Grasping is an essential skill for almost every assistive robot. Variations in shape and/or weight of different objects involved in Activities of Daily Living (ADL) lead to complications, especially, when the robot is trying to grip novel objects for which it has no prior information –too much force will deform or crush the object while too little force will lead to slipping and possibly dropped objects. Thus, successful grasping requires the gripper to immobilize an object with the minimal force. In Chapter 2, we present the design, analysis, and experimental implementation of an adaptive control to facilitate 1-click grasping of novel objects by a robotic gripper. Motivated by a desire to obtain a reduced-order controller, a previously developed grasp model is reparameterized to design an adaptive backstepping controller. A Lyapunov-based analysis is utilized to show asymptotic convergence of the object slip velocity to the origin. Furthermore, the analysis shows that the closed-loop controller is able to estimate the minimal steady-state force required to grasp the object. Simulation and experiment results both show that the object is immobilized within the gripper without any significant deformation. Also, in Chapter 3 we present the design and implementation of an algorithm, equipped with a switched adaptive controller, for grasping unknown objects using a robot gripper. A Lyapunov-based analysis demonstrates that the switching controller is indeed asymptotically stable with both the translational and rotational slip velocities converging to the origin. Experimental results using a novel sensorized gripper prototype and objects of different sizes, shapes, and weights show that the proposed algorithm not only ensures prevention of slippage of the grasped objects, but it is also able to apply the minimal force needed to safely grasp these objects without causing excessive deformation. In Chapter 4, the Pearson and Spearman correlation tests are employed to capture the joint probability distribution of human variables related to human-robot interaction using experiment data obtained from 93 individuals. The findings show that some human factors are jointly distributed within the same group as: (spatial visualization (SpV), spatial orientation (SpO), and visual perception (VP)), (gross dexterity (GD) and fine dexterity (FD)) and (visual acuityWVand SV), while the Reaction Time (RT), working memory (WM), depth perception (DP) are related insignificantly. Furthermore, we present Principal Components Analysis (PCA) of human factors. By using Varimax Rotation matrix to gain obvious interpretations, it confirms the same observations about the interdependencies between the human factors.


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Graduation Date





Behal, Aman


Doctor of Philosophy (Ph.D.)


College of Engineering and Computer Science


Electrical and Computer Engineering

Degree Program

Electrical Engineering




CFE0008941; DP0026274





Release Date

May 2022

Length of Campus-only Access


Access Status

Doctoral Dissertation (Open Access)