The role of morphology of the thumb in anthropomorphic grasping : a review

The unique musculoskeletal structure of the human hand brings in wider dexterous capabilities to grasp and manipulate a repertoire of objects than the non-human primates. It has been widely accepted that the orientation and the position of the thumb plays an important role in this characteristic behavior. There have been numerous attempts to develop anthropomorphic robotic hands with varying levels of success. Nevertheless, manipulation ability in those hands is to be ameliorated even though they can grasp objects successfully. An appropriate model of the thumb is important to manipulate the objects against the fingers and to maintain the stability. Modeling these complex interactions about the mechanical axes of the joints and how to incorporate these joints in robotic thumbs is a challenging task. This article presents a review of the biomechanics of the human thumb and the robotic thumb designs to identify opportunities for future anthropomorphic robotic hands

Morphological Computation of Haptic Perception of a Controllable Stiffness Probe

When people are asked to palpate a novel soft object to discern its physical properties such as texture, elasticity, and even non-homogeneity, they not only regulate probing behaviors, but also the co-contraction level of antagonistic muscles to control the mechanical impedance of fingers. It is suspected that such behavior tries to enhance haptic perception by regulating the function of mechanoreceptors at different depths of the fingertips and proprioceptive sensors such as tendon and spindle sensors located in muscles. In this paper, we designed and fabricated a novel two-degree of freedom variable stiffness indentation probe to investigate whether the regulation of internal stiffness, indentation, and probe sweeping velocity (PSV) variables affect the accuracy of the depth estimation of stiff inclusions in an artificial silicon phantom using information gain metrics. Our experimental results provide new insights into not only the biological phenomena of haptic perception but also new opportunities to design and control soft robotic probes

Human-Aware Robot Navigation by Behavioral Metrics Based on Predictive Model

Human-aware robot navigation is very important in many applications in human-robot shared environments. There are some situations, people have to move with less visual and auditory perceptions. In that case, the robot can help to enhance the efficiency of navigation when moving in noisy and low visibility conditions. In that scenario, haptic is the best way to communicate when other modalities are less reliable. We used a rein to guide a human when 1-DoF robotic arm can perturb the humans’ arm to guide into a desired point. The novelty of our work is presenting behavioral metrics based on novel predictive model to strategically position the humans in human-robot shared environment in low visibly and auditory conditions. We found that humans start with a second order reactive autoregressive following model and changes it to a predictive model with training. This result would help us to enhance humans’ safety and comfort in robot leading navigation in shared environment

Magnetic Field Modelling of a Soft Three-Axis Force Sensor

This paper describes the modelling of a soft threeaxis force sensor. The sensor has a cylindrical cantilever beammade of silicone rubber that compress and bend when normaland tangential forces are applied. The displacement of the beam’send is calculated by measuring the change of the magnetic fieldemitted by a permanent magnet embedded in the soft beam.Spring theory and bending theory, are used to calculate thenormal and tangential force components. The normal forcescalculated by the proposed model and the measured values has anerror less than 5% validating the analogy of the sensor to a softcantilever beam under compression and bending. The proposedmathematical model is simple and faster than a finite elementmodel, and accurately represents the non-linear behavior of thesensor’s physical effects to applied loads

Soft tissue characterisation using a novel robotic medical percussion device with acoustic analysis and neural networks

Medical percussion is a common manual examination procedure used by physicians to determine the state of underlying tissues from their acoustic responses. Although it has been used for centuries, there is a limited quantitative understanding of its dynamics, leading to subjectivity and a lack of detailed standardisation. This letter presents a novel compliant two-degree-of-freedom robotic device inspired by the human percussion action, and validates its performance in two tissue characterisation experiments. In Experiment 1, spectro-temporal analysis using 1-D Continuous Wavelet Transform (CWT) proved the potential of the device to identify hard nodules, mimicking lipomas, embedded in silicone phantoms representing a patient's abdominal region. In Experiment 2, Gaussian Mixture Modelling (GMM) and Neural Network (NN) predictive models were implemented to classify composite phantom tissues of varying density and thickness. The proposed device and methods showed up to 97.5% accuracy in the classification of phantoms, proving the potential of robotic solutions to standardise and improve the accuracy of percussion diagnostic procedures

