I am having issues, trying to get my A Star algorithm'algorithm working, the main problem is with my process of the algorithm, I am using something known as the TL engine which has some of its own datatypes and functions but for the most part, I am 90% certain my problem is with my C++ code and not with TL-engine function, specifically with my understanding / implementation of the algorithm.
myMy grid is a 10x10 grid, called SNodeCubeArray[10][10] my.
My program reads a txtfile in and sets a base value which determines which bools it will set.
- Normal Nodes always have a cost of :1= 1
- Woods Cost = 2
- Water Cost = 3
- Obstacle / wall = 0 (but is impassable)
someSome functions / variables explanation:
This is the structstruct I have declared it is the datatype I'll be using with Unique_ptr's deque.
// Code by ryan newell uclan student G20618255 -> for self reference
struct node
{
public:
// The Structs actual location in the array eg [3,3]
int mLoc_X; // Current X- Position in the Node stored
int mLoc_y; // Current Y- Position in the node stored
int mHeuristic_Manhattan; // Is the stored nodes manhattan distance
// These floats are so I can store the coordinates of the linked cubes it should be in.
//Temporary storage for the string reading.
char mNodeValue;
// Declares what kind node it is.
bool mIsObstacle = false;
bool mIsWater = false;
bool mIsWoods = false;
bool mIsStartNode = false;
bool mIsDestNode = false;
//Cost storage heurestic
int mNodeCost; // The cost to travel between the nodes
int mAccumulatedCost; // The accumulated cost of the node + its parent
int mvalue_f; // Is its total score which will equals NodeCost+Heuristic
//pARENT
node* m_parent;
};
This Isis my functions section of the code being used, that we need to know about, any functions I haven't likely are TL-engine functions, and tested to work.
// sorts the node array by order of total_score
bool CompareNodeScores(unique_ptr<node>& lhs, unique_ptr<node>& rhs)
{
return lhs->mvalue_f < rhs->mvalue_f;
}
bool check_greater_fcost(
bool check_greater_fcost(deque <unique_ptr< node >> &closeList,
unique_ptr <node> &NewState_n,
deque <unique_ptr<node>> &openList)
{
if (openList.empty() == false)
{
for (auto it = openList.begin(); it != openList.end(); it++)
{
if ((*it)->mLoc_X == NewState_n.get()->mLoc_X &&
(*it)->mLoc_y == NewState_n.get()->mLoc_y && NewState_n->mvalue_f >= (*it)->mvalue_f)
NewState_n->mvalue_f >= {(*it)->mvalue_f)
return true;
}
return false;
}
}
if (closeList.empty() == false)
{
for (auto it = closeList.begin(); it != closeList.end(); it++)
{
if ((*it)->mLoc_X == NewState_n.get()->mLoc_X &&
(*it)->mLoc_y == NewState_n.get()->mLoc_y && NewState_n->mvalue_f >= (*it)->mvalue_f)
NewState_n->mvalue_f >= {(*it)->mvalue_f)
return true;
}
return false;
}
}
}
bool astar_checklists(
deque <unique_ptr< node >> &closeList,
unique_ptr <node> &RuleCurrent,
deque <unique_ptr<node>> &openList,
unique_ptr <node> &CurrentNode)
{
// set p to the beginning of the loop
for (auto it = openList.begin(); it != openList.end(); it++)
{
if ((*it)->mLoc_X == RuleCurrent.get()->mLoc_X &&
(*it)->mLoc_y == RuleCurrent.get()->mLoc_y)
{
(*it)->mAccumulatedCost = RuleCurrent->mAccumulatedCost;
(*it)->mHeuristic_Manhattan = RuleCurrent->mHeuristic_Manhattan;
(*it)->mNodeCost = RuleCurrent->mAccumulatedCost;
(*it)->mvalue_f = RuleCurrent->mHeuristic_Manhattan + RuleCurrent->mNodeCost;
(*it)->m_parent = CurrentNode->m_parent;
return true;
}
}
// set p to the beginning of the loop
for (auto it = closeList.begin(); it != closeList.end(); it++)
{
if ((*it)->mLoc_X == RuleCurrent.get()->mLoc_X &&
(*it)->mLoc_y == RuleCurrent.get()->mLoc_y){
{
(*it)->mAccumulatedCost = RuleCurrent->mAccumulatedCost;
(*it)->mHeuristic_Manhattan = RuleCurrent->mHeuristic_Manhattan;
(*it)->mNodeCost = RuleCurrent->mAccumulatedCost;
(*it)->mvalue_f = RuleCurrent->mHeuristic_Manhattan + RuleCurrent->mNodeCost;
(*it)->m_parent = CurrentNode->m_parent;
