Murphey:Murphey Projects
From RoboticsWiki
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Autonomous Vehicles
Support: National Science Foundation CAREER award
Student: Timothy Caldwell
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This work focuses on control and estimation for vehicles that must slip in order to turn. They have the unusual property of being controllable in a hybrid-systems sense, but they are not locally controllable with bounded inputs. Hence, the hybrid structure of the dynamics must be taken into account. We are implementing our results on the autonomous vehicle seen to the right.
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Multiple Point Contact and Distributed Manipulation
Support: National Science Foundation CAREER award
Student: Matthew Travers
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Mechanical grasping is a traditional area of study in robotics. However, when the mechanism is highly constrained, the contact will undergo stick/slip transitions and contact/no-contact transitions that must be taken into account for control purposes. The experimental system to the right allows us to investigate these effects in a dynamic setting, unlike the PI's prior work in distributed manipulation that was effectively kinematic.
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Cooperative Control and Cooperative Manipulation
Collaborators: John Bennett in Computer Science at the University of Colorado
Students: Brian Shucker (now at MIT Lincoln Labs) and Matanya Horowitz
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Cooperative control has been widely studied in the past decade, but cooperative manipulation has received comparatively little attention. My interests in both areas are to show that "simple" controllers can achieve the desired objectives in most cases, and in particular to show that many of the current cooperative control techniques that involve nonsmooth analysis may be replaced by standard adaptive control techniques without any theoretical loss.
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MarioNET
Support: National Science Foundation CreativeIT program
Collaborators: Magnus Egerstedt in Electrical Engineering at Georgia Tech and Mathieu Desbrun at Caltech
Student: Elliot Johnson
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Marionettes are high degree-of-freedom (40-50 DOF) systems that are extremely nonlinear with degenerate Lagrangians. Moreover, they have a strongly geometric structure. We are using them as a test-bed for studying the theoretical foundations of combining classical Riemannian geometry with graph theoretical techniques for efficient simulation and optimization of complex systems. As an embedded control system, marionettes provide challenges in simulation, optimal control, hybrid control, and networking. We are solving these issues in a unified setting using the marionettes as an example, but the developed techniques are generically applicable to networked nonlinear systems. Plus, they are fun to play with!
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Part Manipulation and Self-Assembly
Collaborators: Kevin Lynch in Mechanical Engineering at Northwestern University
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Neuro-Muscular Structure of Human Manipulation
Collaborators: Francisco Valero-Cuevas at USC
Student: Sophia Del Rio
The goal of this initial project is to understand how the neuro-muscular structure of the hand reflects the geometric structures we find in manipulation planning and control.
Networked Data association and Estimation on Riemannian manifolds
Collaborators: Lucy Pao (University of Colorado)
Student: Matthew Travers
coming soon




