mirror of
https://gitlab.com/veloren/veloren.git
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Merge branch 'james/rrt-pathfinding' into 'master'
Initial RRT flight pathfinding See merge request veloren/veloren!2773
This commit is contained in:
commit
e098a38d8e
1
Cargo.lock
generated
1
Cargo.lock
generated
@ -5873,6 +5873,7 @@ dependencies = [
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"fxhash",
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"hashbrown 0.11.2",
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"indexmap",
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"kiddo",
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"lazy_static",
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"num-derive",
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"num-traits",
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|
@ -11,6 +11,7 @@ simd = ["vek/platform_intrinsics"]
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bin_csv = ["ron", "csv", "structopt"]
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bin_graphviz = ["petgraph"]
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bin_cmd_doc_gen = []
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rrt_pathfinding = ["kiddo"]
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default = ["simd"]
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@ -63,6 +64,8 @@ csv = { version = "1.1.3", optional = true }
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structopt = { version = "0.3.13", optional = true }
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# graphviz exporters
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petgraph = { version = "0.5.1", optional = true }
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# K-d trees used for RRT pathfinding
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kiddo = { version = "0.1", optional = true }
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# Data structures
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hashbrown = { version = "0.11", features = ["rayon", "serde", "nightly"] }
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@ -103,4 +106,4 @@ required-features = ["bin_graphviz"]
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[[bin]]
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name = "cmd_doc_gen"
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required-features = ["bin_cmd_doc_gen"]
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required-features = ["bin_cmd_doc_gen"]
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|
@ -5,7 +5,13 @@ use crate::{
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};
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use common_base::span;
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use hashbrown::hash_map::DefaultHashBuilder;
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use rand::prelude::*;
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#[cfg(rrt_pathfinding)] use hashbrown::HashMap;
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#[cfg(rrt_pathfinding)]
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use kiddo::{distance::squared_euclidean, KdTree}; // For RRT paths (disabled for now)
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#[cfg(rrt_pathfinding)]
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use rand::distributions::Uniform;
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use rand::{thread_rng, Rng};
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#[cfg(rrt_pathfinding)] use std::f32::consts::PI;
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use std::iter::FromIterator;
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use vek::*;
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@ -135,9 +141,9 @@ impl Route {
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})
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});
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// Map position of node to middle of block
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let next_tgt = next0.map(|e| e as f32) + Vec3::new(0.5, 0.5, 0.0);
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let closest_tgt = next_tgt.map2(pos, |tgt, pos| pos.clamped(tgt.floor(), tgt.ceil()));
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// Determine whether we're close enough to the next to to consider it completed
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let dist_sqrd = pos.xy().distance_squared(closest_tgt.xy());
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if dist_sqrd
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@ -312,9 +318,9 @@ impl Route {
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Some((
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tgt - pos,
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// Control the entity's speed to hopefully stop us falling off walls on sharp corners.
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// This code is very imperfect: it does its best but it can still fail for particularly
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// fast entities.
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// Control the entity's speed to hopefully stop us falling off walls on sharp
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// corners. This code is very imperfect: it does its best but it
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// can still fail for particularly fast entities.
