add a difficulty value to progress through the arcs; arcs are bi-directional and don't have the same value (for instance for node A to B ...


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Here is my unity path finding using waypoints (nodes linked each others), recursion and linq: ... using System.Collections; using System.Collections.Generic; using System.Linq; ... public List<Waypoint> aStar( Waypoint goal,Waypoint nx, List<Waypoint> visited){ if( visited.Contains(nx)){ return null; } else { ...


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The Fast Marching method for solving the *Eikonal Equation is in fact based on Dijkstra's algorithm. It has O(N log(N)) performance, which is fine for small grids but can be improved on for larger grids by employing either the Fast Iterative or Fast Sweeping methods. As noted in your link: More importantly, the Fast Marching Method uses a binary heap, ...


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Pathfinding algorithms like A* can deal with inertia (or any other dimension you can throw at it) just fine. The key is to treat them as an additional dimension, and create a higher-dimensional search graph to search in. To keep things simple, let's suppose we have only two speeds: slow and fast, and this path: A --(sharp turn)-- B ----------- C ...


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The only new constraint that inertia lays onto path-finding is continuity, which means no sudden breaks in velocity. Start by generating an A* path, but with a big twist. The reason A* by itself is not appropriate is because it violates continuity, so lets make a new one. A* chooses the best path as the shortest path, but with inertia the shortest path is ...


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I'm doing research in autonomous navigation for robotics, and this is a familiar problem. The real world analogy is you have two vehicles (a robotic motorcycle and a robotic tank) that have to decide on going through a narrow alleyway or not. Dimensions and positions are known by whatever means (radar, LIDAR, maps, GPS, etc)--how do you program such a ...



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