Hi Patrick,

Not having looked at your code at all, can I make a request? I have some joints that are not simple chains, and thus are not handled by any IK solver I know of ... but should be.

The joint I have is a three-legged table; one of the legs is fixed, the other two are driven by linkages; I need to be able to know joint angles, given a desired table surface position and orientation.

I currently solve this using some fairly simple hand-written code, integrated in a most awful hacky way, but it would be nice to see this supported in an IK solver.

--linas

On Fri, Nov 6, 2015 at 6:11 AM, Patrick Beeson via ros-users <ros-users@lists.ros.org> wrote:

TRACLabs Inc. is glad to announce the public release of our Inverse Kinematics solver TRAC-IK. TRAC-IK is a faster, significantly more reliable drop-in replacement for KDL's pseudoinverse Jacobian solver.

Source (including a MoveIt! plugin) can be found at:
https://bitbucket.org/traclabs/trac_ik.git

TRAC-IK has a very similar API to KDL's IK solver calls, except that the user passes a maximum time instead of a maximum number of search iterations. Additionally, TRAC-IK allows for error tolerances to be set independently for each Cartesian dimension (x,y,z,roll,pitch.yaw).

More details:

KDL's joint-limited pseudoinverse Jacobian implementation is the solver used by various ROS packages and MoveIt! for generic manipulation chains. In our research with Atlas humanoids in the DARPA Robotics Challenge and with NASA's Robotnaut-2 and Valkyrie humanoids, TRACLabs researchers experienced a high amount of solve errors when using KDL's inverse kinematics functions on robotic arms. We tracked the issues down to the fact that theoretically-sound Newton methods fail in the face of joint limits. As such, we have created TRAC-IK that concurrently runs two different IK methods: 1) an enhancment of KDL's solver (which detects and mitigates local minima that can occur when joint limits are encountered during gradient descent) and 2) a Sequential Quadratic Programming IK formulation that uses quasi-Newton methods that are known to better handle non-smooth search spaces. The results have been very positive. By combing the two approaches together, TRAC-IK outperforms both standalone IK methods with no additional overhead in runtime for small chains, and significant improvements in time for large chains.

Details can be found here in our Humanoids 2015 paper here:
https://personal.traclabs.com/~pbeeson/publications/b2hd-Beeson-humanoids-15.html

A few high-level results are shown in the attached (low-res) figure.

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