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Full text release has been delayed at the author's request until August 16, 2025

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Networks of Saddles to Visualize, Learn, Adjust and Create Branches in Robot State Trajectories

Rouse, Natasha A
http://orcid.org/0000-0001-9929-4026
http://rave.ohiolink.edu/etdc/view?acc_num=case172321055308122

Abstract Details

2024, Doctor of Philosophy, Case Western Reserve University, EMC - Mechanical Engineering.
In robot control, classical stability is formed around a stable point (attractor) or connected stable points (limit cycles). In contrast, connected saddles can be used to describe stable sequences of states. The connection between two saddles in phase space is a heteroclinic channel, and stable heteroclinic channels (SHCs) can be combined to form cycles and networks – stable heteroclinic networks (SHNs). While the stability and subperiod at each saddle have been mathematically predicted, the potential of SHCs as robot controllers has not been fully realized. To move from modelling to control, tools are needed to more precisely design and manipulate these systems. First, this manuscript expands the SHC-framework with a task space transformation inspired by a popular robot control framework – dynamic movement primitives (DMPs). Stable heteroclinic channel-based movement primitives (SMPs) have an intuitive visualization feature that allows users to easily initialize the controller using only the robot’s desired trajectory in its task space. After applying SMPs to a simple robotic system, we characterize the SHC system variables in the larger SMP system, and use the SMP variable nu – the saddle value – for local, real-time controller tuning without compromising the overall stability of the system. Finally, we explore more complex, branching connected-saddle topologies as stable heteroclinic networks. SHCs and SHNs are stochastic systems where noisy external input, such as sensory input, can be used as the stochastic component of the system. For robots, we can use SHNs as a decision-making model where the external input directly drives which decision is made. Overall, this manuscript seeks to parametrize the saddle network frameworks SHCs and SHNs for user-friendly, robust, and versatile robot control. Networks of saddles exist as models for neural activity, neuromechanical models, and robot control, and they can provide further utility in the study and application of biologically-inspired robots.
Kathryn Daltorio (Advisor)
Roger Quinn (Committee Member)
Hillel Chiel (Committee Member)
Murat Cenk Cavusoglu (Committee Member)
109 p.
Mechanical Engineering; Robotics
biologically-inspired robots; motion control; robust control; optimal control; motion planning; cart-and-pendulum; decision making; mutual inhibition; stochastic system; stochastic control

Recommended Citations

Citations

  • Rouse, N. A. (2024). Networks of Saddles to Visualize, Learn, Adjust and Create Branches in Robot State Trajectories [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case172321055308122

    APA Style (7th edition)

  • Rouse, Natasha. Networks of Saddles to Visualize, Learn, Adjust and Create Branches in Robot State Trajectories. 2024. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case172321055308122.

    MLA Style (8th edition)

  • Rouse, Natasha. "Networks of Saddles to Visualize, Learn, Adjust and Create Branches in Robot State Trajectories." Doctoral dissertation, Case Western Reserve University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=case172321055308122

    Chicago Manual of Style (17th edition)

case172321055308122
© 2024, all rights reserved.
This open access ETD is published by Case Western Reserve University School of Graduate Studies and OhioLINK.