E-Thesis 295 views 49 downloads
Real-Time Hybrid Visual Servoing of a Redundant Manipulator via Deep Reinforcement Learning / Alex Williams
Swansea University Author: Alex Williams
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PDF | E-Thesis – open access
Copyright: The author, Alexander J. Williams, 2023. Released under the terms of a Creative Commons Attribution-Only (CC-BY) License. Third party content is excluded for use under the license terms.
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Abstract
Fixtureless assembly may be necessary in some manufacturing tasks and environ-ments due to various constraints but poses challenges for automation due to non-deterministic characteristics not favoured by traditional approaches to industrial au-tomation. Visual servoing methods of robotic control cou...
| Published: |
Swansea
2023
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| Institution: | Swansea University |
| Degree level: | Master of Research |
| Degree name: | MSc by Research |
| Supervisor: | Griffiths, Christian ; Cameron, Ian |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa62598 |
| Tags: | Add Tag |
| Abstract: |
Fixtureless assembly may be necessary in some manufacturing tasks and environ-ments due to various constraints but poses challenges for automation due to non-deterministic characteristics not favoured by traditional approaches to industrial au-tomation. Visual servoing methods of robotic control could be effective for sensitive manipulation tasks where the desired end-effector pose can be ascertained via visual cues. Visual data is complex and computationally expensive to process but deep reinforcement learning has shown promise for robotic control in vision-based manipu-lation tasks. However, these methods are rarely used in industry due to the resources and expertise required to develop application-specific systems and prohibitive train-ing costs. Training reinforcement learning models in simulated environments offers a number of benefits for the development of robust robotic control algorithms by reducing training time and costs, and providing repeatable benchmarks for which algorithms can be tested, developed and eventually deployed on real robotic control environments. In this work, we present a new simulated reinforcement learning envi-ronment for developing accurate robotic manipulation control systems in fixtureless environments. Our environment incorporates a contemporary collaborative industrial robot, the KUKA LBR iiwa, with the goal of positioning its end effector in a generic fixtureless environment based on a visual cue. Observational inputs are comprised of the robotic joint positions and velocities, as well as two cameras, whose positioning reflect hybrid visual servoing with one camera attached to the robotic end-effector, and another observing the workspace respectively. We propose a state-of-the-art deep reinforcement learning approach to solving the task environment and make prelimi-nary assessments of the efficacy of this approach to hybrid visual servoing methods for the defined problem environment. We also conduct a series of experiments ex-ploring the hyperparameter space in the proposed reinforcement learning method. Although we could not prove the efficacy of a deep reinforcement approach to solving the task environment with our initial results, we remain confident that such an ap-proach could be feasible to solving this industrial manufacturing challenge and that our contributions in this work in terms of the novel software provide a good basis for the exploration of reinforcement learning approaches to hybrid visual servoing in accurate manufacturing contexts. |
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| Item Description: |
Research data URL:https://github.com/alexjameswilliams/KukaHybridVisualServoinghttps://www.comet.ml/alexjameswilliams/ kukahybridvs/ ; ORCiD identifier: https://orcid.org/0000-0003-2387-6876 |
| Keywords: |
Artificial Intelligence, Robotics, Reinforcement Learning, Machine Learning, Manufacturing Systems Engineering, Aerospace Manufacturing |
| College: |
Faculty of Science and Engineering |