Proceedings. Florida Conference on Recent Advances in Robotics最新文献

Prosthetic hands help upper limb amputees and people who were born without hands. Currently, these prostheses are rather rudimentary and do not provide adequate sensing capabilities compared to a human hand. People use their natural hands to perceive complex tactile phenomena such as shear and torsion using thousands of mechanoreceptors in their fingertips. The capability to detect torsional loads at the fingertips is a notable gap in prosthetic hand sensation. Flexible tactile sensors are a promising new technology that would be ideal for prosthetic hands since they allow for stretching and movement like human skin without damage to the sensor. Therefore, the purpose of this study is to determine whether a flexible magnetic sensor array combined with an artificial neural network (ANN) can detect and classify torsion. The flexible magnetic sensor is designed as a 3×3 array of magnets embedded in a stretchable elastomer which are situated atop a corresponding array of Hall effect sensors. Torques applied to the soft magnetic skin caused displacement of the magnetic fields that were perceived by the nine Hall effect sensors. In this study, ten different values of torque were applied to the flexible magnetic sensor array using a robotic arm to ensure consistency. Data were used to train an ANN to classify the applied torques. The ANN was trained ten times and could predict the applied torque with an average training classification accuracy of 97.48% ± 0.33%. Given the results of this study, this novel sensor design could enable more refined sensations of touch for people who use prosthetic hands.

Soft Robotic Actuators (SRAs) have piqued the interest of researchers in recent years. SRAs are generally constructed of soft elastomers and use air or water as a mean of actuation. Due to the inherent properties of these actuators, they are ideal for HumanRobot Interactions (HRI), exoskeletons for rehabilitation and for grasping delicate objects. Since SRA's are actuated using a fluid, being able to effectively control the rate of actuation, pressure and the force applied is necessary so that the actuator and the object being grasped does not get damaged. This paper aims to evaluate three types of controllers, an open-loop controller, pressure-feedback controller, and a force-feedback controller, all used to control an SRA. A custom test stand was built to hold the SRA and test it with all three controllers. The pressure-feedback controller was set to limit the pressure to 8.9 kPa and the force was limited to 0.147 N in the force-feedback controller. Since the open-loop controller had no feedback, the SRA was actuated at a specified frequency while force and pressure measurements were taken. The force-feedback and the pressure-feedback controllers accurately controlled the actuators and the open loop-controller was able to actuate the SRA reliably.

This force-feedback approach compares the effect on the sensing ability through a worn glove of the force application of an i-Limb Ultra robotic hand for several experimental scenarios. A Takktile sensor was integrated into a fabricated fingertip to measure the applied force of the i-Limb Ultra. A controller was then designed using MATLAB/Simulink to manipulate the finger motion of the i-Limb to apply force to an external load cell. Testing was performed to check the force measurements and sensing ability/quality for two cases: hand with no glove and hand with a nitrile glove. Each of these scenarios were tested by applying fingertip force in 3 different modes: open/close with no contact, continuous tapping and constant force.