Graduation Year

2015

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Mechanical Engineering

Major Professor

Rajiv Dubey, Ph.D.

Committee Member

Redwan Alqasemi, Ph.D.

Committee Member

Sudeep Sarkar, Ph.D.

Committee Member

Yu Sun, Ph.D.

Committee Member

Kandethody Ramachandran, Ph.D.

Keywords

Dual-Trajectory Control, Manipulability Measure, Optimization, Pose Estimation, Trajectory Tracking

Abstract

A mobile manipulator is a robotic arm mounted on a robotic mobile platform. In such a system, the degrees of freedom of the mobile platform are combined with that of the manipulator. As a result, the workspace of the manipulator is substantially extended. A mobile manipulator has two trajectories: the end-effector trajectory and the mobile platform trajectory. Typically, the mobile platform trajectory is not defined and is determined through inverse kinematics. But in some applications it is important to follow a specified mobile platform trajectory. The main focus of this work is to determine the inverse kinematics of a mobile manipulator to follow the specified end-effector and mobile platform trajectories, especially when both trajectories cannot be exactly followed simultaneously due to physical limitations. Two new control algorithms are developed to solve this problem.

In the first control algorithm, three joint-dependent control variables (spherical coordinates D, α and β) are introduced to define the mobile platform trajectory in relation to the end-effector trajectory and vice versa. This allows direct control of the mobile platform motion relative to the end-effector. Singularity-robust and task-priority inverse kinematics with gradient projection method is used to find best possible least-square solutions for the dual-trajectory tracking while maximizing the whole system manipulability. MATLAB Simulated Planar Mobile Manipulation is used to test and optimize the proposed control system. The results demonstrate the effectiveness of the control system in following the two trajectories as much as possible while optimizing the whole system manipulability measure.

The second new inverse kinematics algorithm is introduced when the mobile platform motion is restricted to stay on a specified virtual or physical track. The control scheme allows xii the mobile manipulator to follow the desired end-effector trajectory while keeping the mobile platform on a specified track. The mobile platform is moved along a track to position the arm at a pose that facilitates the end-effector task. The translation of the redundant mobile manipulator over the mobile platform track is determined by combining the mobility of the platform and the manipulation of the redundant arm in a single control system. The mobile platform is allowed to move forward and backward with different velocities along its track to enable the end-effector in following its trajectory. MATLAB simulated 5 DoF redundant planar mobile manipulator is used to implement and test the proposed control algorithm. The results demonstrate the effectiveness of the control system in adjusting the mobile platform translations along its track to allow the arm to follow its own trajectory with high manipulability. Both control algorithms are implemented on MATLAB simulated wheelchair mounted robotic arm system (WMRA-II). These control algorithms are also implemented on real the WMRA-II hardware.

In order to facilitate mobile manipulation, a control motion scheme is proposed to detect and correct the mobile platform pose estimation error using computer vision algorithm. The Iterative Closest Point (ICP) algorithm is used to register two consecutive Microsoft Kinect camera views. Two local transformation matrices i. e., Encoder and ICP transformation matrices, are fused using Extended Kalman Filter (EKF) to filter the encoder pose estimation error. VICON motion analysis system is used to capture the ground truth of the mobile platform. Real time implementation results show significant improvement in platform pose estimation. A real time application involving obstacle avoidance is used to test the proposed updated motion control system.

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