2026-09-18T21:26:58Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1553662024-06-28T03:40:54Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Fan, Chuchu
Yu, Mingxin
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
2024-06-27T19:48:09Z
2024-06-27T19:48:09Z
2024-05
2024-05-28T19:36:16.663Z
https://hdl.handle.net/1721.1/155366
0000-0002-5274-7451
Manipulating rigid body objects in crowded environments poses significant challenges due to the need for rapid, real-time planning and the assurance of safe operational paths.
The challenges come from varying shapes of the manipulated objects and high-dimensional nature of manipulators.
This thesis addresses these issues by developing (1) a mixed-integer linear programming (MILP)-based approach to plan safe paths for rigid-body objects; and (2) a learned control barrier function (CBF) tailored for manipulators with multiple degrees of freedom (DoF) and an associated framework CBF-RRT to enable efficient planning for robotic manipulators. Comprehensive experimental results have shown that the proposed methods outperform baseline methods, providing tools for improving the safety and efficiency of robotic manipulators in complex environments.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright retained by author(s)
https://rightsstatements.org/page/InC-EDU/1.0/
Safe and Efficient Motion Planning in Robotic Manipulation through Formal Methods
Thesis
application/pdf
Master
Master of Science in Aeronautics and Astronautics
Publication
unknown
unknown
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<Abstract>Manipulating rigid body objects in crowded environments poses significant challenges due to the need for rapid, real-time planning and the assurance of safe operational paths.
The challenges come from varying shapes of the manipulated objects and high-dimensional nature of manipulators.
This thesis addresses these issues by developing (1) a mixed-integer linear programming (MILP)-based approach to plan safe paths for rigid-body objects; and (2) a learned control barrier function (CBF) tailored for manipulators with multiple degrees of freedom (DoF) and an associated framework CBF-RRT to enable efficient planning for robotic manipulators. Comprehensive experimental results have shown that the proposed methods outperform baseline methods, providing tools for improving the safety and efficiency of robotic manipulators in complex environments.</Abstract>
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