You cannot select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
openmw-tes3mp/apps/openmw/mwmechanics/pathgrid.cpp

341 lines
12 KiB
C++

#include "pathgrid.hpp"
#include "../mwbase/world.hpp"
#include "../mwbase/environment.hpp"
#include "../mwworld/cellstore.hpp"
#include "../mwworld/esmstore.hpp"
namespace
{
// See https://theory.stanford.edu/~amitp/GameProgramming/Heuristics.html
//
// One of the smallest cost in Seyda Neen is between points 77 & 78:
// pt x y
// 77 = 8026, 4480
// 78 = 7986, 4218
//
// Euclidean distance is about 262 (ignoring z) and Manhattan distance is 300
// (again ignoring z). Using a value of about 300 for D seems like a reasonable
// starting point for experiments. If in doubt, just use value 1.
//
// The distance between 3 & 4 are pretty small, too.
// 3 = 5435, 223
// 4 = 5948, 193
//
// Approx. 514 Euclidean distance and 533 Manhattan distance.
//
float manhattan(const ESM::Pathgrid::Point& a, const ESM::Pathgrid::Point& b)
{
return 300.0f * (abs(a.mX - b.mX) + abs(a.mY - b.mY) + abs(a.mZ - b.mZ));
}
// Choose a heuristics - Note that these may not be the best for directed
// graphs with non-uniform edge costs.
//
// distance:
// - sqrt((curr.x - goal.x)^2 + (curr.y - goal.y)^2 + (curr.z - goal.z)^2)
// - slower but more accurate
//
// Manhattan:
// - |curr.x - goal.x| + |curr.y - goal.y| + |curr.z - goal.z|
// - faster but not the shortest path
float costAStar(const ESM::Pathgrid::Point& a, const ESM::Pathgrid::Point& b)
{
//return distance(a, b);
return manhattan(a, b);
}
}
namespace MWMechanics
{
PathgridGraph::PathgridGraph(const MWWorld::CellStore *cell)
: mCell(nullptr)
, mPathgrid(nullptr)
, mGraph(0)
, mIsGraphConstructed(false)
, mSCCId(0)
, mSCCIndex(0)
{
load(cell);
}
/*
* mGraph is populated with the cost of each allowed edge.
*
* The data structure is based on the code in buildPath2() but modified.
* Please check git history if interested.
*
* mGraph[v].edges[i].index = w
*
* v = point index of location "from"
* i = index of edges from point v
* w = point index of location "to"
*
*
* Example: (notice from p(0) to p(2) is not allowed in this example)
*
* mGraph[0].edges[0].index = 1
* .edges[1].index = 3
*
* mGraph[1].edges[0].index = 0
* .edges[1].index = 2
* .edges[2].index = 3
*
* mGraph[2].edges[0].index = 1
*
* (etc, etc)
*
*
* low
* cost
* p(0) <---> p(1) <------------> p(2)
* ^ ^
* | |
* | +-----> p(3)
* +---------------->
* high cost
*/
bool PathgridGraph::load(const MWWorld::CellStore *cell)
{
if(!cell)
return false;
if(mIsGraphConstructed)
return true;
mCell = cell->getCell();
mPathgrid = MWBase::Environment::get().getWorld()->getStore().get<ESM::Pathgrid>().search(*cell->getCell());
if(!mPathgrid)
return false;
mGraph.resize(mPathgrid->mPoints.size());
for(int i = 0; i < static_cast<int> (mPathgrid->mEdges.size()); i++)
{
ConnectedPoint neighbour;
neighbour.cost = costAStar(mPathgrid->mPoints[mPathgrid->mEdges[i].mV0],
mPathgrid->mPoints[mPathgrid->mEdges[i].mV1]);
// forward path of the edge
neighbour.index = mPathgrid->mEdges[i].mV1;
mGraph[mPathgrid->mEdges[i].mV0].edges.push_back(neighbour);
// reverse path of the edge
// NOTE: These are redundant, ESM already contains the required reverse paths
//neighbour.index = mPathgrid->mEdges[i].mV0;
//mGraph[mPathgrid->mEdges[i].mV1].edges.push_back(neighbour);
}
buildConnectedPoints();
mIsGraphConstructed = true;
return true;
}
const ESM::Pathgrid *PathgridGraph::getPathgrid() const
{
return mPathgrid;
}
// v is the pathgrid point index (some call them vertices)
void PathgridGraph::recursiveStrongConnect(int v)
{
mSCCPoint[v].first = mSCCIndex; // index
mSCCPoint[v].second = mSCCIndex; // lowlink
mSCCIndex++;
mSCCStack.push_back(v);
int w;
for(int i = 0; i < static_cast<int> (mGraph[v].edges.size()); i++)
{
w = mGraph[v].edges[i].index;
if(mSCCPoint[w].first == -1) // not visited
{
recursiveStrongConnect(w); // recurse
mSCCPoint[v].second = std::min(mSCCPoint[v].second,
mSCCPoint[w].second);
}
else
{
if(find(mSCCStack.begin(), mSCCStack.end(), w) != mSCCStack.end())
mSCCPoint[v].second = std::min(mSCCPoint[v].second,
mSCCPoint[w].first);
}
}
if(mSCCPoint[v].second == mSCCPoint[v].first)
{ // new component
do
{
w = mSCCStack.back();
mSCCStack.pop_back();
mGraph[w].componentId = mSCCId;
}
while(w != v);
mSCCId++;
}
return;
}
/*
* mGraph contains the strongly connected component group id's along
* with pre-calculated edge costs.
