Benjamin A. Kent

Bioinspired Sinusoidal Finger Joint Synergies for a Dexterous Robotic Hand to Screw and Unscrew Objects With Different Diameters

This paper addresses the complex task of unscrewing and screwing objects with a dexterous anthropomorphic robotic hand in two cases: with the first finger and thumb and also with the little finger and thumb. To develop an anthropomorphic solution, human finger synergies from nine test subjects were recorded while unscrewing and screwing a threaded cap. Human results showed that the periodic motions exhibited by the finger joints shared a common frequency for each subject, but differed in amplitude and phase. From the gathered data, a set of sinusoidal trajectories were developed to approximate this motion for application to a robotic hand. Because the joint trajectories exhibited the same frequency, a family of sinusoids that share a common time vector can be used in the path planning of the robotic hand to unscrew and screw objects. Additionally, the human unscrewing data are highly similar to the mirror image of the screwing data. This chiastic trait enables screwing to be performed by decreasing the time vector; increasing the time vector produces unscrewing. These factors significantly reduce the computational cost and complexity of the task. Cartesian and joint space error analyses show that the developed sinusoidal trajectories closely mimic the motion profiles seen in the human experiments. Furthermore, this bioinspired sinusoidal solution is extended to objects with wide variations in diameters by relating joint angle offsets of the robotic hand to object diameter size through the forward kinematics equations. The sinusoidal trajectories are all implemented within a PID sliding mode controller for a dexterous artificial hand to ensure overall system stability. Using the bioinspired sinusoidal joint angle trajectories, the robotic hand successfully unscrewed and screwed four different objects in all trials conducted with each object diameter size.

Anthropomorphic Control of a Dexterous Artificial Hand via Task Dependent Temporally Synchronized Synergies

Despite the recent influx of increasingly dexterous prostheses, there remains a lack of sufficiently intuitive control methods to fully utilize this dexterity. As a solution to this problem, a control framework is proposed which allows the control of an arbitrary number of Degrees of Freedom (DOF) through a single electromyogram (EMG) control input. Initially, the joint motions of nine test subjects were recorded while grasping and catching a cylinder. Inherent differences emerged depending upon whether the cylinder was grasped or caught. These data were used to form a distinct synergy for each task, described as the families of parametric functions of time that share a mutual time vector. These two Temporally Synchronized Synergies (TSS) were derived to reflect the task dependent control strategies adopted by the initial participants. These synergies were then mapped to a dexterous artificial hand that was subsequently controlled by two subjects with transradial amputations. The EMG signals from these subjects were used to replace the time vector shared by the synergies, enabling the subjects to perform both tasks with a dexterous artificial hand using only a single EMG input. After a ten minute training period, the subjects learned to use the dexterous artificial hand to grasp and catch the cylinder with 100.0% and 65.0% average success rates, respectively.

Electromyogram synergy control of a dexterous artificial hand to unscrew and screw objects

BackgroundDue 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.MethodsInitially, a human hand motion analysis was performed during a rotational task. From these data, human hand synergies were derived and mapped to an anthropomorphic artificial hand. The synergy for the artificial hand is controlled using conventional dual site electromyogram (EMG) signals. These EMG signals were mapped to the developed synergy to control four joints of the dexterous artificial hand simultaneously.Five limb absent and ten able-bodied test subjects participated in a comparison study to complete a timed rotational task as quickly as possible with their natural hands (except for one subject with a bilateral hand absence), eight commercially available prosthetic hands, and the proposed synergy controller. Each test subject used two to four different artificial hands.ResultsWith the able-bodied subjects, the developed synergy controller reduced task completion time by 177% on average. The limb absent subjects completed the task faster on average than with their own prostheses by 46%. There was a statistically significant improvement in task completion time with the synergy controller for three of the four limb absent participants with integrated prostheses, and was not statistically different for the fourth.ConclusionsThe proposed synergy controller reduced average task completion time compared to commercially available prostheses. Additionally, the synergy controller is able to function in a small workspace and requires less physical effort since arm movements are not required. The synergy controller is driven by conventional dual site EMG signals that are commonly used for prosthetic hand control, offering a viable solution for people with an upper limb absence to use a more dexterous artificial hand to screw or unscrew objects.

Backdrivable periodic finger joint synergies: Human observations applied to a dexterous robotic hand

This work deals with the complex task of unscrewing and screwing a threaded cap with a dexterous anthropomorphic robotic hand. To that end, human motion profiles of nine test subjects were recorded using the CyberGlove II and the data were analyzed for unscrewing and screwing a bottle cap. Results showed that the periodic motions exhibited by the finger joints shared a common frequency for each subject, but differed in amplitude and phase. The unscrewing data appears highly similar to the mirror image of the screwing data. This implies that the screwing motions can be backdriven to produce the unscrewing motion and vice versa. A forward loop (FL) in time implies an increment in the time vector, which starts from zero and ends at some point resulting to unscrew the bottle cap. A reverse loop (RL) is produced by a decrement in time and results in screwing the bottle cap. From the gathered data, a set of sinusoidal trajectories were developed to approximate this motion for a robotic hand. Because the joint trajectories share the same frequency, a single sinusoidal input can be used in the path planning of the robot to achieve this task. The reference joint is given a sinusoidal input and the remaining joints are scaled in phase and amplitude with respect to this reference joint. This significantly reduces the computational cost and complexity of the task. Simulation results show that the developed sinusoidal trajectories show a close correlation with the motion profiles seen from human experiments. Using the developed sinusoidal trajectories, the robotic hand successfully unscrewed and screwed the bottle cap in all trials conducted.