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straight_factor * traversal_cfg.slow_factor + (1.0 - traversal_cfg.slow_factor),
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))
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.filter(|(bearing, _)| bearing.z < 2.1)
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@ -336,6 +342,9 @@ pub struct Chaser {
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}
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impl Chaser {
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/// Returns bearing and speed
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/// Bearing is a Vec3<f32> dictating the direction of movement
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/// Speed is an f32 between 0.0 and 1.0
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pub fn chase<V>(
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&mut self,
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vol: &V,
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@ -386,12 +395,17 @@ impl Chaser {
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.and_then(|(r, _)| r.traverse(vol, pos, vel, &traversal_cfg))
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}
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} else {
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// There is no route found yet
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None
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};
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// If a bearing has already been determined, use that
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if let Some((bearing, speed)) = bearing {
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Some((bearing, speed))
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} else {
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// Since no bearing has been determined yet, a new route will be
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// calculated if the target has moved, pathfinding is not complete,
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// or there is no route
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let tgt_dir = (tgt - pos).xy().try_normalized().unwrap_or_default();
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// Only search for a path if the target has moved from their last position. We
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@ -406,7 +420,12 @@ impl Chaser {
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{
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self.last_search_tgt = Some(tgt);
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let (path, complete) = find_path(&mut self.astar, vol, pos, tgt, &traversal_cfg);
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// NOTE: Enable air paths when air braking has been figured out
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let (path, complete) = /*if cfg!(rrt_pathfinding) && traversal_cfg.can_fly {
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find_air_path(vol, pos, tgt, &traversal_cfg)
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} else */{
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find_path(&mut self.astar, vol, pos, tgt, &traversal_cfg)
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};
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self.route = path.map(|path| {
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let start_index = path
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@ -429,19 +448,40 @@ impl Chaser {
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)
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});
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}
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let walking_towards_edge = (-3..2).all(|z| {
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vol.get(
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(pos + Vec3::<f32>::from(tgt_dir) * 2.5).map(|e| e as i32) + Vec3::unit_z() * z,
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)
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.map(|b| b.is_air())
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.unwrap_or(false)
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});
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if !walking_towards_edge || traversal_cfg.can_fly {
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Some(((tgt - pos) * Vec3::new(1.0, 1.0, 0.0), 1.0))
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// Start traversing the new route if it exists
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if let Some(bearing) = self
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.route
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.as_mut()
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.and_then(|(r, _)| r.traverse(vol, pos, vel, &traversal_cfg))
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{
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Some(bearing)
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} else {
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None
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// At this point no route is available and no bearing
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// has been determined, so we start sampling terrain.
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// Check for falling off walls and try moving straight
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// towards the target if falling is not a danger
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let walking_towards_edge = (-3..2).all(|z| {
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vol.get(
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(pos + Vec3::<f32>::from(tgt_dir) * 2.5).map(|e| e as i32)
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+ Vec3::unit_z() * z,
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)
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.map(|b| b.is_air())
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.unwrap_or(false)
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});
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// Enable when airbraking/flight is figured out
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/*if traversal_cfg.can_fly {
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Some(((tgt - pos) , 1.0))
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} else */
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if !walking_towards_edge || traversal_cfg.can_fly {
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Some(((tgt - pos) * Vec3::new(1.0, 1.0, 0.0), 1.0))
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} else {
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// This is unfortunately where an NPC will stare blankly
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// into space. No route has been found and no temporary
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// bearing would suffice. Hopefully a route will be found
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// in the coming ticks.
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None
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}
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}
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}
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}
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@ -631,3 +671,399 @@ where
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PathResult::Pending => (None, false),
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}
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}
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// Enable when airbraking/sensible flight is a thing
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#[cfg(rrt_pathfinding)]
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fn find_air_path<V>(
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vol: &V,
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startf: Vec3<f32>,
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endf: Vec3<f32>,
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traversal_cfg: &TraversalConfig,
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) -> (Option<Path<Vec3<i32>>>, bool)
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where
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V: BaseVol<Vox = Block> + ReadVol,
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{
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let radius = traversal_cfg.node_tolerance;
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let mut connect = false;
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let total_dist_sqrd = startf.distance_squared(endf);
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// First check if a straight line path works
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if vol
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.ray(startf + Vec3::unit_z(), endf + Vec3::unit_z())
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.until(Block::is_opaque)
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.cast()
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.0
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.powi(2)
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>= total_dist_sqrd
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{
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let mut path = Vec::new();
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path.push(endf.map(|e| e.floor() as i32));
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connect = true;
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(Some(path.into_iter().collect()), connect)
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// Else use RRTs
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} else {
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let is_traversable = |start: &Vec3<f32>, end: &Vec3<f32>| {
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vol.ray(*start, *end)
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.until(Block::is_solid)
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.cast()
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.0
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.powi(2)
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> (*start).distance_squared(*end)
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//vol.get(*pos).ok().copied().unwrap_or_else(Block::empty).
