In an attempt to demonstrate how KDL works well with some platforms (like the PR2), versus when it creates issues for other platforms (like the Jaco-2 arm), I've started compiling a small table of IK results for various platforms of KDL versus TRAC-IK. https://bitbucket.org/traclabs/trac_ik If you see issues with KDL or MoveIt! with your manipulation hardware, I can run my tests on your URDF and add it to the results table. Coming soon: the cleaned up executable that will allow anyone to run these tests themselves for verification. On 11/05/2015 04:11 PM, Patrick Beeson 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. > -- Patrick Beeson Senior Scientist TRACLabs Inc. _______________________________________________ ros-users mailing list ros-users@lists.ros.org http://lists.ros.org/mailman/listinfo/ros-users