| Author: | Lee, Hoi Yin |
| Title: | Interaction and control in robotics : from assistance to collaborative manipulation |
| Advisors: | Navarro-Alarcon, David (ME) |
| Degree: | Ph.D. |
| Year: | 2024 |
| Subject: | Human-machine systems Robots -- Control systems Robotics Robotics -- Human factors Welding -- Automation Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Mechanical Engineering |
| Pages: | iii, xxii, 168 pages : color illustrations |
| Language: | English |
| Abstract: | Throughout human history, we have collaborated with one another to accomplish more challenging tasks together. With technological advancements, humans have invented a myriad of machines and robots to assist in our daily lives and tasks. Nowadays, we are accustomed to working alongside robots, such as adopting robotic arms to move large metal pieces in car manufacturing. Beyond using robots to assist humans, researchers have also focused on enabling robots to collaborate and work with one another, introducing multi-robot systems. To further enhance the intelligence of robots and minimize the differences between human and machine, researchers have also developed robots that can understand and use human tools to manipulate objects while carrying out tasks. This thesis focuses on the interactions and controls in robotics, exploring various aspects from perception and capability sharing in multi-robot collaboration to collaborative manipulation through tool usage. To investigate potential application scenarios, the work includes three case studies: (1) Welder training assistant with augmented perception, (2) Capability sharing in heterogeneous multi-robot systems, and (3) Non-prehensile tool manipulation. In the welder training assistant project, a multi-sensor interface was developed to assist humans in teaching and learning arc welding more efficiently through performance visualization and quantification. For the multi-robot system, a distributed ontological collaborative task allocation framework was proposed, focusing on allocating tasks among robots based on their capabilities. In the tool manipulation project, a non-prehensile tool manipulation methodology was developed, utilizing a Large Language Model for task decomposition. To enable fine tool motion correction with objects confined in a limited area, an incremental stepping manipulation approach was also designed. The proposed methodologies are validated and analyzed through extensive experiments to demonstrate the efficiency and effectiveness of the developed solutions in real-world scenarios. |
| Rights: | All rights reserved |
| Access: | open access |
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