This paper presents the design of a robotic hand for prosthetic applications. The main characteristic of this robotic hand is its biologically-inspired parallel actuation system, which is based on the behavior/strength space of the Flexor Digitorum Profundus (FDP) and the Flexor Digitorum Superficialis (FDS) muscles. The design separates the strength space of the FDS and FDP muscles into a lighter strength region where finer manipulation and general approach tasks are executed, and a higher strength region where the more robust grasps are achieved. Two parallel actuator types and kinematic structures are designed to complement the requirements of both strength space regions. This unique structure is intended to be driven by electromyographical (EMG) signals captured at the surface of the skin. The direct relation between signal and actuation system lends itself well to interpreting the EMG signals from the FDP and FDS muscles into effective task execution, with the goal of helping the user to achieve a good approximation of the full capabilities associated with the human hand, without compromising strength, dexterity, appearance, or weight; which are common issues associated with prosthetic hands.
This research discusses the implementation of a fuzzy logic control system to drive the movement of a simplified cat leg model. The system’s movement in this paper addresses a planar motion where the model experiences a fixed horizontal velocity and a harmonic vertical displacement. The fuzzy logic (FL) controller applies membership functions to fuzzify the position and velocity errors and applies height defuzzification to generate the time dependant forcing function for the system’s horizontal and vertical governing equations. A PID controller is also applied as a benchmark for this research. Both controllers are optimized using the simplex method for which the FL controller performed just as well as the PID controller with more promise of accounting for the nonlinear influences that were neglected in this simplified cat leg model and requiring actuators with a lower required force range. This research provides the skeletal structure for which an effective total controller can be built on.
Abstract-Natural movements and force feedback are important elements in using teleoperated equipment if complex and speedy manipulation tasks are to be accomplished in remote and/or hazardous environments, such as hot cells, glove boxes, decommissioning, explosive disarmament, and space to name a few. In order to achieve this end the research presented in this paper has developed an admittance-type exoskeleton like multifingered haptic hand user interface that secures the user's palm and provides 3-dimensional force feedback to the user's fingertips. Atypical to conventional haptic hand user interfaces that limit themselves to integrating the human hand's characteristics just into the system's mechanical design, this system also perpetuates that inspiration into the designed user interface's controller. This is achieved by manifesting the property differences of manipulation and grasping activities as they pertain to the resilient human hand into a nonlinear master-slave force relationship. The results presented in this paper show that the admittance-type system has sufficient bandwidth such that it appears nearly transparent to the user when in free motion. Also, when executing a manipulation or grasping task, increased performance is achieved using the nonlinear force relationship compared to the traditional linear scaling techniques implemented in the vast majority of systems.
A neural network capable of solving the inverse kinematics of a four degree of freedom biologically inspired robotic cat leg (qualified as a serial linkage system) within its effective 3-D workspace is presented in this paper. The workspace consists of layers of similar but highly nonlinear cells whose vertices are associated with known kinematic variables provided by the robotic leg. The proposed neural network uses geometric properties coupled with the desired end effecter location as the neural network inputs to locate the cell for which encapsulates the associated location. Another neuron layer utilizing activation functions trained with the Perceptron Fixed learning rule is applied to interpolate within the identified cell. The similarity associated between all of the cells allows the trained neural network to effectively be applied in solving the inverse kinematics of the entire workspace.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.