Biologically inspired posture control for a dexterous robotic hand

A biologically inspired control strategy is presented for producing more human-like movement in anthropomorphic manipulators when in contact with surfaces of varying contours. In preliminary experiments, 10 human subjects were asked to run their hands over several contoured surfaces and the hand motion profiles were recorded. It was observed that humans tended to minimize abduction of the fingers while adapting to the contours of each surface. Based on observations from this data, a control strategy was developed to mimic the tendencies seen in human trials with a robotic hand. Experiments were repeated with the robotic hand using the same surfaces. Results verify that the control strategies developed are capable of producing more biomimetic motion, help to stabilize the system, and enable the manipulator to dynamically react to its environment at a low computational cost.

Human finger joint synergies for a constrained task applied to a dexterous anthropomorphic hand

This work deals with the complex task of unscrewing a bottle cap with a dexterous anthropomorphic hand. To that end, human motion profiles of nine test subjects were recorded using CyberGlove II and the data were analyzed for unscrewing a bottle cap. Results showed that the periodic motions exhibited by the finger joints shared a common frequency for each subject, but differed in amplitude and phase. From the gathered data, a set of sinusoidal trajectories were developed to approximate this motion for a robotic hand. Because the joint trajectories share the same frequency, a single sinusoidal input can be used in the path planning of the robot to achieve this task. A reference joint is given a sinusoidal input, and the remaining joints are scaled in phase and amplitude with respect to this reference joint. This significantly reduces the computational cost and complexity of the task. Simulation results show that the developed sinusoidal trajectories show a close correlation with the motion profiles seen from human experiments. Using the developed sine trajectories, the robotic hand successfully unscrewed the bottle cap in all five conducted trials.

Grasp-Dependent Slip Prevention for a Dexterous Artificial Hand via Wrist Velocity Feedback

A proportional controller is compared to a nonlinear backstepping controller with four different grasps for a dexterous anthropomorphic hand. A bioinspired grasp-dependent control scheme which autonomously modulates the grip force using wrist velocity feedback to prevent grasped object slip is also introduced. Four different grasp types are evaluated to illustrate how the wrist velocity feedback architecture must differ depending upon the manner in which objects are grasped. The backstepping controller can successfully increase grip force with wrist velocity in a robustly stable bioinspired fashion. Experimental results show that the developed backstepping controller improves the position tracking abilities for multiple periodic inputs, as well as reduces step input overshoot. The slip prevention capabilities of the backstepping controller are also demonstrated and compared to the proportional control scheme. Results of the slip prevention experiments show that both the grasp type and manipulator orientation with respect to gravity are significant factors in the performance of the controllers. The backstepping control scheme significantly improves slip prevention of grasped objects for multiple grasps and in two different orientations with respect to gravity.

Human-inspired feedback synergies for environmental interaction with a dexterous robotic hand

Effortless control of the human hand is mediated by the physical and neural couplings inherent in the structure of the hand. This concept was explored for environmental interaction tasks with the human hand, and a novel human-inspired feedback synergy (HFS) controller was developed for a robotic hand which synchronized position and force feedback signals to mimic observed human hand motions. This was achieved by first recording the finger joint motion profiles of human test subjects, where it was observed that the subjects would extend their fingers to maintain a natural hand posture when interacting with different surfaces. The resulting human joint angle data were used as inspiration to develop the HFS controller for the anthropomorphic robotic hand, which incorporated finger abduction and force feedback in the control laws for finger extension. Experimental results showed that by projecting a broader view of the tasks at hand to each specific joint, the HFS controller produced hand motion profiles that closely mimic the observed human responses and allowed the robotic manipulator to interact with the surfaces while maintaining a natural hand posture. Additionally, the HFS controller enabled the robotic hand to autonomously traverse vertical step discontinuities without prior knowledge of the environment, visual feedback, or traditional trajectory planning techniques.

Bioinspired grasp primitives for a dexterous artificial hand to catch and lift a cylinder

Joint motion profiles from nine human test subjects were recorded while catching and lifting a cylinder. Significant differences between the two tasks presented themselves consistently across all test subjects. In general, initial rapid hyperextensions were observed while catching the cylinder versus simply picking it up. Slightly larger ranges of abduction were also seen for the catch, but, as expected for the cylinder, these were approximately symmetric across the four fingers. From the recorded motions, cubic polynomials were fit to the joint angle data to form two distinct grasp primitives. The joint motions of the test subjects were simulated in Simulink with a skeletal structure of the human hand as well as a model of the Shadow Hand. These primitives were subsequently used with the physical Shadow Hand by a human operator who was able to successfully pick up and catch a cylinder. An electromyogram signal measured from the forearm of the test subject was used as the input to the Shadow Hand controller. After a brief training session with the EMG-controlled hand, the cylinder was successfully caught in seven of ten attempts and successfully grasped and lifted in five of five attempts.

Robotic Hand Acceleration Feedback to Synergistically Prevent Grasped Object Slip

A control framework is proposed that autonomously modulates a dexterous grasp synergy to proactively prevent grasped object slip. The proposed controller offers a practical means of preventing slip in uncertain environments, such as upper-limb prosthetics applications. The controller was evaluated with slip prevention experiments in which a dexterous manipulator grasped four separate objects and was subsequently excited by a robotic arm. Results showed that the proposed strategy significantly reduced grasped object slip.