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// is_fluid();
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};
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informed_rrt_connect(start, end, is_traversable)
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}
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}
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/// Attempts to find a path from a start to the end using an informed
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/// RRT-Connect algorithm. A point is sampled from a bounding spheroid
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/// between the start and end. Two separate rapidly exploring random
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/// trees extend toward the sampled point. Nodes are stored in k-d trees
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/// for quicker nearest node calculations. Points are sampled until the
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/// trees connect. A final path is then reconstructed from the nodes.
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/// This pathfinding algorithm is more appropriate for 3D pathfinding
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/// with wider gaps, such as flying through a forest than for terrain
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/// with narrow gaps, such as navigating a maze.
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/// Returns a path and whether that path is complete or not.
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#[cfg(rrt_pathfinding)]
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fn informed_rrt_connect(
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start: Vec3<f32>,
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end: Vec3<f32>,
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is_valid_edge: impl Fn(&Vec3<f32>, &Vec3<f32>) -> bool,
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) -> (Option<Path<Vec3<i32>>>, bool) {
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let mut path = Vec::new();
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// Each tree has a vector of nodes
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let mut node_index1: usize = 0;
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let mut node_index2: usize = 0;
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let mut nodes1 = Vec::new();
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let mut nodes2 = Vec::new();
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// The parents hashmap stores nodes and their parent nodes as pairs to
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// retrace the complete path once the two RRTs connect
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let mut parents1 = HashMap::new();
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let mut parents2 = HashMap::new();
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// The path vector stores the path from the appropriate terminal to the
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// connecting node or vice versa
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let mut path1 = Vec::new();
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let mut path2 = Vec::new();
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// K-d trees are used to find the closest nodes rapidly
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let mut kdtree1 = KdTree::new();
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let mut kdtree2 = KdTree::new();
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// Add the start as the first node of the first k-d tree
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kdtree1
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.add(&[startf.x, startf.y, startf.z], node_index1)
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.unwrap_or_default();
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nodes1.push(startf);
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node_index1 += 1;
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// Add the end as the first node of the second k-d tree
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kdtree2
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.add(&[endf.x, endf.y, endf.z], node_index2)
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.unwrap_or_default();
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nodes2.push(endf);
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node_index2 += 1;
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let mut connection1_idx = 0;
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let mut connection2_idx = 0;
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let mut connect = false;
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// Scalar non-dimensional value that is proportional to the size of the
|
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// sample spheroid volume. This increases in value until a path is found.
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let mut search_parameter = 0.01;
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// Maximum of 7000 iterations
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for _i in 0..7000 {
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if connect {
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break;
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}
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// Sample a point on the bounding spheroid
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let (sampled_point1, sampled_point2) = {
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let point = point_on_prolate_spheroid(startf, endf, search_parameter);
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(point, point)
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};
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// Find the nearest nodes to the the sampled point
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let nearest_index1 = kdtree1
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.nearest_one(
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&[sampled_point1.x, sampled_point1.y, sampled_point1.z],
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&squared_euclidean,
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)
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.map_or(0, |n| *n.1);
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let nearest_index2 = kdtree2
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.nearest_one(
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&[sampled_point2.x, sampled_point2.y, sampled_point2.z],
|
||||
&squared_euclidean,
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)
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.map_or(0, |n| *n.1);
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let nearest1 = nodes1[nearest_index1];
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let nearest2 = nodes2[nearest_index2];
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// Extend toward the sampled point from the nearest node of each tree
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let new_point1 = nearest1 + (sampled_point1 - nearest1).normalized().map(|a| a * radius);
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let new_point2 = nearest2 + (sampled_point2 - nearest2).normalized().map(|a| a * radius);
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|
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// Ensure the new nodes are valid/traversable
|
||||
if is_valid_edge(&nearest1, &new_point1) {
|
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kdtree1
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.add(&[new_point1.x, new_point1.y, new_point1.z], node_index1)
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.unwrap_or_default();
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nodes1.push(new_point1);
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parents1.insert(node_index1, nearest_index1);
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node_index1 += 1;
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// Check if the trees connect
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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];
|
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connection2_idx = *index;
|
||||
nodes1.push(connection);
|
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connection1_idx = nodes1.len() - 1;
|
||||
parents1.insert(node_index1, node_index1 - 1);
|
||||
connect = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 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;
|
||||
}
|
||||
}
|
||||
}
|
||||
// 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(¤t_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(¤t_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(¤t_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;
|
||||
}
|
||||
|
||||
/// Returns a random point within a radially symmetrical ellipsoid with given
|
||||
/// foci and a `search parameter` to determine the size of the ellipse beyond
|
||||
/// the foci. Technically the point is within a prolate spheroid translated and
|
||||
/// rotated to the proper place in cartesian space.
