On the use of generalized force data for kinematically controlled manipulators
| Autor: | Schroeder, Kyle Anthony |
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| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: | |
| Druh dokumentu: | Diplomová práce |
| Popis: | The Department of Energy national laboratories, like Los Alamos National Lab or Sandia National Lab, perform work on radioactive and chemically dangerous materials. Gloveboxes are often used to shield workers from these hazards, but they cannot completely eliminate the danger and often create new safety concerns due to reduced operator dexterity and ergonomic posture. When feasible, robots can be employed to remove the human from the radioactive hazard; allowing them to analyze the situation and make decisions remotely. Force sensor data from the manipulator can be used to simplify the control of these remote systems as well as make them more robust. Much research has been done to develop force and torque control algorithms to introduce compliance or detect collisions. Many of these algorithms are very complicated and currently only implemented in research institutions on torque-controlled manipulators. The literature review discusses many such controllers which have been developed and/or demonstrated. This thesis reviews, develops, and demonstrates several beneficial algorithms which can be implemented on commercially-available kinematically-controlled robots using commercially-available sensors with a reasonable investment of time. Force data is used to improve safety and manage contact forces while kinematically controlling the robot, as well as improve the world model. Safety is improved by detecting anomalous and/or excessive forces during operation. Environmental modeling data is inferred from position and/or force data. A six-axis sensor and joint torque sensors on 2 7DOF manipulators are used to demonstrate the proposed algorithms in two DOE relevant applications: remotely opening an incompletely modeled cabinet door and moving a robot in a confined space. text |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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