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Move rrt algorithm into its own function

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This commit is contained in:
James Melkonian 2021-09-17 16:27:00 -07:00
parent 9875a74640
commit 36884d6919
1 changed files with 208 additions and 199 deletions
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407
common/src/path.rs
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@ -673,16 +673,6 @@ where
}
// Enable when airbraking/sensible flight is a thing
/// Attempts to find a path from a start to the end using an informed
/// RRT-Connect algorithm. A point is sampled from a bounding spheroid
/// between the start and end. Two separate rapidly exploring random
/// trees extend toward the sampled point. Nodes are stored in k-d trees
/// for quicker nearest node calculations. Points are sampled until the
/// trees connect. A final path is then reconstructed from the nodes.
/// This pathfinding algorithm is more appropriate for 3D pathfinding
/// with wider gaps, such as flying through a forest than for terrain
/// with narrow gaps, such as navigating a maze.
/// Returns a path and whether that path is complete or not.
#[cfg(rrt_pathfinding)]
fn find_air_path<V>(
vol: &V,
@ -694,7 +684,6 @@ where
V: BaseVol<Vox = Block> + ReadVol,
{
let radius = traversal_cfg.node_tolerance;
let mut path = Vec::new();
let mut connect = false;
let total_dist_sqrd = startf.distance_squared(endf);
// First check if a straight line path works
@ -706,8 +695,10 @@ where
.powi(2)
>= total_dist_sqrd
{
let mut path = Vec::new();
path.push(endf.map(|e| e.floor() as i32));
connect = true;
(Some(path.into_iter().collect()), connect)
// Else use RRTs
} else {
let is_traversable = |start: &Vec3<f32>, end: &Vec3<f32>| {
@ -720,216 +711,234 @@ where
//vol.get(*pos).ok().copied().unwrap_or_else(Block::empty).
// is_fluid();
};
let mut node_index1: usize = 0;
let mut node_index2: usize = 0;
informed_rrt_connect(start, end, is_traversable)
}
}
// Each tree has a vector of nodes
let mut nodes1 = Vec::new();
let mut nodes2 = Vec::new();
/// Attempts to find a path from a start to the end using an informed
/// RRT-Connect algorithm. A point is sampled from a bounding spheroid
/// between the start and end. Two separate rapidly exploring random
/// trees extend toward the sampled point. Nodes are stored in k-d trees
/// for quicker nearest node calculations. Points are sampled until the
/// trees connect. A final path is then reconstructed from the nodes.
/// This pathfinding algorithm is more appropriate for 3D pathfinding
/// with wider gaps, such as flying through a forest than for terrain
/// with narrow gaps, such as navigating a maze.
/// Returns a path and whether that path is complete or not.
#[cfg(rrt_pathfinding)]
fn informed_rrt_connect(
start: Vec3<f32>,
end: Vec3<f32>,
is_valid_edge: impl Fn(&Vec3<f32>, &Vec3<f32>) -> bool,
) -> (Option<Path<Vec3<i32>>>, bool) {
let mut path = Vec::new();
// The parents hashmap stores nodes and their parent nodes as pairs to
// retrace the complete path once the two RRTs connect
let mut parents1 = HashMap::new();
let mut parents2 = HashMap::new();
// Each tree has a vector of nodes
let mut node_index1: usize = 0;
let mut node_index2: usize = 0;
let mut nodes1 = Vec::new();
let mut nodes2 = Vec::new();
// The path vector stores the path from the appropriate terminal to the
// connecting node or vice versa
let mut path1 = Vec::new();
let mut path2 = Vec::new();
// The parents hashmap stores nodes and their parent nodes as pairs to
