README.md

Pathfinding

Pathfinding algorithms for autonomous devices. There are two methods that are both based on the A* pathfinding algorithm that determine the most optimal path for robots to take in a field with obstacles. One method is confined to a grid while the other is continuous and allows complex vertex obstacles.

Grid Pathfinding

Here is some simple code and visualization for how to use the grid pathfinding algorithm.

Pathfinding examplePathfinding = new Pathfinding();

CollisionGrid exampleCollisionGrid = new CollisionGrid(8, 8);

exampleCollisionGrid.setTileBlocked(2, 4, true);
exampleCollisionGrid.setTileBlocked(3, 4, true);
exampleCollisionGrid.setTileBlocked(4, 4, true);
exampleCollisionGrid.setTileBlocked(2, 5, true);
exampleCollisionGrid.setTileBlocked(3, 5, true);
exampleCollisionGrid.setTileBlocked(4, 5, true);

List<Node> path = examplePathfinding.findPath(new Vector2i(0,7), new Vector2i(7, 0), exampleCollisionGrid);

for (Node n : path) {
    System.out.println("(" + n.tile.getX() + ", " + n.tile.getY() + ")");
}

The code returns the following path:

(1, 6)
(1, 5)
(1, 4)
(2, 3)
(3, 2)
(4, 1)
(5, 1)
(6, 1)
(7, 0)

What the grid space looks like before the algorithm starts:

What the grid space looks like after the algorithm is finished:

Continuous Pathfinding

Here is some simple code and visualization for how to use the continuous pathfinding algorithm.

double sx = 10.0;
double sy = 10.0;
double gx = 50.0;
double gy = 50.0;
double gridSize = 1.0;
double robotSize = 1.0;

List<Vector2f> obstaclePositions = new ArrayList<Vector2f>();

for (int i = 0; i < 60; i++) {
    obstaclePositions.add(new Vector2f(i, 0.0));
}
for (int i = 0; i < 60; i++) {
    obstaclePositions.add(new Vector2f(60.0, i));
}
for (int i = 0; i < 61; i++) {
    obstaclePositions.add(new Vector2f(i, 60.0));
}
for (int i = 0; i < 61; i++) {
    obstaclePositions.add(new Vector2f(0.0, i));
}
for (int i = 0; i < 40; i++) {
    obstaclePositions.add(new Vector2f(20.0, i));
}
for (int i = 0; i < 40; i++) {
    obstaclePositions.add(new Vector2f(40.0, 60.0 - i));
}

ContinuousPathfinding exampleContinuousPathfinding = new ContinuousPathfinding();

List<ContinuousNode> path = exampleContinuousPathfinding.findPath(new Vector2f(sx, sy), new Vector2f(gx, gy), obstaclePositions, gridSize, robotSize);

for (ContinuousNode n : path) {
    System.out.println("(" + n.position.getX() + ", " + n.position.getY() + ")");
}

The code returns the following path:

(10, 10)
(10, 11)
(10, 12)
(10, 13)
(10, 14)
(10, 15)
(10, 16)
(11, 17)
(12, 18)
(12, 19)
(12, 20)
(12, 21)
(12, 22)
(12, 23)
(12, 24)
(12, 25)
(13, 26)
(13, 27)
(13, 28)
(13, 29)
(13, 30)
(13, 31)
(13, 32)
(13, 33)
(13, 34)
(14, 35)
(15, 36)
(16, 37)
(17, 38)
(18, 39)
(19, 40)
(20, 41)
(21, 40)
(22, 39)
(22, 38)
(23, 37)
(24, 36)
(25, 35)
(26, 34)
(27, 33)
(28, 32)
(29, 31)
(30, 30)
(31, 29)
(32, 28)
(33, 27)
(34, 26)
(35, 25)
(36, 24)
(37, 23)
(38, 22)
(38, 21)
(39, 20)
(40, 19)
(41, 20)
(42, 21)
(43, 22)
(44, 23)
(45, 24)
(46, 25)
(47, 26)
(48, 27)
(49, 28)
(49, 29)
(49, 30)
(49, 31)
(49, 32)
(49, 33)
(49, 34)
(49, 35)
(49, 36)
(49, 37)
(49, 38)
(49, 39)
(49, 40)
(49, 41)
(49, 42)
(49, 43)
(49, 44)
(49, 45)
(49, 46)
(50, 47)
(50, 48)
(50, 49)
(50, 50)

What the continuous space looks like before the algorithm starts:

What the continuous space looks like after the algorithm is finished: