A Soft Robotic Wearable Wrist Device for Kinesthetic Haptic Feedback

Advances in soft robotics provide a unique approach for delivering haptic feedback to a user by a soft wearable device. Such devices can apply forces directly on the human joints, while still maintaining the safety and flexibility necessary for use in close proximity to the human body. To take advantage of these properties, we present a new haptic wrist device using pressure-driven soft actuators called reverse pneumatic artificial muscles (rPAMs) mounted on four sides of the wrist. These actuators are originally pre-strained and release compressive stress under pressure, applying a safe torque around the wrist joints while being compact and portable, representing the first soft haptic device capable of real-time feedback. To demonstrate the functional utility of this device, we created a virtual path-following task, wherein the user employs the motion of their wrist to control their embodied agent. We used the haptic wrist device to assist the user in following the path and study their performance with and without haptic feedback in multiple scenarios. Our results quantify the effect of wearable soft robotic haptic feedback on user performance. Specifically, we observed that our haptic feedback system improved the performance of users following complicated paths in a statistically significant manner, but did not show improvement for simple linear paths. Based on our findings, we anticipate broader applications of wearable soft robotic haptic devices toward intuitive user interactions with robots, computers, and other users

Desarrollo de un controlador de posición avanzado para endoscopio blando en cirugía laparoscópica

El presente estudio se desarrolla en el marco de brindar asistencia al cirujano en la laparoscopia, la cual es una cirugía utilizada para tratar problemas de salud en la zona abdominal. El procedimiento utiliza una cámara conectada a un tubo delgado flexible llamado endoscopio, el cual permite observar al interior de la zona abdominal del paciente; las imágenes obtenidas por el instrumento son utilizadas por el cirujano durante el tratamiento del paciente. Para garantizar un procedimiento correcto, se debe mover correctamente el endoscopio en el interior del abdomen, siendo esta tarea específicamente la que se busca facilitar su control y con ello dar apertura a una serie de posibilidades como el movimiento asistido, la operación remota y la automatización completa de la tarea. Con el fin de proponer una solución, actualmente, con el avance en el campo de la robótica blanda se han diseñado y fabricados manipuladores o actuadores blandos que puedan ser usados como endoscopios, los cuales tienen la capacidad de deformarse y ser forzados a moverse para alcanzar diferentes posiciones deseadas dentro de sus límites de operación. El cuerpo del manipulador o actuador blando en estudio presenta cuatro cámaras internas, las cuales pueden ser deformadas regulando la cantidad de presión de aire al interior de cada cámara. Para controlar y alcanzar la posición deseada del efector final del endoscopio, donde una cámara será conectada, en el presente trabajo se realiza el modelamiento de la dinámica del cuerpo del endoscopio y el diseño del controlador de posición. La tarea de modelamiento consiste en definir las características de la estructura de una red neuronal recurrente con realimentación a la salida y luego realizar su entrenamiento usando el algoritmo DBP (Dynamic Back-Propagation) para obtener los pesos de conexión entre las neuronas de la red. El diseño del controlador consiste de dos etapas. En la primera etapa se definen las características de la estructura de una red neuronal prealimentada (feed-forward). Para el entrenamiento de la red se utiliza el algoritmo DBP bajo un enfoque dinámico donde se considera el sistema en lazo cerrado, el cual comprende tanto al controlador como al modelo del sistema. El controlador de posición obtenido es válido solamente dentro de un rango de movimiento; por ello, se definen un conjunto de controladores para cada rango de operación. En la segunda etapa, se utiliza el método difuso Takagi Sugeno para la integración de los controladores locales y la obtención de un controlador global valido en todo el rango de operación. El controlador obtenido se implementa y prueba mediante simulación con el objetivo de validar su desempeño para diferentes posiciones deseadas del endoscopio.The present study is carried out within the framework of providing assistance to the surgeon in laparoscopy, which is a surgery used to treat health problems in the abdominal area. The procedure uses a camera connected to a thin flexible tube called endoscope, which allows seeing inside the patient's abdominal area; the images obtained are used by the surgeon during the patient's treatment. An essential and correct procedure consists of moving the endoscope correctly inside the abdomen. This task is seeking to facilitate its control and open up a series of possibilities such as assisted movement, remote operation and complete automation of tasks. In order to propose a solution, currently, with advances in the field of soft robotics, soft manipulators or actuators have been designed and manufactured to be used as endoscopes, which have the ability to deform and be forced to move and reach different desired positions within its operation limits. The soft manipulator body or actuator under study has four internal chambers, which can be deformed by regulating air pressure of each chamber. In order to control and reach the desired position of endoscope final effector, where a camera will be connected, in the current work the endoscope body modeling and the position controller design are carried out as main tasks. The modeling task consists of defining the characteristics of a recurrent neural network with output feedback and then training it using the DBP (Dynamic Back-Propagation) algorithm to obtain its connection weights between network neurons. The controller design consists of two stages. In the first stage, the characteristics of a feed forward neural network are defined. For network training, the DBP algorithm is used under a dynamic approach where the closed-loop system is considered, which includes both the controller and the system model. The obtained position controller is valid only within a range of motion; therefore, a set of controllers is defined for each range of operation. In the second stage, the fuzzy Takagi Sugeno method is used to integrate the local controllers and obtain a global controller for complete endoscope operating range. The controller obtained is implemented and tested by simulation in order to validate its performance for different desired positions of endoscope final effector