// retrace the complete path once the two RRTs connect
let mut parents1 = HashMap::new();
let mut parents2 = HashMap::new();
// K-d trees are used to find the closest nodes rapidly
let mut kdtree1 = KdTree::new();
let mut kdtree2 = KdTree::new();
// The path vector stores the path from the appropriate terminal to the
// connecting node or vice versa
let mut path1 = Vec::new();
let mut path2 = Vec::new();
// Add the start as the first node of the first k-d tree
kdtree1
.add(&[startf.x, startf.y, startf.z], node_index1)
.unwrap_or_default();
nodes1.push(startf);
node_index1 += 1;
// K-d trees are used to find the closest nodes rapidly
let mut kdtree1 = KdTree::new();
let mut kdtree2 = KdTree::new();
// Add the end as the first node of the second k-d tree
kdtree2
.add(&[endf.x, endf.y, endf.z], node_index2)
.unwrap_or_default();
nodes2.push(endf);
node_index2 += 1;
// Add the start as the first node of the first k-d tree
kdtree1
.add(&[startf.x, startf.y, startf.z], node_index1)
.unwrap_or_default();
nodes1.push(startf);
node_index1 += 1;
let mut connection1_idx = 0;
let mut connection2_idx = 0;
// Add the end as the first node of the second k-d tree
kdtree2
.add(&[endf.x, endf.y, endf.z], node_index2)
.unwrap_or_default();
nodes2.push(endf);
node_index2 += 1;
// Scalar non-dimensional value that is proportional to the size of the
// sample spheroid volume. This increases in value until a path is found.
let mut search_parameter = 0.01;
let mut connection1_idx = 0;
let mut connection2_idx = 0;
// Maximum of 7000 iterations
for _i in 0..7000 {
if connect {
break;
}
let mut connect = false;
// Sample a point on the bounding spheroid
let (sampled_point1, sampled_point2) = {
let point = point_on_prolate_spheroid(startf, endf, search_parameter);
(point, point)
};
// Scalar non-dimensional value that is proportional to the size of the
// sample spheroid volume. This increases in value until a path is found.
let mut search_parameter = 0.01;
// Find the nearest nodes to the the sampled point
let nearest_index1 = kdtree1
.nearest_one(
&[sampled_point1.x, sampled_point1.y, sampled_point1.z],
&squared_euclidean,
)
.map_or(0, |n| *n.1);
let nearest_index2 = kdtree2
.nearest_one(
&[sampled_point2.x, sampled_point2.y, sampled_point2.z],
&squared_euclidean,
)
.map_or(0, |n| *n.1);
let nearest1 = nodes1[nearest_index1];
let nearest2 = nodes2[nearest_index2];
// Extend toward the sampled point from the nearest node of each tree
let new_point1 =
nearest1 + (sampled_point1 - nearest1).normalized().map(|a| a * radius);
let new_point2 =
nearest2 + (sampled_point2 - nearest2).normalized().map(|a| a * radius);
// Ensure the new nodes are valid/traversable
if is_traversable(&nearest1, &new_point1) {
kdtree1
.add(&[new_point1.x, new_point1.y, new_point1.z], node_index1)
.unwrap_or_default();
nodes1.push(new_point1);
parents1.insert(node_index1, nearest_index1);
node_index1 += 1;
// Check if the trees connect
if let Ok((check, index)) = kdtree2.nearest_one(
&[new_point1.x, new_point1.y, new_point1.z],
&squared_euclidean,
) {
if check < radius {
let connection = nodes2[*index];
connection2_idx = *index;
nodes1.push(connection);
connection1_idx = nodes1.len() - 1;
parents1.insert(node_index1, node_index1 - 1);
connect = true;
}
}
}
// Repeat the validity check for the second tree
if is_traversable(&nearest2, &new_point2) {
kdtree2
.add(&[new_point2.x, new_point2.y, new_point1.z], node_index2)
.unwrap_or_default();
nodes2.push(new_point2);
parents2.insert(node_index2, nearest_index2);
node_index2 += 1;
// Again check for a connection