Human Behavioral Metrics of a Predictive Model Emerging During Robot Assisted Following Without Visual Feedback

Robot assisted guiding is gaining increased interest due to many applications involving moving in noisy and low visibility environments. In such cases, haptic feedback is the most effective medium to communicate. In this paper, we focus on perturbation based haptic feedback due to applications like guide dogs for visually impaired people and potential robotic counterparts providing haptic feedback via reins to assist indoor firefighting in thick smoke. Since proprioceptive sensors like spindles and tendons are part of the muscles involved in the perturbation, haptic perception becomes a coupled phenomenon with spontaneous reflex muscle activity. The nature of this interplay and how the model based sensory-motor integration evolves during haptic based guiding is not well understood yet. In this study, we asked human followers to hold the handle of a hard rein attached to a 1-DoF robotic arm that gave perturbations to the hand to correct an angle error of the follower. We found that human followers start with a 2nd order reactive autoregressive following model and changes it to a predictive model with training. The post-perturbation Electromyography (EMG) activity exhibited a reduction in co-contraction of muscles with training. This was accompanied by a reduction in the leftward/rightward asymmetry of a set of followers behavioural metrics. These results show that the model based prediction accounts for the internal coupling between proprioception and muscle activity during perturbation responses. Furthermore, the results provide a firm foundation and measurement metrics to design and evaluate robot assisted haptic guiding of humans in low visibility environments

Wearable Haptic Based Pattern Feedback Sleeve System

This paper presents how humans trained in primitive hap- tic based patterns using a wearable sleeve, can recognize their scaling and shifting. The wearable sleeve consisted of 7 vibro-actuators to stimulate subjects arm to convey the primitive haptic based patterns. The used primitive haptic patterns are the Gaussian template (T), shifted right (R), shifted left (L), half Gaussian (H), and shrink (S) hereafter denoted by templates. The results of this paper would give an idea as to how humans mentally construct the cutaneous feedback in different scenarios such as shifting and scaling with respect to trained patterns and how they recognize all trained patterns when played randomly. These insights will help to develop more e�efficient haptic feedback systems us- ing a small number of templates to be learnt to encode complex haptic messages. Therefore, the results provide new insights and design guide- lines/algorithm to convey messages encoded in vibro-tactile actuator arrays specially in where vision and audition are less reliable scenarios like search and rescue, factories. For example, the results would be used to convey a message to the human to give an idea of the shape and stiffness of obstacles that come into contact with the robot during haptic based guiding in low visibility conditions in human-robot interactions

Wearable Haptic Based Pattern Feedback Sleeve System

This paper presents how humans trained in primitive hap- tic based patterns using a wearable sleeve, can recognize their scaling and shifting. The wearable sleeve consisted of 7 vibro-actuators to stimulate subjects arm to convey the primitive haptic based patterns. The used primitive haptic patterns are the Gaussian template (T), shifted right (R), shifted left (L), half Gaussian (H), and shrink (S) hereafter denoted by templates. The results of this paper would give an idea as to how humans mentally construct the cutaneous feedback in different scenarios such as shifting and scaling with respect to trained patterns and how they recognize all trained patterns when played randomly. These insights will help to develop more e�efficient haptic feedback systems us- ing a small number of templates to be learnt to encode complex haptic messages. Therefore, the results provide new insights and design guide- lines/algorithm to convey messages encoded in vibro-tactile actuator arrays specially in where vision and audition are less reliable scenarios like search and rescue, factories. For example, the results would be used to convey a message to the human to give an idea of the shape and stiffness of obstacles that come into contact with the robot during haptic based guiding in low visibility conditions in human-robot interactions