|
||||
/// The search_parameter is a float that relates to the length of the string for
|
||||
/// a two dimensional ellipse or the size of the ellipse beyond the foci. In
|
||||
/// this case that analogy still holds as the ellipse is radially symmetrical
|
||||
/// along the axis between the foci. The value of the search parameter must be
|
||||
/// greater than zero. In order to increase the sample area, the
|
||||
/// search_parameter should be increased linearly as the search continues.
|
||||
#[allow(clippy::many_single_char_names)]
|
||||
#[cfg(rrt_pathfinding)]
|
||||
pub fn point_on_prolate_spheroid(
|
||||
focus1: Vec3<f32>,
|
||||
focus2: Vec3<f32>,
|
||||
search_parameter: f32,
|
||||
) -> Vec3<f32> {
|
||||
let mut rng = thread_rng();
|
||||
// Uniform distribution
|
||||
let range = Uniform::from(0.0..1.0);
|
||||
|
||||
// Midpoint is used as the local origin
|
||||
let midpoint = 0.5 * (focus1 + focus2);
|
||||
// Radius between the start and end of the path
|
||||
let radius: f32 = focus1.distance(focus2);
|
||||
// The linear eccentricity of an ellipse is the distance from the origin to a
|
||||
// focus A prolate spheroid is a half-ellipse rotated for a full revolution
|
||||
// which is why ellipse variables are used frequently in this function
|
||||
let linear_eccentricity: f32 = 0.5 * radius;
|
||||
|
||||
// For an ellipsoid, three variables determine the shape: a, b, and c.
|
||||
// These are the distance from the center/origin to the surface on the
|
||||
// x, y, and z axes, respectively.
|
||||
// For a prolate spheroid a and b are equal.
|
||||
// c is determined by adding the search parameter to the linear eccentricity.
|
||||
// As the search parameter increases the size of the spheroid increases
|
||||
let c: f32 = linear_eccentricity + search_parameter;
|
||||
// The width is calculated to prioritize increasing width over length of
|
||||
// the ellipsoid
|
||||
let a: f32 = (c.powi(2) - linear_eccentricity.powi(2)).powf(0.5);
|
||||
// The width should be the same in both the x and y directions
|
||||
let b: f32 = a;
|
||||
|
||||
// The parametric spherical equation for an ellipsoid measuring from the
|
||||
// center point is as follows:
|
||||
// x = a * cos(theta) * cos(lambda)
|
||||
// y = b * cos(theta) * sin(lambda)
|
||||
// z = c * sin(theta)
|
||||
//
|
||||
// where -0.5 * PI <= theta <= 0.5 * PI
|
||||
// and 0.0 <= lambda < 2.0 * PI
|
||||
//
|
||||
// Select these two angles using the uniform distribution defined at the
|
||||
// beginning of the function from 0.0 to 1.0
|
||||
let rtheta: f32 = PI * range.sample(&mut rng) - 0.5 * PI;
|
||||
let lambda: f32 = 2.0 * PI * range.sample(&mut rng);
|
||||
// Select a point on the surface of the ellipsoid
|
||||
let point = Vec3::new(
|
||||
a * rtheta.cos() * lambda.cos(),
|
||||
b * rtheta.cos() * lambda.sin(),
|
||||
c * rtheta.sin(),
|
||||
);
|
||||
// NOTE: Theoretically we should sample a point within the spheroid
|
||||
// requiring selecting a point along the radius. In my tests selecting
|
||||
// a point *on the surface* of the spheroid results in sampling that is
|
||||
// "good enough". The following code is commented out to reduce expense.
