Abstract
We present a discrete-optimization technique for finding feasible robot arm trajectories that pass through provided 6-DOF Cartesian-space end-effector paths with high accuracy, a problem called pathwise-inverse kinematics. The output from our method consists of a path function of joint-angles that best follows the provided end-effector path function, given some definition of “best”. Our method, called Stampede, casts the robot motion translation problem as a discrete-space graph-search problem where the nodes in the graph are individually solved for using non-linear optimization; framing the problem in such a way gives rise to a well-structured graph that affords an effective best path calculation using an efficient dynamic-programming algorithm. We present techniques for sampling configuration space, such as diversity sampling and adaptive sampling, to construct the search-space in the graph. Through an evaluation, we show that our approach performs well in finding smooth, feasible, collision-free robot motions that match the input end-effector trace with very high accuracy, while alternative approaches, such as a state-of-the-art per-frame inverse kinematics solver and a global non-linear trajectory-optimization approach, performed unfavorably.
DOI: 10.1109/icra.2019.8793617
BibTex
@inproceedings{Rakita_2019,
doi = {10.1109/icra.2019.8793617},
url = {https://doi.org/10.1109%2Ficra.2019.8793617},
year = 2019,
month = {may},
publisher = {{IEEE}},
author = {Daniel Rakita and Bilge Mutlu and Michael Gleicher},
title = {{STAMPEDE}: A Discrete-Optimization Method for Solving Pathwise-Inverse Kinematics},
booktitle = {2019 International Conference on Robotics and Automation ({ICRA})}
}