Repository Summary
Checkout URI | https://github.com/PickNikRobotics/pick_ik.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2023-12-13 |
Dev Status | DEVELOPED |
CI status | No Continuous Integration |
Released | RELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
pick_ik | 1.1.0 |
README
pick_ik
pick_ik
is an inverse kinematics (IK) solver compatible with MoveIt 2.
The solver is a reimplementation of bio_ik
, which combines:
* A local optimizer which solves inverse kinematics via gradient descent
* A global optimizer based on evolutionary algorithms
Critically, pick_ik
allows you to specify custom cost functions as discussed in this paper, so you can prioritize additional objectives than simply solving inverse kinematics at a specific frame. For example, you can minimize joint displacement from the initial guess, enforce that joints are close to a particular pose, or even pass custom cost functions to the plugin.
If you are familiar with bio_ik
, the functionality in this package includes:
* Reimplementation of the memetic solver (equivalent to bio1
and bio2_memetic
solvers)
* Reimplementation of the numeric gradient descent solvers (equivalent to gd
, gd_r
, and gd_c
solvers)
* Fully configurable number of threads if using the global solver
* Cost functions on joint displacement, joint centering, and avoiding joint limits
For more details on the implementation, take a look at the paper or the full thesis.
Getting Started
To get started using pick_ik
, refer to the following README files:
CONTRIBUTING
Repository Summary
Checkout URI | https://github.com/PickNikRobotics/pick_ik.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2023-12-13 |
Dev Status | DEVELOPED |
CI status | No Continuous Integration |
Released | RELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
pick_ik | 1.1.0 |
README
pick_ik
pick_ik
is an inverse kinematics (IK) solver compatible with MoveIt 2.
The solver is a reimplementation of bio_ik
, which combines:
* A local optimizer which solves inverse kinematics via gradient descent
* A global optimizer based on evolutionary algorithms
Critically, pick_ik
allows you to specify custom cost functions as discussed in this paper, so you can prioritize additional objectives than simply solving inverse kinematics at a specific frame. For example, you can minimize joint displacement from the initial guess, enforce that joints are close to a particular pose, or even pass custom cost functions to the plugin.
If you are familiar with bio_ik
, the functionality in this package includes:
* Reimplementation of the memetic solver (equivalent to bio1
and bio2_memetic
solvers)
* Reimplementation of the numeric gradient descent solvers (equivalent to gd
, gd_r
, and gd_c
solvers)
* Fully configurable number of threads if using the global solver
* Cost functions on joint displacement, joint centering, and avoiding joint limits
For more details on the implementation, take a look at the paper or the full thesis.
Getting Started
To get started using pick_ik
, refer to the following README files:
CONTRIBUTING
Repository Summary
Checkout URI | https://github.com/PickNikRobotics/pick_ik.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2023-12-13 |
Dev Status | DEVELOPED |
CI status | No Continuous Integration |
Released | RELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
pick_ik | 1.1.0 |
README
pick_ik
pick_ik
is an inverse kinematics (IK) solver compatible with MoveIt 2.
The solver is a reimplementation of bio_ik
, which combines:
* A local optimizer which solves inverse kinematics via gradient descent
* A global optimizer based on evolutionary algorithms
Critically, pick_ik
allows you to specify custom cost functions as discussed in this paper, so you can prioritize additional objectives than simply solving inverse kinematics at a specific frame. For example, you can minimize joint displacement from the initial guess, enforce that joints are close to a particular pose, or even pass custom cost functions to the plugin.
If you are familiar with bio_ik
, the functionality in this package includes:
* Reimplementation of the memetic solver (equivalent to bio1
and bio2_memetic
solvers)
* Reimplementation of the numeric gradient descent solvers (equivalent to gd
, gd_r
, and gd_c
solvers)
* Fully configurable number of threads if using the global solver
* Cost functions on joint displacement, joint centering, and avoiding joint limits
For more details on the implementation, take a look at the paper or the full thesis.
Getting Started
To get started using pick_ik
, refer to the following README files:
CONTRIBUTING
Repository Summary
Checkout URI | https://github.com/PickNikRobotics/pick_ik.git |
VCS Type | git |
VCS Version | main |
Last Updated | 2023-12-13 |
Dev Status | DEVELOPED |
CI status | No Continuous Integration |
Released | RELEASED |
Tags | No category tags. |
Contributing |
Help Wanted (0)
Good First Issues (0) Pull Requests to Review (0) |
Packages
Name | Version |
---|---|
pick_ik | 1.1.0 |
README
pick_ik
pick_ik
is an inverse kinematics (IK) solver compatible with MoveIt 2.
The solver is a reimplementation of bio_ik
, which combines:
* A local optimizer which solves inverse kinematics via gradient descent
* A global optimizer based on evolutionary algorithms
Critically, pick_ik
allows you to specify custom cost functions as discussed in this paper, so you can prioritize additional objectives than simply solving inverse kinematics at a specific frame. For example, you can minimize joint displacement from the initial guess, enforce that joints are close to a particular pose, or even pass custom cost functions to the plugin.
If you are familiar with bio_ik
, the functionality in this package includes:
* Reimplementation of the memetic solver (equivalent to bio1
and bio2_memetic
solvers)
* Reimplementation of the numeric gradient descent solvers (equivalent to gd
, gd_r
, and gd_c
solvers)
* Fully configurable number of threads if using the global solver
* Cost functions on joint displacement, joint centering, and avoiding joint limits
For more details on the implementation, take a look at the paper or the full thesis.
Getting Started
To get started using pick_ik
, refer to the following README files: