Graduation Year
2014
Document Type
Thesis
Degree
M.S.Cp.
Degree Name
MS in Computer Engineering (M.S.C.P.)
Department
Computer Engineering
Degree Granting Department
Computer Science & Engineering
Major Professor
Redwan Alqasemi, Ph.D.
Co-Major Professor
Srinivas Katkoori, Ph.D.
Committee Member
Luther Palmer, Ph.D.
Keywords
Kinematics, Manipulator, Robotics, WMRA
Abstract
The calibration of a 9 degree of freedom (DOF) robotic manipulator using multiple three axis accelerometers and an embedded system will be accomplished in this work. The 9-DOF robotic system used in this study is a 7-DOF robotic arm attached to a 2-DOF power wheelchair. Combined they create a Wheelchair Mounted Robotic Arm (WMRA). The problem that will be solved by this thesis is the calibration of the robotic system during start up. The 7 DOF robotic arm is comprised of rotational joints only. These joints have dual channel encoders to determine the joint position, among other useful data. The problem with dual channel encoders is that when power to the encoders is turned off and the motor is moved, then the robot controller does not have accurate position data when the system is powered again. The proposed calibration method will find the angles of two joints per three axis accelerometer. Four separate accelerometers are mounted on different locations of the 7 DOF robotic arm to determine the arms joint values. To determine the orientation of the base frame, an inertial measurements unit (IMU) is mounted to the origin of the base frame. By using this system of accelerometers and inertial measurement unit, the WMRA can be completely calibrated during system start up. The results collected for this calibration method show joint estimations with an error of +-0.1 radians for each joint. The results also show an accumulation of error for joints that are farther from the base frame.
Scholar Commons Citation
Ashley, Daniel, "Using Embedded Systems to Determine the Configuration of a Static Wheelchair Mounted Robotic Arm" (2014). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/5344