*
* A cell can have disjointed pathgrids, e.g. Seyda Neen has 3
*
* mGraph for Seyda Neen will therefore have 3 different values. When
* selecting a random pathgrid point for AiWander, mGraph can be checked
* for quickly finding whether the destination is reachable.
*
* Otherwise, buildPath can automatically select a closest reachable end
* pathgrid point (reachable from the closest start point).
*
* Using Tarjan's algorithm:
*
* mGraph | graph G |
* mSCCPoint | V | derived from mPoints
* mGraph[v].edges | E (for v) |
* mSCCIndex | index | tracking smallest unused index
* mSCCStack | S |
* mGraph[v].edges[i].index | w |
*
*/
void PathgridGraph::buildConnectedPoints()
{
// both of these are set to zero in the constructor
//mSCCId = 0; // how many strongly connected components in this cell
//mSCCIndex = 0;
int pointsSize = static_cast<int> (mPathgrid->mPoints.size());
mSCCPoint.resize(pointsSize, std::pair<int, int> (-1, -1));
mSCCStack.reserve(pointsSize);
for(int v = 0; v < pointsSize; v++)
{
if(mSCCPoint[v].first == -1) // undefined (haven't visited)
recursiveStrongConnect(v);
}
}
bool PathgridGraph::isPointConnected(const int start, const int end) const
{
return (mGraph[start].componentId == mGraph[end].componentId);
}
void PathgridGraph::getNeighbouringPoints(const int index, ESM::Pathgrid::PointList &nodes) const
{
for(int i = 0; i < static_cast<int> (mGraph[index].edges.size()); i++)
{
int neighbourIndex = mGraph[index].edges[i].index;
if (neighbourIndex != index)
nodes.push_back(mPathgrid->mPoints[neighbourIndex]);
}
}
/*
* NOTE: Based on buildPath2(), please check git history if interested
* Should consider using a 3rd party library version (e.g. boost)
*
* Find the shortest path to the target goal using a well known algorithm.
* Uses mGraph which has pre-computed costs for allowed edges. It is assumed
* that mGraph is already constructed.
*
* Should be possible to make this MT safe.
*
* Returns path which may be empty. path contains pathgrid points in local
* cell coordinates (indoors) or world coordinates (external).
*
* Input params:
* start, goal - pathgrid point indexes (for this cell)
*
* Variables:
* openset - point indexes to be traversed, lowest cost at the front
* closedset - point indexes already traversed
* gScore - past accumulated costs vector indexed by point index
* fScore - future estimated costs vector indexed by point index
*
* TODO: An intersting exercise might be to cache the paths created for a
* start/goal pair. To cache the results the paths need to be in
* pathgrid points form (currently they are converted to world
* coordinates). Essentially trading speed w/ memory.
*/
std::deque<ESM::Pathgrid::Point> PathgridGraph::aStarSearch(const int start, const int goal) const
{
std::deque<ESM::Pathgrid::Point> path;
if(!isPointConnected(start, goal))
{
return path; // there is no path, return an empty path
}
int graphSize = static_cast<int> (mGraph.size());
std::vector<float> gScore (graphSize, -1);
std::vector<float> fScore (graphSize, -1);
std::vector<int> graphParent (graphSize, -1);
// gScore & fScore keep costs for each pathgrid point in mPoints
gScore[start] = 0;
fScore[start] = costAStar(mPathgrid->mPoints[start], mPathgrid->mPoints[goal]);
std::list<int> openset;
std::list<int> closedset;
openset.push_back(start);
int current = -1;
while(!openset.empty())
{
current = openset.front(); // front has the lowest cost
openset.pop_front();
if(current == goal)
break;
closedset.push_back(current); // remember we've been here
// check all edges for the current point index
for(int j = 0; j < static_cast<int> (mGraph[current].edges.size()); j++)
{
if(std::find(closedset.begin(), closedset.end(), mGraph[current].edges[j].index) ==
closedset.end())
{
// not in closedset - i.e. have not traversed this edge destination
int dest = mGraph[current].edges[j].index;
float tentative_g = gScore[current] + mGraph[current].edges[j].cost;
bool isInOpenSet = std::find(openset.begin(), openset.end(), dest) != openset.end();
if(!isInOpenSet
|| tentative_g < gScore[dest])
{
graphParent[dest] = current;
gScore[dest] = tentative_g;
fScore[dest] = tentative_g + costAStar(mPathgrid->mPoints[dest],
mPathgrid->mPoints[goal]);
if(!isInOpenSet)
{
// add this edge to openset, lowest cost goes to the front
// TODO: if this causes performance problems a hash table may help
std::list<int>::iterator it = openset.begin();
for(it = openset.begin(); it!= openset.end(); ++it)
{
if(fScore[*it] > fScore[dest])
break;
}
openset.insert(it, dest);
}
}
} // if in closedset, i.e. traversed this edge already, try the next edge
}
}
if(current != goal)
return path; // for some reason couldn't build a path
// reconstruct path to return, using local coordinates
while(graphParent[current] != -1)
{
path.push_front(mPathgrid->mPoints[current]);
current = graphParent[current];
}
// add first node to path explicitly
path.push_front(mPathgrid->mPoints[start]);
return path;
}
}