if let Ok((check, index)) = kdtree1.nearest_one(
&[new_point2.x, new_point2.y, new_point1.z],
&squared_euclidean,
) {
if check < radius {
let connection = nodes1[*index];
connection1_idx = *index;
nodes2.push(connection);
connection2_idx = nodes2.len() - 1;
parents2.insert(node_index2, node_index2 - 1);
connect = true;
}
}
}
// Increase the search parameter to widen the sample volume
search_parameter += 0.02;
// Maximum of 7000 iterations
for _i in 0..7000 {
if connect {
break;
}
if connect {
// Construct paths from the connection node to the start and end
let mut current_node_index1 = connection1_idx;
while current_node_index1 > 0 {
current_node_index1 = *parents1.get(&current_node_index1).unwrap_or(&0);
path1.push(nodes1[current_node_index1].map(|e| e.floor() as i32));
// Sample a point on the bounding spheroid
let (sampled_point1, sampled_point2) = {
let point = point_on_prolate_spheroid(startf, endf, search_parameter);
(point, point)
};
// Find the nearest nodes to the the sampled point
let nearest_index1 = kdtree1
.nearest_one(
&[sampled_point1.x, sampled_point1.y, sampled_point1.z],
&squared_euclidean,
)
.map_or(0, |n| *n.1);
let nearest_index2 = kdtree2
.nearest_one(
&[sampled_point2.x, sampled_point2.y, sampled_point2.z],
&squared_euclidean,
)
.map_or(0, |n| *n.1);
let nearest1 = nodes1[nearest_index1];
let nearest2 = nodes2[nearest_index2];
// Extend toward the sampled point from the nearest node of each tree
let new_point1 = nearest1 + (sampled_point1 - nearest1).normalized().map(|a| a * radius);
let new_point2 = nearest2 + (sampled_point2 - nearest2).normalized().map(|a| a * radius);
// Ensure the new nodes are valid/traversable
if is_valid_edge(&nearest1, &new_point1) {
kdtree1
.add(&[new_point1.x, new_point1.y, new_point1.z], node_index1)
.unwrap_or_default();
nodes1.push(new_point1);
parents1.insert(node_index1, nearest_index1);
node_index1 += 1;
// Check if the trees connect
if let Ok((check, index)) = kdtree2.nearest_one(
&[new_point1.x, new_point1.y, new_point1.z],
&squared_euclidean,
) {
if check < radius {
let connection = nodes2[*index];
connection2_idx = *index;
nodes1.push(connection);
connection1_idx = nodes1.len() - 1;
parents1.insert(node_index1, node_index1 - 1);
connect = true;
}
}
let mut current_node_index2 = connection2_idx;
while current_node_index2 > 0 {
current_node_index2 = *parents2.get(&current_node_index2).unwrap_or(&0);
path2.push(nodes2[current_node_index2].map(|e| e.floor() as i32));
}
// Repeat the validity check for the second tree
if is_valid_edge(&nearest2, &new_point2) {
kdtree2
.add(&[new_point2.x, new_point2.y, new_point1.z], node_index2)
.unwrap_or_default();
nodes2.push(new_point2);
parents2.insert(node_index2, nearest_index2);
node_index2 += 1;
// Again check for a connection
if let Ok((check, index)) = kdtree1.nearest_one(
&[new_point2.x, new_point2.y, new_point1.z],
&squared_euclidean,
) {
if check < radius {
let connection = nodes1[*index];
connection1_idx = *index;
nodes2.push(connection);
connection2_idx = nodes2.len() - 1;
parents2.insert(node_index2, node_index2 - 1);
connect = true;
}
}
// Join the two paths together in the proper order and remove duplicates
path1.pop();
path1.reverse();
path.append(&mut path1);
path.append(&mut path2);
path.dedup();
} else {
// If the trees did not connect, construct a path from the start to
// the closest node to the end
let mut current_node_index1 = kdtree1
.nearest_one(&[endf.x, endf.y, endf.z], &squared_euclidean)
.map_or(0, |c| *c.1);
// Attempt to pick a node other than the start node
for _i in 0..3 {
