Thrishantha Nanayakkara

Dominant sources of variability in passive walking

This paper investigates possible sources of variability in the dynamics of legged locomotion, even in its most idealized form. The rimless wheel model is a seemingly deterministic legged dynamic system, popular within the legged locomotion community for understanding basic collision dynamics and energetics during passive phases of walking. Despite the simplicity of this legged model, however, experimental motion capture data recording the passive step-to-step dynamics of a rimless wheel down a constant-slope terrain actually demonstrate significant variability, providing strong evidence that stochasticity is an intrinsic-and thus unavoidable-property of legged locomotion that should be modeled with care when designing reliable walking machines. We present numerical comparisons of several hypotheses as to the dominant source(s) of this variability: 1) the initial distribution of the angular velocity, 2) the uneven profile of the leg lengths and 3) the distribution of the coefficients of friction and restitution across collisions. Our analysis shows that the 3rd hypothesis most accurately predicts the noise characteristics observed in our experimental data while the 1st hypothesis is also valid for certain contexts of terrain friction. These findings suggest that variability due to ground contact dynamics, and not simply due to geometric variations more typically modeled in terrain, is important in determining the stochasticity and resulting stability of walking robots. Although such ground contact variability might be an expected result in field robotics on significantly rough terrain, we again note our experimental data applies seemingly deterministic-looking terrains: our results suggest that stochastic ground collision models should play an important role in the analysis and optimization of dynamic performance and stability in robot walking.

Robust real time material classification algorithm using soft three axis tactile sensor: Evaluation of the algorithm

Materials and textures identification is a desired ability for robots. Developing such systems require tactile sensors that have enough sensitivity and spatial resolution, and the computational intelligence to meaningfully interpret sensor data. This paper introduces a texture classification algorithm utilizing support vector machine (SVM) classifier. Data taken from a novel three axis tactile sensor that utilize magnetic flux measurements for transduction was used to obtain the three dimensional tactile data. Frobenius norm calculated from the covariance matrix of the above data and the mean values of the three dimensional sensor data were used as features. Palpation velocity and small vertical load variances had minimum influence on the proposed algorithm. We have compared this algorithm with two other classification methods. They are: classify using the feature spatial period that is calculated from principal frequencies of the textures/material, and classify using neural network classifier with special properties of each materials tactile signals as features. For eight classes of material, the proposed algorithm performed faster and more accurately than the comparators when the scanning velocity and the vertical load varied.

A Real-Time State Predictor in Motor Control: Study of Saccadic Eye Movements during Unseen Reaching Movements

Theoretical motor control predicts that because of delays in sensorimotor pathways, a neural system should exist in the brain that uses efferent copy of commands to the arm, sensory feedback, and an internal model of the dynamics of the arm to predict the future state of the hand (i.e., a forward model). We tested this theory under the hypothesis that saccadic eye movements, tracking an unseen reaching movement, would reflect the output of this state predictor. We found that in unperturbed reaching movements, saccade occurrence at any timet consistently provided an unbiased estimate of hand position at t + 196 msec. To investigate the behavior of this predictor during feedback error control, we applied 50 msec random-force perturbations to the moving hand. Saccades showed a sharp inhibition at 100 msec after perturbation. At ∼170 msec, there was a sharp increase in saccade probabilities. These postperturbation saccades were an unbiased estimator of hand position at saccade timet + 150 msec. The ability of the brain to guide saccades to the future position of the hand failed when a force field unexpectedly changed the dynamics of the hand immediately after perturbation. The behavior of the eyes suggested that during reaching movements, the brain computes an estimate of future hand position based on an internal model that relies on real-time proprioceptive feedback. When an error occurs in reaching movements, the estimate of future hand position is recomputed. The saccade inhibition period that follows the hand perturbation may indicate the length of time it takes for this computation to take place.

Design of a variable stiffness flexible manipulator with composite granular jamming and membrane coupling

Robotic manipulators for minimally invasive surgeries have traditionally been rigid, with a steerable end effector. While the rigidity of manipulators improve precision and controllability, it limits reachability and dexterity in constrained environments. Soft manipulators with controllable stiffness on the other hand, can be deployed in single port or natural orifice surgical applications to reach a wide range of areas inside the body, while being able to passively adapt to uncertain external forces, adapt the stiffness distribution to suit the kinematic and dynamic requirements of the task, and provide flexibility for configuration control. Here, we present the design of a snake-like laboratory made soft robot manipulator of 20 mm in average diameter, which can actuate, soften, or stiffen joints independently along the length of the manipulator by combining granular jamming with McKibben actuators. It presents a comprehensive study on the relative contributions of the granule size, material type, and membrane coupling on the range, profile, and variability of stiffness.

Implementation of Tactile Sensing for Palpation in Robot-Assisted Minimally Invasive Surgery: A Review

Robot-assisted minimally invasive surgery (RMIS) made it possible to perform a number of medical manipulations with reduced patient trauma and better accuracy. Various devices, including tactile sensors, have been developed in recent years to enhance the quality of this procedure. The objective of this paper is to review the latest advancements and challenges in the development of tactile sensing devices designed for surgical applications. In particular, the focus is on palpation and probing devices that can be potentially used in RMIS. In addition, we explore the aspects that should be taken into account when designing tactile sensors for RMIS, incorporating biological inspiration of tactile sensing, features of manual palpation, requirements of RMIS. We provide an overview of recommendations for the development of tactile sensing devices, especially in the context of RMIS.

