PRELIMINARY DESIGN AND EVALUATION OF AN OVERHEAD KITCHEN ROBOT APPLIANCE

Many older adults and individuals with disabilities have difficulty with reaching, grasping, and carrying items that are a necessity to perform independent activities of daily living, including meal preparation in the kitchen. Assistive robotic manipulators are starting to show potential for independent assistance through their use on wheelchairs or mobile bases, but continue to lack many of the autonomous features readily available with fixed environment manipulators. The KitchenBot design described here provides the details and approach to providing an assistive robotic manipulator access to an entire kitchen workspace by utilizing a multi-degree track. Numerous focus groups were conducted in conjunction with the design and major features like heavy payload ability, tablet control interface, and user feedback was extracted. With further development, the KitchenBot could perform an even longer list of routine autonomous tasks in a product viable for everyone to use

Biomimetic Design, Modeling, and Adaptive Control of Robotic Gripper for Optimal Grasping

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

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Augmenting user capabilities through an adaptive assistive manipulator

Mención Internacional en el título de doctorAssistive robot manipulators have the potential to increase the independence of disabled persons in activities of daily living. The current designs are mainly limited to pure teleoperation by the user, given the need for keeping the user in the control loop, and the complexity of the tasks and environments in which they operate. This thesis aims to augment the user’s capabilities for performing such tasks by adapting the robot, and its level of assistance, to the user. Methodologies for modeling and benchmarking the complete human-robot system were established, which helped drive the development of different approaches to adaptation. This included a task-oriented optimization of the robot physical structure, approaches for low-level adaptive shared control, and work on interactive learning of, and assistance on completing, simple object manipulation tasks. Three experimental platforms were used: The ASIBOT manipulator of Universidad Carlos III de Madrid (UC3M), the AMOR manipulator of Exact Dynamics, and the iCub humanoid robot.Los manipuladores asistenciales tienen el potencial de incrementar la independencia de personas discapacitadas en sus actividades de la vida diaria. Los diseños actuales se limitan principalmente a una pura teleoperación, pues dada la complejidad de las tareas y del entorno, se necesita mantener al usuario en el lazo de control. Esta tesis pretende mejorar las capacidades del usuario para realizar estas tareas, adaptando el robot y su nivel de asistencia a las necesidades del usuario. Se han establecido metodologías para el modelado y evaluación del comportamiento del sistema formado por humano y robot, lo que ha permitido el desarrollo de diferentes aproximaciones a la adaptación. Esto incluye desde la optimización de la estructura del robot atendiendo a las tareas, la evaluación de diversas aproximaciones al control compartido adaptativo a bajo nivel, al aprendizaje interactivo y el desarrollo de asistencias para completar tareas sencillas de manipulación. Se ha hecho uso de tres plataformas experimentales: el manipulador ASIBOT de la Universidad Carlos III de Madrid (UC3M), el manipulador AMOR de Exact Dynamics y el humanoide iCub.Programa Oficial de Doctorado en Ingeniería Eléctrica, Electrónica y AutomáticaPresidente: Alberto Sanfeliú.- Secretario: Concepción Alicia Monje Micharet.- Vocal: Yiannis Demiri