This paper investigates the effect of inverse kinematics (IK) on operator performance during the telemanipulation of an industrial robot. Robotic teleoperation is often preferred when manipulating objects in extreme conditions. In many applications, e.g., hazardous and high-consequence environments, operators cannot directly perceive the robot motions and have to rely only on CCTV views of the scene for situational awareness while teleoperating the heavy-duty industrial robots. Making best guesses for the IK plays a significant role on the task success rate and increases the operator cognitive load significantly. In this context, we develop a new optimisation-based IK solver that is robust with respect to the robot's singularities and assists the operator in generating smooth trajectories. Inspired by a successful algorithm used in computer graphics to solve the IK problem and devise smooth movements (FABRIK), our algorithm takes advantage also of the kinematic structure of the robot in order to decouple the notoriously difficult IK problem of orientation and position. To evaluate the effectiveness of the proposed method, we have compared its performance to that of the commonly used Jacobian pseudo inverse-based method in terms of positional accuracy and task-space reachability. We also report the results of telemanipulation experiments with human test-subjects. Our proposed IK algorithm outperforms classical IK methods on both objective metrics of task success, and subjective metrics of operator preference.
|Title of host publication||2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019|
|Publisher||IEEE Computer Society|
|Number of pages||8|
|Publication status||Published - Aug 2019|
|Event||15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada|
Duration: 22 Aug 2019 → 26 Aug 2019
|Name||IEEE International Conference on Automation Science and Engineering|
|Conference||15th IEEE International Conference on Automation Science and Engineering, CASE 2019|
|Period||22/08/19 → 26/08/19|
Bibliographical noteFunding Information:
This work forms part of the UK National Centre for Nuclear Robotics initiative, funded by EPSRC EP/R02572X/1. It is also supported by H2020 RoMaNS 645582, EPSRC EP/P017487/1, and EPSRC EP/P01366X/1 project grants. Stolkin is supported by a Royal Society Industry Fellowship.
© 2019 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.
ASJC Scopus subject areas
- Control and Systems Engineering
- Electrical and Electronic Engineering