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You searched for subject:(Motion Primitives). Showing records 1 – 2 of 2 total matches.

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University of Toronto

1. Kroeze, Zachary. Output Reach Control Problem with Applications to Motion Planning for Robotic Systems.

Degree: PhD, 2019, University of Toronto

As electronic devices become more embedded into everyday products, an increasing number of consumer devices are being equipped with control systems. These control systems are sometimes required to achieve complex specifications such as safety constraints or switching between distinct tasks. Among a wide range of techniques to deal with the control design of complex systems, one is to partition the state space into polytopic regions, and solve a control problem on each region such that the collective behaviour of the hybrid system solves the original complex control specification. In this dissertation we consider such a methodology where the control problem on each polytopic region is called the {\em Reach Control Problem} (RCP). The RCP is to find a state feedback to make the closed-loop trajectories of an affine control system defined on a polytope reach and exit a prescribed facet of the polytope in finite time. There is already an extensive literature on the RCP. This dissertation extends this literature by considering the {\em Output Reach Control Problem} (ORCP). The ORCP is to control the output trajectory of the control system on a simplex in the output space, in contrast to control the entire state trajectory. We also extend the applications of the RCP by developing a unified framework for control with complex specifications. This dissertation contains four distinct contributions. The first contribution provides an extension of classical linear regulator theory to affine systems and exosystems. This extension then provides the basis to develop a framework for the output reach control problem with disturbances. The second contribution leverages existing literature on viability theory to convert the ORCP in the output space on a simplex to a RCP in the full state space on a polytope. The third contribution is to propose a unified framework for solving control problems with complex specifications. Our framework is targeted at robotic systems. Finally, our fourth contribution is a set of so called {\em motion primitives} which comprise one of the layers of our aforementioned framework. These motion primitives are based on our approach to the ORCP. Advisors/Committee Members: Broucke, Mireille E, Electrical and Computer Engineering.

Subjects/Keywords: Integrator Systems; Motion Planning; Motion Primitives; Output Reach Control; Reach Control Problem; 0544

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Kroeze, Z. (2019). Output Reach Control Problem with Applications to Motion Planning for Robotic Systems. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/95882

Chicago Manual of Style (16th Edition):

Kroeze, Zachary. “Output Reach Control Problem with Applications to Motion Planning for Robotic Systems.” 2019. Doctoral Dissertation, University of Toronto. Accessed January 19, 2020. http://hdl.handle.net/1807/95882.

MLA Handbook (7th Edition):

Kroeze, Zachary. “Output Reach Control Problem with Applications to Motion Planning for Robotic Systems.” 2019. Web. 19 Jan 2020.

Vancouver:

Kroeze Z. Output Reach Control Problem with Applications to Motion Planning for Robotic Systems. [Internet] [Doctoral dissertation]. University of Toronto; 2019. [cited 2020 Jan 19]. Available from: http://hdl.handle.net/1807/95882.

Council of Science Editors:

Kroeze Z. Output Reach Control Problem with Applications to Motion Planning for Robotic Systems. [Doctoral Dissertation]. University of Toronto; 2019. Available from: http://hdl.handle.net/1807/95882

2. YUAN JINQIANG. LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING.

Degree: 2019, National University of Singapore

Subjects/Keywords: Learning from Demonstrations; Combined Task and Motion Planning; Manipulationg Planning; Robotic Learning; Dynamic Movement Primitives; Task Planning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

JINQIANG, Y. (2019). LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING. (Thesis). National University of Singapore. Retrieved from https://scholarbank.nus.edu.sg/handle/10635/158088

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

JINQIANG, YUAN. “LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING.” 2019. Thesis, National University of Singapore. Accessed January 19, 2020. https://scholarbank.nus.edu.sg/handle/10635/158088.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

JINQIANG, YUAN. “LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING.” 2019. Web. 19 Jan 2020.

Vancouver:

JINQIANG Y. LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING. [Internet] [Thesis]. National University of Singapore; 2019. [cited 2020 Jan 19]. Available from: https://scholarbank.nus.edu.sg/handle/10635/158088.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

JINQIANG Y. LEARNING ACTIONS FROM DEMONSTRATIONS FOR MANIPULATION TASK PLANNING. [Thesis]. National University of Singapore; 2019. Available from: https://scholarbank.nus.edu.sg/handle/10635/158088

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.