Thesis
Cooperative mobile robot and manipulator system for autonomous manufacturing
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2024
- Thesis identifier
- T16958
- Person Identifier (Local)
- 201864679
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- Autonomous manufacturing is broadly defined as a set of manufacturing practices with the wide adoption of advanced autonomous technologies, e.g., autonomous robotic systems. With the purpose of shaping future manufacturing operations, autonomous manufacturing plays a pivotal role on the fourth industrial evolution. In advanced manufacturing contexts, fixed, static and inflexible transportation systems such as conveyor belt refrain the greater performances and efficiency of large-scale fixed-base robotic manipulators in autonomous manufacturing. Mobile robots, as flexible and movable platforms, can be cooperated with the fixed-base robotic manipulator to perform both material transportation and material handling tasks at production lines. However, due to the many technologies involved, material transportation by the mobile robot and material handling by the fixed-base robotic manipulator have not been studied on how to develop a holistic, integrated, cooperative and autonomous robotic system. To solve this challenging issue, a strategy that is capable of seamlessly integrating different modules into a cooperative mobile robot and manipulator system (Co-MRMS) and provides autonomous material transportation with sufficiently accurate and robust capabilities for material positioning, is required to investigate. This thesis deals with the system integration and performance improvement for a Co-MRMS which contains a fixed-base robotic manipulator and a mobile robot. Compared to previous works, this research is focused on the specific challenges arising from heterogeneous robots that must be coordinated along with the complex set of tasks required for autonomous manufacturing applications. In this thesis, a new integrated simulation framework is proposed to comprehensively demonstrate the cooperative concepts of the whole proposed Co-MRMS which integrates a mobile robot with a fixed-base manipulator. Furthermore, to validate the feasibility of the proposed Co-MRMS, a case study on robotic material transportation and composite lay-up, which is based on a real-world scenario commonly found in advanced composite manufacturing, is investigated. The simulation-based results demonstrate promising features of the proposed CoMRMS. From this simulation-based case study, a flexible and efficient interaction mode is designed for the proposed Co-MRMS and a novel positioning system is developed for the relative positioning between the mobile robot and the fixed-base robotic manipulator. In the developed positioning system, a new two-stage positioning framework with multi-sensor fusion positioning is proposed, which contains two different kinds of localization approaches for the robotic manipulator continually perceiving the mobile robot. One positioning approach is ultrasonic sensors fused with an inertial measurement unit (IMU) by using the filtered extended Kalman filter (EKF) algorithm and another positioning approach is vision-based positioning by identifying ArUco marker. Crucially, to ensure the robustness of positioning, a seamless switching strategy for the robotic manipulator to relocalize the mobile robot is presented for the case in which the vision sensor fails. Another contribution in this thesis is the performance improvement of advanced visual-based simultaneous localization and mapping (SLAM) system in scenarios that contain blurred frames by integrating an efficient image deblurring framework, which can be used for the phase of autonomous material transport. The conventional localization systems in manufacturing rely on external setups such as ArUco marker, lacking of sufficient flexibility to adapt to autonomous manufacturing of the future. Visual-based SLAM enables autonomous robotic behaviors which allow the mobile robot to adequately handle dynamic environments by visual sensors. It takes advantage of the natural markers from the around environment and allows the mobile robot to move autonomously toward the target. However, challenges arise when applying visual-based SLAM in the practices of autonomous manufacturing. Particularly, blurred images that exist in visual-based SLAM can result in low-quality outcomes and are thus studied in this work. The proposed efficient image deblurring framework is feasible in real-world scenarios and incrementally enhances the positioning accuracy of visual-based SLAM according to the results in the TUM RGBD dataset and TUM Visual-Inertial dataset. At last, a physical prototyping Co-MRMS, which is basically comprised of a fixed-base robotic manipulator and a mobile robot was developed. Cooperative behaviors and handling tasks to advance composite manufacturing serve as a case study. Abundant evaluations through a series of tasks were performed to evaluate the performance of individual components of the proposed Co-MRMS through a small-scale robotic cell consisting of a 6 degrees of freedom manipulator and a Turtlebot3 Burger mobile robot. An effective machine vision system has been developed to support the robotic tasks described above by providing the capabilities for object detection, localization and fiber orientation detection and dealing with uncertainties such as size and shape of fiber plies. In conclusion, by exploiting the availability of the proposed Co-MRMS, it is possible to implement a flexible system that provides autonomous material transportation and sufficiently-accurate material handling capabilities that extend beyond what is currently adopted in the industry.
- Advisor / supervisor
- Yang, Erfu
- Resource Type
- DOI
- Funder
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PDF of thesis T16958 | 2024-06-11 | Público | Descargar |