A computationally fast algorithm for local contact shape and pose classification using a tactile array sensor

This paper proposes a new computationally fast algorithm for classifying the primitive shape and pose of the local contact area in real-time using a tactile array sensor attached on a robotic fingertip. The proposed approach abstracts the lower structural property of the tactile image by analyzing the covariance between pressure values and their locations on the sensor and identifies three orthogonal principal axes of the pressure distribution. Classifying contact shapes based on the principal axes allows the results to be invariant to the rotation of the contact shape. A naïve Bayes classifier is implemented to classify the shape and pose of the local contact shapes. Using an off-shelf low resolution tactile array sensor which comprises of 5×9 pressure elements, an overall accuracy of 97.5% has been achieved in classifying six primitive contact shapes. The proposed method is very computational efficient (total classifying time for a local contact shape = 576μs (1736 Hz)). The test results demonstrate that the proposed method is practical to be implemented on robotic hands equipped with tactile array sensors for conducting manipulation tasks where real-time classification is essential.

Locomotion with continuum limbs

This paper presents the kinematics, dynamics, and experimental results for a novel quadruped robot using continuum limbs. We propose soft continuum limbs as a new paradigm for robotic locomotion in unstructured environments due to their potential to generate a wide array of locomotion behaviors ranging from walking, trotting, crawling, and propelling to whole arm grasping as a means of negotiating difficult obstacles. A straightforward method to derive the kinematics and dynamics for the proposed quadruped has been demonstrated through numerical simulations. Initial experiments on a prototype continuum quadruped demonstrate the ability to stand up from a flat-belly stance, absorb external disturbances such as maintaining stability after dropping from a height and after being perturbed by a collision, and crawling on flat and cluttered environments. Experiment results provide evidence that locomotion with soft continuum limbs are feasible and usable in unstructured environments for variety of applications.

A VARIABLE STIFFNESS JOINT BY GRANULAR JAMMING

We propose a novel, high degree of freedom variable stiffness joint for use in a miniature snake-like robot for minimally invasive surgeries via granular jamming. By pulling granule filled membrane-columns under vacuum, the columns and joint stiffen as the granular matter begin to jam. In our experiments, we achieved a four-fold increase in stiffness, and the stiffness can be achieved while the columns are straight or bent. Current flexible manipulators in industrial and medical robotics have followed two dominating methods of actuation and stiffness control. The first method is the continuum manipulator, which utilizes tendons or rods to bend the manipulator in a continuous fashion. The second method is classified as the highly articulated robot, where the manipulator is comprised of multiple segments linked by motordriven universal joints. Like the latter, our manipulator is highly articulated, however stiffness of each joint can be independently controlled by the granular jamming principle. This paper studies the effect of grain type and vacuum pressure for stiffness tuning. We found that granules with a matte surface were able to achieve higher stiffnesses, with a cube shape exhibiting the highest stiffness, but at the cost of high levels of hysteresis.

Bio-inspired tactile sensor sleeve for surgical soft manipulators

Robotic manipulators for Robot-assisted Minimally Invasive Surgery (RMIS) pass through small incisions into the patients body and interact with soft internal organs. The performance of traditional robotic manipulators such as the da Vinci Robotic System is limited due to insufficient flexibility of the manipulator and lack of haptic feedback. Modern surgical manipulators have taken inspiration from biology e.g. snakes or the octopus. In order for such soft and flexible arms to reconfigure itself and to control its pose with respect to organs as well as to provide haptic feedback to the surgeon, tactile sensors can be integrated with the robots flexible structure. The work presented here takes inspiration from another area of biology: cucumber tendrils have shown to be ideal tactile sensors for the plant that they are associated with providing useful environmental information during the plants growth. Incorporating the sensing principles of cucumber tendrils, we have created miniature sensing elements that can be distributed across the surface of soft manipulators to form a sensor network capable of acquire tactile information. Each sensing element is a retractable hemispherical tactile measuring applied pressure. The actual sensing principle chosen for each tactile makes use of optic fibres that transfer light signals modulated by the applied pressure from the sensing element to the proximal end of the robot arm. In this paper, we describe the design and structure of the sensor system, the results of an analysis using Finite Element Modeling in ABAQUS as well as sensor calibration and experimental results. Due to the simple structure of the proposed tactile sensor element, it is miniaturisable and suitable for MIS. An important contribution of this work is that the developed sensor system can be ”loosely” integrated with a soft arm effectively operating independently of the arm and without affecting the arms motion during bending or elongation.

Efficient Break-Away Friction Ratio and Slip Prediction Based on Haptic Surface Exploration

The break-away friction ratio (BF-ratio), which is the ratio between friction force and the normal force at slip occurrence, is important for the prediction of incipient slip and the determination of optimal grasping forces. Conventionally, this ratio is assumed constant and approximated as the static friction coefficient. However, this ratio varies with acceleration rates and force rates applied to the grasped object and the object material, which lead to difficulties in determining optimal grasping forces that avoid slip. In this paper, we propose a novel approach based on the interactive forces to allow a robotic hand to predict object slip before its occurrence. The approach only requires the robotic hand to have a short haptic surface exploration over the object surface before manipulating it. Then, the frictional properties of the finger-object contact can be efficiently identified, and the BF-ratio can be real-time predicted to predict slip occurrence under dynamic grasping conditions. Using the predicted BF-ratio as a slip, threshold is demonstrated to be more accurate than using the static/Coulomb friction coefficient. The presented approach has been experimentally evaluated on different object surfaces, showing good performance in terms of prediction accuracy, robustness, and computational efficiency.