Five unique soft robotic jellyfish were manufactured with eight pneumatic network tentacle actuators extending radially from their centers. These jellyfish robots were able to freely swim untethered in the ocean, to steer from side to side, and to swim through orifices more narrow than the nominal diameter of the jellyfish. Each of the five jellyfish robots were manufactured with a different composition of body and tentacle actuator Shore hardness. A three-factor study was performed with these five jellyfish robots to determine the impact that actuator material Shore hardness, actuation frequency, and tentacle stroke actuation amplitude had upon the measured thrust force. It was found that all three of these factors significantly impacted mean thrust force generation, which peaked with a half-stroke actuation amplitude at a frequency of 0.8 Hz.

Most robotic applications that contain shape memory alloy (SMA) actuators use the SMA in a linear or spring shape. In contrast, a novel robotic finger was designed in this paper using SMA plates that were thermomechanically trained to take the shape of a flexed human finger when Joule heated. This flexor actuator was placed in parallel with an extensor actuator that was designed to straighten when Joule heated. Thus, alternately heating and cooling the flexor and extensor actuators caused the finger to flex and extend. Three different NiTi based SMA plates were evaluated for their ability to apply forces to a rigid and compliant object. The best of these three SMAs was able to apply a maximum fingertip force of 9.01N on average. A 3D CAD model of a human finger was used to create a solid model for the mold of the finger covering skin. Using a 3D printer, inner and outer molds were fabricated to house the actuators and a position sensor, which were assembled using a multi-stage casting process. Next, a nonlinear antagonistic controller was developed using an outer position control loop with two inner MOSFET current control loops. Sine and square wave tracking experiments demonstrated minimal errors within the operational bounds of the finger. The ability of the finger to recover from unexpected disturbances was also shown along with the frequency response up to 7 rad s−1. The closed loop bandwidth of the system was 6.4 rad s−1 when operated intermittently and 1.8 rad s−1 when operated continuously.

Upper limb amputees have no direct sense of the grip force applied by a prosthetic hand; thus, precise control of the applied grip force is difficult for amputees. Since there is little object deformation when rigid objects are grasped, it is difficult for amputees to visually gauge the applied grip force in this situation. To determine if the applied grip force from a prosthetic hand can be visually displayed and used to more efficaciously grasp objects. Experimental controlled trial. Force feedback is used in the control algorithm for the prosthetic hand and supplied visually to the user through a bicolor LED experimentally mounted to the thumb. Several experiments are performed by able-bodied test subjects to rate the usefulness of the additional visual feedback when manipulating a clearly visible, brittle object that can break if grasped too firmly. A hybrid force-velocity sliding mode controller is used with and without additional visual force feedback supplied to the operators. Subjective evaluations and success rates from the test subjects indicate a statistically significant reduction in breaking the grasped object when using the prosthesis with the extra visual feedback. The additional visual force feedback can effectively facilitate the manipulation of brittle objects. The novel approach of this research is the implementation of a noninvasive, effective and economic technique to visually indicate the grip force applied by a prosthetic hand to upper limb amputees. This technique provides a statistically significant improvement when handling brittle objects.

Multifunctional flexible tactile sensors could be useful to improve the control of prosthetic hands. To that end, highly stretchable liquid metal tactile sensors (LMS) were designed, manufactured via photolithography, and incorporated into the fingertips of a prosthetic hand. Three novel contributions were made with the LMS. First, individual fingertips were used to distinguish between different speeds of sliding contact with different surfaces. Second, differences in surface textures were reliably detected during sliding contact. Third, the capacity for hierarchical tactile sensor integration was demonstrated by using four LMS signals simultaneously to distinguish between ten complex multi-textured surfaces. Four different machine learning algorithms were compared for their successful classification capabilities: K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and neural network (NN). The time-frequency features of the LMSs were extracted to train and test the machine learning algorithms. The NN generally performed the best at the speed and texture detection with a single finger and had a 99.2 ± 0.8% accuracy to distinguish between ten different multi-textured surfaces using four LMSs from four fingers simultaneously. The capability for hierarchical multi-finger tactile sensation integration could be useful to provide a higher level of intelligence for artificial hands.

Due to their limited dexterity, it is currently not possible to use a commercially available prosthetic hand to unscrew or screw objects without using elbow and shoulder movements. For these tasks, prosthetic hands function like a wrench, which is unnatural and limits their use in tight working environments. Results from timed rotational tasks with human subjects demonstrate the clinical need for increased dexterity of prosthetic hands, and a clinically viable solution to this problem is presented for an anthropomorphic artificial hand.