openList.push_back(move(*it));
closeList.erase(it);
sort(openList.begin(), openList.end(), CompareNodeScores);
return true;
}
else
{
return true;
}
}
return false;
}
void AStarFindPathNew(
I3DEngine* myEngine,
IModel* ANodeCubeModels[10][10],
node SNodeArray[10][10],
int storemLoc_X,
int storemLoc_y,
int storedestmLoc_x,
int storedestmLoc_y)
{
int xstore = 0;
int ystore = 0;
int xplus = 0;
int yplus = 0;
bool goal_state_found = false;
//bool current_is_on_openlist = false;
//bool current_is_on_closelist = false; previous unused values
//bool current_is_on_bothlists = false;
ofstream outfile;
unique_ptr <node> NewState_n(new node);
unique_ptr <node> CurrentNode(new node);
unique_ptr <node> initialState(new node);
deque <unique_ptr < node > > openList;
deque <unique_ptr < node > > closeList;
// Declaring Intial state
initialState->mLoc_X = gSTART_X;
initialState->mLoc_y = gSTART_Y;
initialState->m_parent = 0;
initialState->mAccumulatedCost = 0;
initialState->mHeuristic_Manhattan = CalculateManhattan( ANodeCubeModels,
SNodeArray,
initialState->mLoc_X,
initialState->mLoc_y,
storedestmLoc_x,
storedestmLoc_y);
initialState->mvalue_f = initialState->mHeuristic_Manhattan + initialState->mNodeCost;
DisplayOpenlist(openList, outfile);
openList.push_front(move(initialState));
xstore = gSTART_X;
ystore = gSTART_Y;
while (!goal_state_found || !openList.empty())
{
// Values Value_G = Manhattan Distance = mHeuristic_Manhattan
// Value: Value_MC = Movement Cost Heuristic = mNodeCost
// Value: Value_F = H + MC = m_valuef
CurrentNode = (move(openList.front()));
cout << "popped open to New current Node = " <<
CurrentNode->mLoc_X << ":" << CurrentNode->mLoc_y << endl;
openList.pop_front();
//cout << CurrentNode->mLoc_X << "," << CurrentNode->mLoc_y << " Target: "
// << gDDEST_X << "," << gDDEST_Y << endl;
if (CurrentNode->mLoc_X == storedestmLoc_x && CurrentNode->mLoc_y == storedestmLoc_y)
{
int xstore = 0;
int ystore = 0;
int xplus = 0;
int yplus = 0;
bool goal_state_found = false;
//bool current_is_on_openlist = false;
//bool current_is_on_closelist = false; previous unused values
//bool current_is_on_bothlists = false;
ofstream outfile;
unique_ptr <node> NewState_n(new node);
unique_ptr <node> CurrentNode(new node);
unique_ptr <node> initialState(new node);
deque <unique_ptr < node > > openList;
deque <unique_ptr < node > > closeList;
// Declaring Intial state
initialState->mLoc_X = gSTART_X;
initialState->mLoc_y = gSTART_Y;
initialState->m_parent = 0;
initialState->mAccumulatedCost = 0;
initialState->mHeuristic_Manhattan = CalculateManhattan(ANodeCubeModels, SNodeArray, initialState->mLoc_X, initialState->mLoc_y, storedestmLoc_x, storedestmLoc_y);
initialState->mvalue_f = initialState->mHeuristic_Manhattan + initialState->mNodeCost;
DisplayOpenlist(openList, outfile);
openList.push_front(move(initialState));
xstore = gSTART_X;
ystore = gSTART_Y;
while (!goal_state_found || !openList.empty())
{
// Values Value_G = Manhattan Distance = mHeuristic_Manhattan
// Value: Value_MC = Movement Cost Heuristic = mNodeCost
// Value: Value_F = H + MC = m_valuef
CurrentNode = (move(openList.front()));
cout << "popped open to New current Node = " << CurrentNode->mLoc_X << ":" << CurrentNode->mLoc_y << endl;
openList.pop_front();
//cout << CurrentNode->mLoc_X << "," << CurrentNode->mLoc_y << " Target: " << gDDEST_X << "," << gDDEST_Y << endl;
if (CurrentNode->mLoc_X == storedestmLoc_x && CurrentNode->mLoc_y == storedestmLoc_y)
{
cout << "Found Path" << endl;
_getch();
fRetracesteps(myEngine, ANodeCubeModels, SNodeArray, CurrentNode);
return;
}
DisplayOpenlist(openList, outfile);
for (int i = 0; i < 4; i++)
{
if (i == 0)
{
xplus = 0;
yplus = +1;
}
if (i == 1)
{
xplus = +1;
yplus = 0;
}
if (i == 2)
{
xplus = 0;
yplus = -1;
}
if (i == 3)
{
xplus = -1;
yplus = 0;
}
cout << "Generating Rule X:" << xstore + xplus << "Y:" << ystore + yplus << endl;
//d) i Generate N
NewState_n.reset(new node);
