A* Pathfinding Algorithm AnnouncementsTortoise SVN DemoPath Following at Constant SpeedPathfindingA* pathfindingOverview of A* algorithmRunning ExampleOpen and Closed ListsWhere to go next…Computing the costComputing H using Manhattan methodContinuing the searchExampleExample continuedSome issuesMore issuesSlide Number 18A* Pathfinding AlgorithmGame Design ExperienceProfessor Jim WhiteheadFebruary 20, 2009Creative Commons Attribution 3.0 (Except A* algorithm images) creativecommons.org/licenses/by/3.0Announcements• Homework #2 (Quadtrees)► Due Monday, (by 1pm in my mailbox, or in class)► Ask questions on forum► Friday section• 12:30-1:40pm, Natural Sciences Annex, Room 102 • Partially operational game prototype► Due Friday, February 27► Need to demonstrate:• An XNA project (25%) • A few objects, including a player object (25%) • The ability to move the player object, even if in a limited way (25%) • Some ability for the player object to interact with their world (firing, jumping, picking up objects, etc.) (25%) ► Submit on CDROM, USB Drive, or URL to Subversion projectTortoise SVN Demo• Demonstration of use of Tortoise SVN tool► http://tortoisesvn.tigris.org/► Also: link on Tools page of class websitePath Following at Constant Speed• In previous lecture showed path following► Used Lerp and CatmullRom interpolation methods built into Vector2 class► These methods take an amount• A number between 0 and 1 indicating the percentage distance between the start and end points (a “weight”)• Problem► A given amount can result in different perceived speeds, depending on the length of the line• Solution► Given a desired speed in terms of units per clock tick► Compute per-tick change in amount as follows► Per-tick-amount = unit-speed / Vector2.Distance(start, end)• That is, determine the percentage of the total distance between the points covered by one unit-speedDemonstration of updated code with constant speedPathfinding• In many games, computer controlled opponents need to move from one place to another in the game world► If the start and end points are known in advance, and never change, • Can simply define a fixed path• Computed before the game begins• Use path following techniques from previous lecture• Most platformers do this► If the start and end points vary, and are not known in advance (or may vary due to changes in game state), • Have to compute the path to follow while the game is running• Need to use a pathfinding algorithmVideo of championship Starcraft matchA* pathfinding• A* algorithm is widely used as the conceptual core for pathfinding in computer games► Most commercial games use variants of the algorithm tailored to the specific game► Original paper:• A Formal Basis for the Heuristic Determination of Minimum Cost Paths• Hart, P.E.; Nilsson, N.J.; Raphael, B.• IEEE Trans. Systems Science and Cybernetics, 4(2), July 1968, pp. 100-107• A* is a graph search algorithm► There is a large research literature on graph search algorithms, and computer game pathfindingOverview of A* algorithm• The following slides borrow heavily from:► A* Pathfinding for Beginners, by Patrick Lester• http://www.policyalmanac.org/games/aStarTutorial.htm• Problem► A unit wants to move from point A to point B on a game map, ideally along the shortest path• Assume the game map is a rectangular grid– Makes explanation easier, algorithm can accommodate many grid types– The literature views the map as a graph• Map squares are– Open/walkable (open terrain)– Or closed/unwalkable (walls, water, etc.)Running Example• A unit in the green square wants to move to the red square► From here on out, we’ll call the squares “nodes” to be consistent with the research literature• Moving► Horizontally or vertically requires 10 movement points► Diagonal movement requires 14 movement points► Cannot move through blue squares (wall, unwalkable)• Observations► Can’t just draw a line between A and B and follow that line• Wall in-between► It’s not ideal to just follow the minimal line between A and B until you hit the wall, then walk along wall• Not an optimal pathPathfinding example. Green square is starting location, red square is desired goal. Blue is a wall. Black is walkable, blue is unwalkable.Open and Closed Lists• Starting the search► Add A to open list of nodes to be considered► Look at adjacent nodes, and add all walkable nodes to the open list• I.e., ignore walls, water, or other illegal terrain• For each of these squares, note that their parent node is the starting node, A► Remove A from open list, add to closed list• Open list► Contains nodes that might fall along the path (or might not)► A list of nodes that need to be “checked out”• Closed list► A list of nodes that no longer need to be consideredState of A* after the first step has completed. All adjacent nodes are part of the open list. The circle with line points to the parent node. The blue outline around the green start node indicates it is in the closed list.Where to go next…• Now need to determine which node to consider next► Do not want to consider all nodes, since the number of nodes expands exponentially with each square moved (bad)• Want to pick the node that is closest to the destination, and explore that one first• Intuitive notion of “closest”► Add together:• The cost we have paid so far to move to a node (“G”)• An estimate of the distance to the end point (“H”)Computing the cost• F=G+H► Movement cost to get to destination• G► Movement cost to move from start node A to a given node on the grid• Following the path generated to get there• Add 10 for horizontal/vertical move, 14 for diagonal move• H► Estimated movement cost to move from that given node on the grid to the final destination, node B ► Known as the heuristic• A guess as to the remaining distance• There are many ways to make this guess—this lecture shows just one wayComputing H using Manhattan method• Manhattan method► Calculate the total number of nodes moved horizontally/vertically to reach the target node (B) from the current square► Ignore diagonal movement, and ignore obstacles that may be in the way. ► Multiply total # of nodes by 10, the cost for moving one node horizontally/vertically. ► Called Manhattan method because it is like calculating the