|
||||
//let surface_point = Vec3::new(a * rtheta.cos() * lambda.cos(), b *
|
||||
// rtheta.cos() * lambda.sin(), c * rtheta.sin()); let magnitude =
|
||||
// surface_point.magnitude(); let direction = surface_point.normalized();
|
||||
//// Randomly select a point along the vector to the previously selected surface
|
||||
//// point using the uniform distribution
|
||||
//let point = magnitude * range.sample(&mut rng) * direction;
|
||||
|
||||
// Now that a point has been selected in local space, it must be rotated and
|
||||
// translated into global coordinates
|
||||
// NOTE: Don't rotate about the z axis as the point is already randomly
|
||||
// selected about the z axis
|
||||
//let dx = focus2.x - focus1.x;
|
||||
//let dy = focus2.y - focus1.y;
|
||||
let dz = focus2.z - focus1.z;
|
||||
// Phi and theta are the angles from the x axis in the x-y plane and from
|
||||
// the z axis, respectively. (As found in spherical coordinates)
|
||||
// These angles are used to rotate the random point in the spheroid about
|
||||
// the local origin
|
||||
//
|
||||
// Rotate about z axis by phi
|
||||
//let phi: f32 = if dx.abs() > 0.0 {
|
||||
// (dy / dx).atan()
|
||||
//} else {
|
||||
// 0.5 * PI
|
||||
//};
|
||||
// This is unnecessary as rtheta is randomly selected between 0.0 and 2.0 * PI
|
||||
// let rot_z_mat = Mat3::new(phi.cos(), -1.0 * phi.sin(), 0.0, phi.sin(),
|
||||
// phi.cos(), 0.0, 0.0, 0.0, 1.0);
|
||||
|
||||
// Rotate about perpendicular vector in the xy plane by theta
|
||||
let theta: f32 = if radius > 0.0 {
|
||||
(dz / radius).acos()
|
||||
} else {
|
||||
0.0
|
||||
};
|
||||
// Vector from focus1 to focus2
|
||||
let r_vec = focus2 - focus1;
|
||||
// Perpendicular vector in xy plane
|
||||
let perp_vec = Vec3::new(-1.0 * r_vec.y, r_vec.x, 0.0).normalized();
|
||||
let l = perp_vec.x;
|
||||
let m = perp_vec.y;
|
||||
let n = perp_vec.z;
|
||||
// Rotation matrix for rotation about a vector
|
||||
let rot_2_mat = Mat3::new(
|
||||
l * l * (1.0 - theta.cos()),
|
||||
m * l * (1.0 - theta.cos()) - n * theta.sin(),
|
||||
n * l * (1.0 - theta.cos()) + m * theta.sin(),
|
||||
l * m * (1.0 - theta.cos()) + n * theta.sin(),
|
||||
m * m * (1.0 - theta.cos()) + theta.cos(),
|
||||
n * m * (1.0 - theta.cos()) - l * theta.sin(),
|
||||
l * n * (1.0 - theta.cos()) - m * theta.sin(),
|
||||
m * n * (1.0 - theta.cos()) + l * theta.sin(),
|
||||
n * n * (1.0 - theta.cos()) + theta.cos(),
|
||||
);
|
||||
|
||||
// Get the global coordinates of the point by rotating and adding the origin
|
||||
// rot_z_mat is unneeded due to the random rotation defined by lambda
|
||||
// let global_coords = midpoint + rot_2_mat * (rot_z_mat * point);
|
||||
midpoint + rot_2_mat * point
|
||||
}
|
||||
|
Loading…
Reference in New Issue
Block a user