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.

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.

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.

Maintaining the grip on an artery with a pulsating impedance, holding the steering wheel of a vehicle on a bumpy terrain, or holding a live hamster without excessive squeezing may be trivial tasks to most humans. However, a robot will find it very difficult to maintain the grip of such uncertain objects based on real-time feedback control. This paper presents a stochastic control law to maintain the grip on an uncertain object while manipulating against external forces. The radial impedance parameters of the soft object is assumed to undergo Gaussian random variations. Here we demonstrate that the proposed model free grip controller can maintain a safe grip at two diagonally opposite points of the object merely based on the statistics of the normal force. It accomplishes this by computing a probability of grip failure to adapt the compression on the soft object. A novel optimal estimation algorithm that can concurrently estimate the unknown impedance parameters of the object and the states of the coupled dynamic system is discussed as a potential tool to be used in predictive optimal impedance control on uncertain objects. Experimental results on adaptive grip control on a cylindrical tube inflated and deflated with a Gaussian random variation has been presented to validate the algorithm.

Object surface properties are among the most important information which a robot requires in order to effectively interact with an unknown environment. This paper presents a novel haptic exploration strategy for recognizing the physical properties of unknown object surfaces using an intelligent finger. This developed intelligent finger is capable of identifying the contact location, normal and tangential force, and the vibrations generated from the contact in real time. In the proposed strategy, this finger gently slides along the surface with a short stroke while increasing and decreasing the sliding velocity. By applying a dynamic friction model to describe this contact, rich and accurate surface physical properties can be identified within this stroke. This allows different surface materials to be easily distinguished even if when they have very similar texture. Several supervised learning algorithms have been applied and compared for surface recognition based on the obtained surface properties. It has been found that the naïve Bayes classifier is superior to radial basis function network and k-NN method, achieving an overall classification accuracy of 88.5% for distinguishing twelve different surface materials.

Slip prediction is important for maintaining the stability of object handling in robust grasping and dexterous manipulation. However, up to date a challenge still remains that how to accurately predict slip occurrence before it actually happens to allow robotic hands to conduct slip compensation in time. The concept of friction cone has been conventionally used to predict slip occurrence, where the static/kinetic friction coefficient is used as a threshold. However, this threshold, i.e. the ratio of the friction and normal forces at slip occurrence (also named as break-away friction ratio), is found not constant but varies with changes in acceleration and disturbing forces applied on the grasped object, raising difficulties when attempting to accurately predict slip. In this paper, we propose a novel approach to accurately predict varying slip thresholds in real time and compensate the predicted slip during a dynamic grasping. To achieve this, first a simple but efficient haptic surface exploration using robotic fingers is carried out to identify the friction properties of an object surface. Once the friction properties are established, the slip threshold at a given grasping condition can be predicted and the grasping forces are adjusted to prevent slip. The presented approach has been evaluated, showing good performance in terms of prediction accuracy and computational efficiency.

This paper presents a novel optical fiber-based rolling indentation probe designed to measure the stiffness distribution of a soft tissue while rolling over the tissue surface during minimally invasive surgery. By fusing the measurements along rolling paths, the probe can generalize a mechanical image to visualize the stiffness distribution within the internal tissue structure. Since tissue abnormalities are often firmer than the surrounding organ or parenchyma, a surgeon then can localize abnormalities by analyzing the image. The performance of the developed probe was validated using simulated soft tissues. Results show that the probe can measure both force and indentation depth accurately with different orientations when the probe approached and rolled on the tissue surface. In addition, experiments for tumor, identification through rolling indentation were conducted. The size and embedded depth of the tumor, as well as the stiffness ratio between the tumor and tissue, were varied during tests. Results demonstrate that the probe can effectively and accurately identify the embedded tumors.

Slip plays a vital role in traction control when a mobile robot traverses over soft soils. To estimate slip parameters, accurate measurement of robot velocity is particularly required. Previous related work done by the authors has adopted a single downward-looking single camera system for velocity and slip estimation [1][2]; however, such a single camera system is prone to lose accuracy when the distance between the camera and terrain is time-varying, such as traversing over uneven terrains [1]. To cope with the problem, this paper presents a robust downward-looking camera based velocity estimation approach, which can particularly be capable of identifying height variation and compensating for velocity estimation. A downward-looking stereo camera instead of previously used single camera is adopted. The camera-terrain distance can be estimated by matching same features in left and right frames. Robot velocity measured with height compensation can be more accurate, compared to estimates without it. The proposed approach has been validated through comprehensive experimental study on a lab-based test rig; and test results show good performance of the proposed approach. With the proposed method, slip estimation techniques given by [2][3] can be promisingly extended to non-flat terrains.

This paper presents a robust slip estimation method for skid-steered mobile robots when they traverse over rough terrain. An optical flow-based visual sensor looking down the terrain surface is employed to recover motion of a mobile robot by tracking features selected from the terrain surface. The motion states of the mobile robot are initially estimated by the visual sensor, however, the estimates are prone to noise and uncertainty which degrades the accuracy and robustness of estimation. To cope with the noise and uncertainty from the visual sensor, a sliding mode observer (SMO) based on the kinematics model of the skid-steered mobile robot is delicately designed to simultaneously estimate slip parameters. The SMO scheme can give more accurate estimates than the extended Kalman filter (EKF) when the slip of the mobile robot has significant changes at abrupt steering. The complete slip estimation method is independent of terrain parameters and robust in the presence of noise and uncertainty. Experimental results show that the method has confident potential for slip estimation of skid-steered mobile robots.

This paper proposes a novel technique to estimate slips and velocities of an unmanned skid-steered vehicle. An optical flow-based visual sensor looking down the terrain surface is employed to recover the motion of the vehicle by tracking features selected from the terrain surface. The special orientation of the on-board camera is to assure high accuracy of the motion estimation. To cope with the noise and uncertainty from the visual sensor, a sliding mode observer (SMO) based on the kinematic model of the skid-steered vehicle is delicately designed to simultaneously estimate the slips and velocities. The complete non-GPS slip and velocity estimation technique is independent of terrain parameters and robust to noise and uncertainty. The SMO scheme can produce more accurate estimates than the extended Kalman filter (EKF) in the nonlinear case. Experimental results are given to show that the technique has good potential for vehicle slip and velocity estimation.