Robots--Kinematics

In this thesis work, hierarchical control techniques will be used for controlling a robotic manipulator. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control will consist on solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Micro-robot with three degrees of freedom will be used to evaluate this methodology. A decentralized fuzzy controller will be used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematic mapping in a supervisory mode. The FAM determines the inverse kinematic mapping which maps the desired Cartesian coordinates to the individual joint angles. The individual fuzzy controller for each joint will generate the required control signal to a DC motor to move the associated link to the new position. The proposed hierarchical fuzzy controller will be compared to a conventional PD controller.

Laser tracking coordinate measuring machines have the potential of continuously measuring three dimensional target coordinates in a large workspace with a fast sampling rate and high accuracy. Proper calibration of a laser tracking measurement system is essential prior to use of such a device for metrology. In the absence of a more accurate instrument for system calibration, one has to rely on self-calibration strategies. In this dissertation, a kinematic model that describes not only the motion but also geometric variations of a multiple-beam laser tracking system was developed. The proposed model has the following features: (1) Target positions can be computed from both distance and angular measurements. (2) Through error analysis it was proven that even rough angular measurement may improve the overall system calibration results. A self-calibration method was proposed to calibrate intelligent machines with planar constraints. The method is also applied to the self-calibration of the laser tracking system and a standard PUMA 560 robot. Various calibration strategies utilizing planar constraints were explored to deal with different system setups. For each calibration strategy, issues about the error parameter estimation of the system were investigated to find out under which conditions these parameters can be uniquely estimated. These conditions revealed the applicability of the planar constraints to the system self-calibration. The observability conditions can serve as a guideline for the experimental setup when planar constraint is utilized in the machine calibration including the calibration of the laser tracking systems. Intensive simulation studies were conducted to check validity of the theoretical results. Realistic noise values were injected to the system models to statistically assess the behavior of the self-calibration system under real-world conditions. Various practical calibration issues were also explored in the simulations and therefore to pave ways for experimental investigation. The calibration strategies were also applied experimentally to calibrate a laser tracking system constructed at the Robotics Center in Florida Atlantic University.