NewState_n->mLoc_X = CurrentNode->mLoc_X + xplus;
NewState_n->mLoc_y = CurrentNode->mLoc_y + yplus;
NewState_n->mAccumulatedCost = CurrentNode->mAccumulatedCost + SNodeArray[NewState_n->mLoc_X][NewState_n->mLoc_y].mHeuristic_Manhattan;
NewState_n->mHeuristic_Manhattan = CalculateManhattan(ANodeCubeModels, SNodeArray, NewState_n->mLoc_X, NewState_n->mLoc_y, storedestmLoc_x, storedestmLoc_y);
NewState_n->mvalue_f = NewState_n->mHeuristic_Manhattan + NewState_n->mAccumulatedCost;
NewState_n->m_parent = CurrentNode.get();
cout << NewState_n->m_parent;
cout << "Checking curent rule for within boundaries" << endl;
if (!checkcurrentruleinbounds(NewState_n))
{
continue;
}
NewState_n->mIsObstacle = SNodeArray[CurrentNode->mLoc_X + xplus][CurrentNode->mLoc_y + yplus].mIsObstacle;
cout << "Checking whether the rule is on the lists or has a greater cost" << endl;
if (astar_checklists(closeList, NewState_n, openList,CurrentNode) && !check_greater_fcost(closeList, NewState_n, openList))
{
continue;
}
if (NewState_n->mIsObstacle != true)
{
//cout << "Setting newstate_n" << NewState_n.get()->mLoc_X << NewState_n->mLoc_y << NewState_n->m_parent;
ANodeCubeModels[NewState_n->mLoc_X][NewState_n->mLoc_y]->SetSkin("opennode.png");
openList.push_back(move(NewState_n));
sort(openList.begin(), openList.end(), CompareNodeScores);
cout << CurrentNode->mLoc_X << ":" << CurrentNode->mLoc_y;
}
DisplayOpenlistWithScore(openList, outfile);
}
cout << "Finished first set of rules" << endl;
cout << "Close List:" << CurrentNode->mLoc_X <<":"<< CurrentNode->mLoc_y << endl;
ANodeCubeModels[CurrentNode->mLoc_X][CurrentNode->mLoc_y]->SetSkin("closednode.png");
closeList.push_back(move(CurrentNode));
_getch();
myEngine->DrawScene();
DelayProc(0.75);
}
}
cout << "Found Path" << endl;
_getch();
fRetracesteps(myEngine, ANodeCubeModels, SNodeArray, CurrentNode);
return;
}
DisplayOpenlist(openList, outfile);
for (int i = 0; i < 4; i++)
{
if (i == 0)
{
xplus = 0;
yplus = +1;
}
if (i == 1)
{
xplus = +1;
yplus = 0;
}
if (i == 2)
{
xplus = 0;
yplus = -1;
}
if (i == 3)
{
xplus = -1;
yplus = 0;
}
cout << "Generating Rule X:" << xstore + xplus << "Y:" << ystore + yplus << endl;
//d) i Generate N
NewState_n.reset(new node);
NewState_n->mLoc_X = CurrentNode->mLoc_X + xplus;
NewState_n->mLoc_y = CurrentNode->mLoc_y + yplus;
NewState_n->mAccumulatedCost = CurrentNode->mAccumulatedCost +
SNodeArray[NewState_n->mLoc_X][NewState_n->mLoc_y].mHeuristic_Manhattan;
NewState_n->mHeuristic_Manhattan = CalculateManhattan(ANodeCubeModels, SNodeArray,
NewState_n->mLoc_X, NewState_n->mLoc_y, storedestmLoc_x, storedestmLoc_y);
NewState_n->mvalue_f = NewState_n->mHeuristic_Manhattan + NewState_n->mAccumulatedCost;
NewState_n->m_parent = CurrentNode.get();
cout << NewState_n->m_parent;
cout << "Checking curent rule for within boundaries" << endl;
if (!checkcurrentruleinbounds(NewState_n))
{
continue;
}
NewState_n->mIsObstacle =
SNodeArray[CurrentNode->mLoc_X + xplus][CurrentNode->mLoc_y + yplus].mIsObstacle;
cout << "Checking whether the rule is on the lists or has a greater cost" << endl;
if (astar_checklists(closeList, NewState_n, openList, CurrentNode) &&
!check_greater_fcost(closeList, NewState_n, openList))
{
continue;
}
if (NewState_n->mIsObstacle != true)
{
//cout << "Setting newstate_n" << NewState_n.get()->mLoc_X <<
//NewState_n->mLoc_y << NewState_n->m_parent;
ANodeCubeModels[NewState_n->mLoc_X][NewState_n->mLoc_y]->SetSkin("opennode.png");
openList.push_back(move(NewState_n));
sort(openList.begin(), openList.end(), CompareNodeScores);
cout << CurrentNode->mLoc_X << ":" << CurrentNode->mLoc_y;
}
DisplayOpenlistWithScore(openList, outfile);
}
cout << "Finished first set of rules" << endl;
cout << "Close List:" << CurrentNode->mLoc_X <<":"<< CurrentNode->mLoc_y << endl;
ANodeCubeModels[CurrentNode->mLoc_X][CurrentNode->mLoc_y]->SetSkin("closednode.png");
closeList.push_back(move(CurrentNode));
_getch();
myEngine->DrawScene();
DelayProc(0.75);
}
}