if current_node_index1 == 0
|| nodes1[current_node_index1].distance_squared(startf) < 4.0
{
if let Some(index) = parents1.values().choose(&mut thread_rng()) {
current_node_index1 = *index;
} else {
break;
}
}
// Increase the search parameter to widen the sample volume
search_parameter += 0.02;
}
if connect {
// Construct paths from the connection node to the start and end
let mut current_node_index1 = connection1_idx;
while current_node_index1 > 0 {
current_node_index1 = *parents1.get(&current_node_index1).unwrap_or(&0);
path1.push(nodes1[current_node_index1].map(|e| e.floor() as i32));
}
let mut current_node_index2 = connection2_idx;
while current_node_index2 > 0 {
current_node_index2 = *parents2.get(&current_node_index2).unwrap_or(&0);
path2.push(nodes2[current_node_index2].map(|e| e.floor() as i32));
}
// Join the two paths together in the proper order and remove duplicates
path1.pop();
path1.reverse();
path.append(&mut path1);
path.append(&mut path2);
path.dedup();
} else {
// If the trees did not connect, construct a path from the start to
// the closest node to the end
let mut current_node_index1 = kdtree1
.nearest_one(&[endf.x, endf.y, endf.z], &squared_euclidean)
.map_or(0, |c| *c.1);
// Attempt to pick a node other than the start node
for _i in 0..3 {
if current_node_index1 == 0
|| nodes1[current_node_index1].distance_squared(startf) < 4.0
{
if let Some(index) = parents1.values().choose(&mut thread_rng()) {
current_node_index1 = *index;
} else {
break;
}
} else {
break;
}
}
path1.push(nodes1[current_node_index1].map(|e| e.floor() as i32));
// Construct the path
while current_node_index1 != 0 && nodes1[current_node_index1].distance_squared(startf) > 4.0
{
current_node_index1 = *parents1.get(&current_node_index1).unwrap_or(&0);
path1.push(nodes1[current_node_index1].map(|e| e.floor() as i32));
// Construct the path
while current_node_index1 != 0
&& nodes1[current_node_index1].distance_squared(startf) > 4.0
{
current_node_index1 = *parents1.get(&current_node_index1).unwrap_or(&0);
path1.push(nodes1[current_node_index1].map(|e| e.floor() as i32));
}
}
path1.reverse();
path.append(&mut path1);
}
let mut new_path = Vec::new();
let mut node = path[0];
new_path.push(node);
let mut node_idx = 0;
let num_nodes = path.len();
let end = path[num_nodes - 1];
while node != end {
let next_idx = if node_idx + 4 > num_nodes - 1 {
num_nodes - 1
} else {
node_idx + 4
};
let next_node = path[next_idx];
let start_pos = node.map(|e| e as f32 + 0.5);
let end_pos = next_node.map(|e| e as f32 + 0.5);
if vol
.ray(start_pos, end_pos)
.until(Block::is_solid)
.cast()
.0
.powi(2)
> (start_pos).distance_squared(end_pos)
{
node_idx = next_idx;
new_path.push(next_node);
} else {
node_idx += 1;
}
node = path[node_idx];
}
path = new_path;
path1.reverse();
path.append(&mut path1);
}
(Some(path.into_iter().collect()), connect)
let mut new_path = Vec::new();
let mut node = path[0];
new_path.push(node);
let mut node_idx = 0;
let num_nodes = path.len();
let end = path[num_nodes - 1];
while node != end {
let next_idx = if node_idx + 4 > num_nodes - 1 {
num_nodes - 1
} else {
node_idx + 4
};
let next_node = path[next_idx];
let start_pos = node.map(|e| e as f32 + 0.5);
let end_pos = next_node.map(|e| e as f32 + 0.5);
if vol
.ray(start_pos, end_pos)
.until(Block::is_solid)
.cast()
.0
.powi(2)
> (start_pos).distance_squared(end_pos)
{
node_idx = next_idx;
new_path.push(next_node);
} else {
node_idx += 1;
}
node = path[node_idx];
}
path = new_path;
}
/// Returns a random point within a radially symmetrical ellipsoid with given