Computing Robust Inverse Kinematics Under Uncertainty

TL;DR

This paper introduces a method to compute inverse kinematics solutions that are robust to actuation errors, improving success rates in robotic tasks like pre-grasping and pre-insertion by accounting for uncertainties.

Contribution

It formulates and solves the robust inverse kinematics problem for redundant manipulators with actuation uncertainties, including simulation and experimental validation.

Findings

Robust IK solutions increase task success rates.

Robust IK enables robot self-evaluation of success probability.

Experimental results on a 7-DoF manipulator validate the approach.

Abstract

Robotic tasks, like reaching a pre-grasp configuration, are specified in the end effector space or task space, whereas, robot motion is controlled in joint space. Because of inherent actuation errors in joint space, robots cannot achieve desired configurations in task space exactly. Furthermore, different inverse kinematics (IK) solutions map joint space error set to task space differently. Thus for a given task with a prescribed error tolerance, all IK solutions will not be guaranteed to successfully execute the task. Any IK solution that is guaranteed to execute a task (possibly with high probability) irrespective of the realization of the joint space error is called a robust IK solution. In this paper we formulate and solve the robust inverse kinematics problem for redundant manipulators with actuation uncertainties (errors). We also present simulation and experimental results on a $7$-DoF redundant manipulator for two applications, namely, a pre-grasp positioning and a pre-insertion positioning scenario. Our results show that the robust IK solutions result in higher success rates and also allows the robot to self-evaluate how successful it might be in any application scenario.