Struct k::JacobianIkSolver [−][src]
pub struct JacobianIkSolver<T: RealField> { pub allowable_target_distance: T, pub allowable_target_angle: T, pub jacobian_multiplier: T, pub num_max_try: usize, // some fields omitted }
Expand description
Inverse Kinematics Solver using Jacobian matrix
Fields
allowable_target_distance: T
If the distance is smaller than this value, it is reached.
allowable_target_angle: T
If the angle distance is smaller than this value, it is reached.
jacobian_multiplier: T
multiplier for jacobian
num_max_try: usize
How many times the joints are tried to be moved
Implementations
pub fn new(
allowable_target_distance: T,
allowable_target_angle: T,
jacobian_multiplier: T,
num_max_try: usize
) -> JacobianIkSolver<T>
pub fn new(
allowable_target_distance: T,
allowable_target_angle: T,
jacobian_multiplier: T,
num_max_try: usize
) -> JacobianIkSolver<T>
Create instance of JacobianIkSolver
.
JacobianIkSolverBuilder
is available instead of calling this new
method.
Examples
let solver = k::JacobianIkSolver::new(0.01, 0.01, 0.5, 100);
Set a null space function for redundant manipulator.
Examples
let mut solver = k::JacobianIkSolver::new(0.01, 0.01, 0.5, 100); solver.set_nullspace_function(Box::new( k::create_reference_positions_nullspace_function( vec![0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], vec![0.1, 0.1, 0.1, 1.0, 0.1, 0.5, 0.0], ), ));
Clear the null function which is set by set_nullspace_function
.
Trait Implementations
Set joint positions of arm
to reach the target_pose
Examples
use k::prelude::*; let chain = k::Chain::<f32>::from_urdf_file("urdf/sample.urdf").unwrap(); // Create sub-`Chain` to make it easy to use inverse kinematics let target_joint_name = "r_wrist_pitch"; let r_wrist = chain.find(target_joint_name).unwrap(); let mut arm = k::SerialChain::from_end(r_wrist); println!("arm: {}", arm); // Set joint positions of `arm` let positions = vec![0.1, 0.2, 0.0, -0.5, 0.0, -0.3]; arm.set_joint_positions(&positions).unwrap(); println!("initial positions={:?}", arm.joint_positions()); // Get the transform of the end of the manipulator (forward kinematics) let mut target = arm.update_transforms().last().unwrap().clone(); println!("initial target pos = {}", target.translation); println!("move x: -0.1"); target.translation.vector.x -= 0.1; // Create IK solver with default settings let solver = k::JacobianIkSolver::default(); // solve and move the manipulator positions solver.solve(&arm, &target).unwrap(); println!("solved positions={:?}", arm.joint_positions());
fn solve_with_constraints(
&self,
arm: &SerialChain<T>,
target_pose: &Isometry3<T>,
constraints: &Constraints
) -> Result<(), Error>
fn solve_with_constraints(
&self,
arm: &SerialChain<T>,
target_pose: &Isometry3<T>,
constraints: &Constraints
) -> Result<(), Error>
Set joint positions of arm
to reach the target_pose
with constraints
If you want to loose the constraints, use this method. For example, ignoring rotation with an axis. It enables to use the arms which has less than six DoF.
Example
use k::prelude::*; let chain = k::Chain::<f32>::from_urdf_file("urdf/sample.urdf").unwrap(); let target_joint_name = "r_wrist_pitch"; let r_wrist = chain.find(target_joint_name).unwrap(); let mut arm = k::SerialChain::from_end(r_wrist); let positions = vec![0.1, 0.2, 0.0, -0.5, 0.0, -0.3]; arm.set_joint_positions(&positions).unwrap(); let mut target = arm.update_transforms().last().unwrap().clone(); target.translation.vector.x -= 0.1; let solver = k::JacobianIkSolver::default(); let mut constraints = k::Constraints::default(); constraints.rotation_x = false; constraints.rotation_z = false; solver .solve_with_constraints(&arm, &target, &constraints) .unwrap_or_else(|err| { println!("Err: {}", err); });
Auto Trait Implementations
impl<T> !RefUnwindSafe for JacobianIkSolver<T>
impl<T> Send for JacobianIkSolver<T>
impl<T> Sync for JacobianIkSolver<T>
impl<T> Unpin for JacobianIkSolver<T> where
T: Unpin,
impl<T> !UnwindSafe for JacobianIkSolver<T>
Blanket Implementations
Mutably borrows from an owned value. Read more
type Output = T
type Output = T
Should always be Self
The inverse inclusion map: attempts to construct self
from the equivalent element of its
superset. Read more
Checks if self
is actually part of its subset T
(and can be converted to it).
Use with care! Same as self.to_subset
but without any property checks. Always succeeds.
The inclusion map: converts self
to the equivalent element of its superset.