Proposals/Obsolete Proposals/Macro Pathfinding

From UFO:AI
< Proposals‎ | Obsolete Proposals
Revision as of 10:40, 13 August 2013 by H-hour (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

Macro Pathfinding

Author: BTAxis (talk, contrib)

This article is obsolete. ffesti has implemented a Dijkstra algorithm that can plot routes over large distances quickly, in most cases.

Just FYI:
If you are speaking about this patch here: #1820316 Implement Dijkstra algorithm for Grid_MoveCalc
This is not yet in the game, but will be as soon as some minor things are solved I imagine. --Hoehrer
It is in now. --BTAxis 22:33, 13 December 2007 (CET)

Currently, AI pathfinding suffers from a range limitation. This range limitation is rooted in the amount of CPU time necessary to calculate the path, and in the byte size. This range limitation also applies to human-controlled soldiers. A good way to visualize this is by turning on order confirmation in the options and experimenting with how far you can set the path. This limitation is referred to in this article as the "short-range pathing range".

This article describes a system that will allow for AI pathfinding across large distances (large being defined as larger than the short-range pathing range). With this system, the AI will be able to find its way around obstacles and through mazes. This is especially important on random map assemblies, because the terrain layout on random map assemblies is uncertain until the game has assembled one.

This system is a generalization of the civilian waypoint system that has already been implemented. This system allows civilians to follow a long path to a destination by short-range pathing towards the next highest priority waypoint in range. To extend this to be dynamic, the following conditions must be met:

  • Waypoints placed on the map must not have a priority value assigned to them at compile time.
  • Waypoints placed on the map must be in short-range pathing range of at least one other waypoint, preferrably more than one. This is the responsibility of the map makers.
  • Every actor on the map must have a table of waypoints with priority values assigned to them.
  • It must be possible to update the waypoint table ingame.

How it works

In order to allow the AI to use the waypoints, the waypoints must be connected to a graph. In the graph, each node is a waypoint and nodes will share an edge if the two waypoints can find a path ot each other using the short-range pathing algorithm. This graph is constructed at load time. For random assemblies, it must happen after the assembly is complete. Note: on map assemblies it is VERY IMPORTANT that waypoints try to find waypoints outside their own tile!

With the graph in place, actors can be assigned their waypoint tables. When the AI moves an actor, it can do so in two ways:

  • Locally. The AI moves the actor as it does in the current game. The AI should choose to move locally if:
    • There are no waypoints within the search radius (the search radius should reflect the short-range pathing range). Ideally, this shouldn't happen.
    • Enemies are in the area, and movement should focus on combat.
  • By waypoints. The AI will use the waypoint graph to find a route to a remote destination.

When the AI moves by waypoints, it should first decide where on the map it wants the actor to go.* For example, the actor could be on a mission to free aliens from Alien Containment. In that case, the AI will select a waypoint on the Alien Containment map tile. Next, the AI gets a list of the waypoints inside the search radius of the actor. Finally, the AI uses the waypoint graph to plot the shortest route (in the graph, not on the map!) from the destination waypoint to all of the waypoints near the actor. If there are multiple shortest routes, it just picks one. Given this path in the graph, the actor's waypoint table will be updated with new waypoint priorities, starting with the waypoint near the actor that is part of the path and ending at the destination waypoint. All other waypoints will be given priority -1 (or some other value - magic numbers are against the coding policy). The AI will then proceed to move the actor towards the waypoints analogous to the civilian waypoint system.

* Deciding where the actor should go is a whole different story. However, the point is that wherever it does choose to go, it will be able to find its way there efficiently.

Algorithms and data structures

The following is a description of some of the algorithms and data structures inolved, including pseudocode. Whenever an identifier uses underscores, like next_node, that means it's an abstract for a more correct coding syntax, used for better readability.

Graph representation

The graph is represented by an array of structs. Each struct represents one waypoint, and contains a list of waypoints within short range pathing range.

typedef struct graphNode_s {
  edict_t *thisWaypoint; /**< This should directly reference to the waypoint entity. */
  /* Arrays are of size 16 here. It is unlikely that there are that many waypoints in range of each other, but it still calls for robustness. */
  struct graphNode_s *nodesInRange[16]; /**< elements in the overall array of nodes. */
  int distanceTo[16]; /**< each element holds the pathfinding distance to the node referenced to by nodesInRange with the same index. */
  int distanceFromActor; /**< used only when copied when the path must be found.
} graphNode_t;

/* 256 waypoints is probably a good limit */
graphNode graph[256];

Waypoint numbering

Each waypoint entity has a variable that represents the index in graph[] that references this waypoint (inverse referencing).

int indexForThisWaypoint;

Waypoint representation per actor

Each actor has a single array that represents all waypoints on the map. Each element of the array represents one waypoint. The waypoint referenced by a given index is the same waypoint referenced by graph[] with the same index. The value of the element is the waypoint's priority for that actor.

int waypoints[256];

Building the graph

As stated above, the graph is constructed when the map is loaded. Note that the following pseudocode abstracts from exactly how the list of candidate waypoints is retrieved, as well as from how the pathfinding involved is called.

