Miguel Moises Serrano

Locomotor benefits of being a slender and slick sand swimmer

Squamates classified as ‘subarenaceous’ possess the ability to move long distances within dry sand; body elongation among sand and soil burrowers has been hypothesized to enhance subsurface performance. Using X-ray imaging, we performed the first kinematic investigation of the subsurface locomotion of the long, slender shovel-nosed snake (Chionactis occipitalis) and compared its biomechanics with those of the shorter, limbed sandfish lizard (Scincus scincus). The sandfish was previously shown to maximize swimming speed and minimize the mechanical cost of transport during burial. Our measurements revealed that the snake also swims through sand by propagating traveling waves down the body, head to tail. Unlike the sandfish, the snake nearly followed its own tracks, thus swimming in an approximate tube of self-fluidized granular media. We measured deviations from tube movement by introducing a parameter, the local slip angle, βs, which measures the angle between the direction of movement of each segment and body orientation. The average βs was smaller for the snake than for the sandfish; granular resistive force theory (RFT) revealed that the curvature utilized by each animal optimized its performance. The snake benefits from its slender body shape (and increased vertebral number), which allows propagation of a higher number of optimal curvature body undulations. The snakes low skin friction also increases performance. The agreement between experiment and RFT combined with the relatively simple properties of the granular ‘frictional fluid’ make subarenaceous swimming an attractive system to study functional morphology and bauplan evolution.

Lower limb pose estimation for monitoring the kicking patterns of infants

Monitoring the spontaneous kicking patterns of infants can give insight into their development. A computer vision based method for estimating the pose of an infants leg from range images is presented in this paper. After some manual inputs for initialization, the range data associated with the infant is extracted. The method uses Robust Point Set Registration (RPSR) to fit an articulated model to the subject in every frame in the sequence, thus it provides the joint trajectories over time of the kicking kinematics. For validation, the method is used to track the articulation of a robotic humanoid that was programmed to kick in a fashion similar to an infant. Furthermore, the method is applied to a sequence collected from an actual infant and the resultant signal estimates are presented.Monitoring the spontaneous kicking patterns of infants can give insight into their development. A computer vision based method for estimating the pose of an infants leg from range images is presented in this paper. After some manual inputs for initialization, the range data associated with the infant is extracted. The method uses Robust Point Set Registration (RPSR) to fit an articulated model to the subject in every frame in the sequence, thus it provides the joint trajectories over time of the kicking kinematics. For validation, the method is used to track the articulation of a robotic humanoid that was programmed to kick in a fashion similar to an infant. Furthermore, the method is applied to a sequence collected from an actual infant and the resultant signal estimates are presented.

Depth invariant visual servoing

This paper studies the problem of achieving consistent performance for visual servoing. Given the nonlinearities introduced by the camera projection equations in monocular visual servoing systems, many control algorithms experience non-uniform performance bounds. The variable performance bounds arise from depth dependence in the error rates. In order to guarantee depth invariant performance bounds, the depth nonlinearity must be cancelled, however estimating distance along the optical axis is problematic when faced with an object with unknown geometry. By tracking a planar visual feature on a given target, and measuring the area of the planar feature, a distance invariant input to state stable visual servoing controller is derived. Two approaches are given for achieving the visual tracking. Both of these approaches avoid the need to maintain long-term tracks of individual feature points. Realistic image uncertainty is captured in experimental tests that control the camera motion in a 3D renderer using the observed image data for feedback.

Incorporating frictional anisotropy in the design of a robotic snake through the exploitation of scales

The scales on the skin of a snake are an integral part of the snakes locomotive capabilities. It stands to reason that the integration of scales into the design of robotic snakes would open new properties to exploit. In this work, we present a robotic snake design that incorporates rigid scales in the casing of each module. To validate the impact of the scales, locomotion is tested under three conditions: with scales, covered in cloth with scales, and covered in cloth without scales. The performance of the snake robot, in each of the aforementioned scenarios, is evaluated based on its forward displacement while executing each of two pre-programmed gaits: inchworm and lateral undulation. The lateral undulation gait is tested under two additional conditions: pitched and un-pitched. Tracks of the experimental runs are presented followed by a statistical analysis demonstrating an increase in locomotive performance when incorporating scales in the chassis design.

Shape-centric modeling of traveling wave rectilinear locomotion for snake-like robots

A traveling wave rectilinear gait for elongated, continuous bodies is modeled as planar deviations along a baseline average body and with respect to an average body frame. This framework enables intuitive and convenient formulation of the gait as a cyclically-varying backbone curve as well as contact patterns and other elements of the model which are integral in computing external forcing. A rolling friction model is introduced to describe interactions with the environment. The equations of motion of the dynamical system are derived in closed form along with the steady-state velocity for a straight-line average body. We demonstrate that control objectives such as turning can be conveniently achieved by introducing a constant radius of curvature into the average body, without any change to the gait formulation.

Visual closed-loop tracking with area stabilization

We present a technique that combines image segmentation with nonlinear control to achieve closed-loop visual tracking. Camera rotation and zoom are used as the controls. The approach uses an analysis of admissible curve evolution flows generated by changes in projective scaling and angular velocity. Lyapunov stability analysis is used to synthesize a control scheme that renders the closed-loop system input-to-state stable (ISS) with respect to unknown target motion. Our formulation avoids the need to constantly perform rigid body camera calibration and motion reconstruction by stating the control goal directly in terms of image data. The results confirm expected tracking performance for a simulated 3D environment containing a camera and moving target.

Automated feet detection for clinical gait assessment

The paper describes a computer vision method for estimating the clinical gait metrics of walking patients in unconstrained environments. The method employs background subtraction to produce a silhouette of the subject and a randomized decision forest to detect their feet. Given the feet detections, the stride and step length, cadence, and walking speed are estimated. Validation of the system is presented through an error analysis on manually annotated videos of subjects walking in different outdoor settings. This method is significant as it provides clinical therapists and non-specialists the opportunity to record from any camera and obtain high accuracy estimates of the clinical gait metrics for subjects walking at outdoor or at-home locations.

Shape-centric modeling of lateral undulation and sidewinding gaits for Snake Robots

Planar equations of motion for lateral undulation and sidewinding by snake robots are derived within a common framework. The equations use a continuous model of the snake shape kinematics. The shape curve is defined as time and arc length parametrized deviations from a fixed curve. Viscous anisotropic friction models the snake-ground interaction. Vertical lifting of the body off of the ground plane defines time and spatially varying contact profiles in the planar model, which influence the external forcing applied to the system. We demonstrate this framework facilitates intuitive and convenient formulation of traveling wave gaits, in the form of cyclically-varying backbone curves and ground contact patterns. Simulation results illustrate that both lateral undulation and sidewinding gaits are indeed modeled by this single formulation.