Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/83546
Title: | Time-optimal motion planning and control | Authors: | Pham, Tien Hung | Keywords: | Engineering::Mechanical engineering::Robots | Issue Date: | 2019 | Source: | Pham, T. H. (2019). Time-optimal motion planning and control. Doctoral thesis, Nanyang Technological University, Singapore. | Abstract: | The recent years have seen rapid growth in the adoption of robotics technologies. This welcoming development has led to increasingly complex applications with stringent requirements, motivating research on time-optimal motion planning and control for robots. This thesis presents developments that extend the state-of-the-art in time-optimal motion planning and control for robots. I first revisit a classical problem in the robotic literature--computing the Time-Optimal Path Parameterization along a specified path--which was posed more than 30 years ago by (Bobrow, 1985). The presented new approach to the problem, as suggested by experimental evaluations, outperforms existing solutions in both computational complexity and robustness. Next, I discuss an experimental case study of an industrial task: planning critically fast motions for robots transporting objects with suction cups. Experimental results suggest that by appropriately modelling of ``suction cup constraints'', one can control an industrial robot at high speed (near the robot hardware speed limit) and still achieve 100\% transport success rate. Finally, I discuss and present solutions to two issues that are commonly associated with the use of Time-Optimal Path Parameterizations: (i) the existence of switching points with infinite joint jerk and (ii) the poor regulation of tracking error. | URI: | https://hdl.handle.net/10356/83546 http://hdl.handle.net/10220/49772 |
DOI: | 10.32657/10220/49772 | Schools: | School of Mechanical and Aerospace Engineering | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | MAE Theses |
Page view(s) 50
581
Updated on Mar 27, 2024
Download(s) 1
1,178
Updated on Mar 27, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.