/* First we must link the waypoint entities to the graph data struct: */
for (waypoint_entities_on_the_map) {
  waypoint.indexForThisWaypoint = counter;
  graph[counter].thisWaypoint = this_waypoint;
  counter++;
}

/* Next, we build the actual graph. */
for (waypoint_entities_on_the_map) {
  candidate_waypoints[] = getCandidateWaypoints(this_waypoint);
  for (candidate_waypoints) {
    if (temp = pathTo(this_waypoint, candidate_waypoint)) {
      graph[this_waypoint.indexForThisWaypoint].nodesInRange[nodes_index] = candidate_waypoint.indexForThisWaypoint; //Save edge
      graph[this_waypoint.indexForThisWaypoint].distanceTo[nodes_index] = temp; //Save length of edge
    }
    else {
    /* Do nothing. This node does not share an edge with the candidate node. */
    }
  }
}

int pathTo(entity, entity) {
  !! Black box! I know nothing about how pathfinding works, so this is left as an excercise to the coder. !!
  /* return -1 if the pathfinding code can NOT find its way from the first waypoint to the candidate waypoint. Otherwise, return the pathfinding distance. */
}

entity[] getCandidateWaypoints(waypoint) {
  !! Black box !!
  /* return an array of waypoints that are within "candidate range" */
}

Finding a path in the graph

The following assumes a destination exists. The graph used here is a temporary copy of the one built at load time and is discarded once the path has been found.

int nextNodes[] = setStartWaypoints(this_actor);
graphNode tempGraph = graph; //Make a copy.
memset (this_actor.waypoints, 0); //clear out previous path
if (sizeOf(nextNodes) == 0) {
  /* No nodes in range! Local movement. */
}
else {
  /* First a one-off bootstrapping loop that sets the distance to the starting waypoints from the actor. */
  for (nextNodes) {
    tempGraph[next_node].distanceFromActor = pathTo(this_actor, tempGraph[next_node].thisWaypoint);
    /* Mark these as the starting waypoints. starts holds indices for tempGraph[]. */
    int starts[starts_index] = next_node;
  }
  /* Now for a more iterative process: Expand the "walked" part of the graph and mark the nodes with their total pathing distance. */
  for (nextNodes) {
    for (tempGraph[next_node].nodesInRange) {
      /* Now comes a tricky bit. The node in range is updated to be the next "step" when it hasn't been walked yet,
       * OR when it has but the previous path to it was LONGER. This allows us to find the shortest path.*/
      if (tempGraph[node_in_range].distanceFromActor > tempGraph[next_node].distanceFromActor + tempGraph[next_node].distanceTo[index_of_nodesInRange]) {
        tempGraph[node_in_range].distanceFromActor = tempGraph[next_node].distanceFromActor + tempGraph[next_node].distanceTo[index_of_nodesInRange];
        newNextNodes[n3_index] = tempGraph[node_in_range];
        n3_index++;
      }
      else {
        /* Do nothing. The node in range was already pathed with a path that was as long as or shorter than the one we're attempting. */
      }
    }
    nextNodes = newNextNodes; //copy the next iteration to newNodes
    memset(newNextNodes, 0); //empty
    n3_index = 0; //reset
  }
}
/* This process terminates. When it has, the shortest path from the actor to the destination waypoint is known,
 * and the destination waypoint knows how far it is from the actor. Now all we have to do is walk back to the
 * actor by steepest slope approach: */

int priority = 1; //1 being the destination priority in this case.
int nextNearest = destination_index;
while (!elementOf(destination_index, starts)) {
  this_actor.waypoints[nextNearest] = priority;
  priority++;
  int newNextNearest = tempGraph[nextNearest].distanceFromActor;
  for (tempGraph[nextNearest].nodesInRange) {
    if (newNextNearest > tempGraph[index_of_nodesInRange].distanceFromActor) {
      newNextNearest = tempGraph[index_of_nodesInRange].distanceFromActor);
    }
  }
  nextNearest = newNextNearest; //Choose the node that is next nearest the actor as the next step in the graph
}
this_actor.waypoints[nextNearest] = priority; //This one is for the starting waypoint, which is skipped by the while loop.

/* Done! The actor now has priorities assigned to the waypoints. */

int[] setStartWaypoints(actor) {
  !! Black box !!
  /* return an array of indices for tempGraph[]. The indices are for those waypoints that this actor can path to. */
}

int elementOf(int, int[]) {
  /* Elementary. Returns 1 if the first argument is an element of the second argument, 0 otherwise. */
}