Individual recognition of Eurasian beavers (Castor fiber) by their tail patterns using a computer-assisted pattern-identification algorithm.

Individual recognition of animals is an important aspect of ecological sciences. Photograph-based individual recognition options are of particular importance since these represent a non-invasive method to distinguish and identify individual animals. Recent developments and improvements in computer-based approaches make possible a faster semi-automated evaluation of large image databases than was previously possible. We tested the Scale Invariant Feature Transform (SIFT) algorithm, which extracts distinctive invariant features of images robust to illumination, rotation or scaling of images. We applied this algorithm to a dataset of 800 tail pattern images from 100 individual Eurasian beavers (Castor fiber) collected as part of the Norwegian Beaver Project (NBP). Images were taken using a single-lens reflex camera and the pattern of scales on the tail, similar to a human fingerprint, was extracted using freely accessible image processing programs. The focus for individual recognition was not on the shape or the scarring of the tail, but purely on the individual scale pattern on the upper (dorsal) surface of the tail. The images were taken from two different heights above ground, and the largest possible area of the tail was extracted. The available data set was split in a ratio of 80% for training and 20% for testing. Overall, our study achieved an accuracy of 95.7%. We show that it is possible to distinguish individual beavers from their tail scale pattern images using the SIFT algorithm.

Bidirectional biomimetic flow sensing with antiparallel and curved artificial hair sensors.

Background: Flow stimuli in the natural world are varied and contain a wide variety of directional information. Nature has developed morphological polarity and bidirectional arrangements for flow sensing to filter the incoming stimuli. Inspired by the neuromasts found in the lateral line of fish, we present a novel flow sensor design based on two curved cantilevers with bending orientation antiparallel to each other. Antiparallel cantilever pairs were designed, fabricated and compared to a single cantilever based hair sensor in terms of sensitivity to temperature changes and their response to changes in relative air flow direction. Results: In bidirectional air flow, antiparallel cantilever pairs exhibit an axially symmetrical sensitivity between 40 µV/(m s-1) for the lower air flow velocity range (between ±10-20 m s-1) and 80 µV/(m s-1) for a higher air flow velocity range (between ±20-32 m s-1). The antiparallel cantilever design improves directional sensitivity and provides a sinusoidal response to flow angle. In forward flow, the single sensor reaches its saturation limitation, flattening at 67% of the ideal sinusoidal curve which is earlier than the antiparallel cantilevers at 75%. The antiparallel artificial hair sensor better compensates for temperature changes than the single sensor. Conclusion: This work demonstrated the successive improvement of the bidirectional sensitivity, that is, improved temperature compensation, decreased noise generation and symmetrical response behaviour. In the antiparallel configuration, one of the two cantilevers always extends out into the free stream flow, remaining sensitive to directional flow and preserving a sensitivity to further flow stimuli.

Nitride-Based Materials for Flexible MEMS Tactile and Flow Sensors in Robotics.

The response to different force load ranges and actuation at low energies is of considerable interest for applications of compliant and flexible devices undergoing large deformations. We present a review of technological platforms based on nitride materials (aluminum nitride and silicon nitride) for the microfabrication of a class of flexible micro-electro-mechanical systems. The approach exploits the material stress differences among the constituent layers of nitride-based (AlN/Mo, Si x N y /Si and AlN/polyimide) mechanical elements in order to create microstructures, such as upwardly-bent cantilever beams and bowed circular membranes. Piezoresistive properties of nichrome strain gauges and direct piezoelectric properties of aluminum nitride can be exploited for mechanical strain/stress detection. Applications in flow and tactile sensing for robotics are described.

Modelling of a biologically inspired robotic fish driven by compliant parts.

Inspired by biological swimmers such as fish, a robot composed of a rigid head, a compliant body and a rigid caudal fin was built. It has the geometrical properties of a subcarangiform swimmer of the same size. The head houses a servo-motor which actuates the compliant body and the caudal fin. It achieves this by applying a concentrated moment on a point near the compliant body base. In this paper, the dynamics of the compliant body driving the robotic fish is modelled and experimentally validated. Lighthill's elongated body theory is used to define the hydrodynamic forces on the compliant part and Rayleigh proportional damping is used to model damping. Based on the assumed modes method, an energetic approach is used to write the equations of motion of the compliant body and to compute the relationship between the applied moment and the resulting lateral deflections. Experiments on the compliant body were carried out to validate the model predictions. The results showed that a good match was achieved between the measured and predicted deformations. A discussion of the swimming motions between the real fish and the robot is presented.

Self-motion effects on hydrodynamic pressure sensing: part I. forward-backward motion.

In underwater locomotion, extracting meaningful information from local flows is as desirable as it is challenging, due to complex fluid-structure interaction. Sensing and motion are tightly interconnected; hydrodynamic signals generated by the external stimuli are modified by the self-generated flow signals. Given that very little is known about self-generated signals, we used onboard pressure sensors to measure the pressure profiles over the head of a fusiform-shape craft while moving forward and backward harmonically. From these measurements we obtained a second-order polynomial model which incorporates the velocity and acceleration of the craft to estimate the surface pressure within the swimming range up to one body length/second (L s(-1)). The analysis of the model reveals valuable insights into the temporal and spatial changes of the pressure intensity as a function of craft's velocity. At low swimming velocities (<0.2 L s(-1)) the pressure signals are more sensitive to the acceleration of the craft than its velocity. However, the inertial effects gradually become less important as the velocity increases. The sensors on the front part of the craft are more sensitive to its movements than the sensors on the sides. With respect to the hydrostatic pressure measured in still water, the pressure detected by the foremost sensor reaches values up to 300 Pa at 1 L s(-1) swimming velocity, whereas the pressure difference between the foremost sensor and the next one is less than 50 Pa. Our results suggest that distributed pressure sensing can be used in a bimodal sensing strategy. The first mode detects external hydrodynamic events taking place around the craft, which requires minimal sensitivity to the self-motion of the craft. This can be accomplished by moving slowly with a constant velocity and by analyzing the pressure gradient as opposed to absolute pressure recordings. The second mode monitors the self-motion of the craft. It is shown here that distributed pressure sensing can be used as a speedometer to measure the craft's velocity.

Hydrodynamic pressure sensing with an artificial lateral line in steady and unsteady flows.

With the overall goal being a better understanding of the sensing environment from the local perspective of a situated agent, we studied uniform flows and Kármán vortex streets in a frame of reference relevant to a fish or swimming robot. We visualized each flow regime with digital particle image velocimetry and then took local measurements using a rigid body with laterally distributed parallel pressure sensor arrays. Time and frequency domain methods were used to characterize hydrodynamically relevant scenarios in steady and unsteady flows for control applications. Here we report that a distributed pressure sensing mechanism has the capability to discriminate Kármán vortex streets from uniform flows, and determine the orientation and position of the platform with respect to the incoming flow and the centre axis of the Kármán vortex street. It also enables the computation of hydrodynamic features which may be relevant for a robot while interacting with the flow, such as vortex shedding frequency, vortex travelling speed and downstream distance between vortices. A Kármán vortex street was distinguished in this study from uniform flows by analysing the magnitude of fluctuations present in the sensor measurements and the number of sensors detecting the same dominant frequency. In the Kármán vortex street the turbulence intensity was 30% higher than that in the uniform flow and the sensors collectively sensed the vortex shedding frequency as the dominant frequency. The position and orientation of the sensor platform were determined via a comparative analysis between laterally distributed sensor arrays; the vortex travelling speed was estimated via a cross-correlation analysis among the sensors.

What information do Kármán streets offer to flow sensing?

In this work, we focus on biomimetic lateral line sensing in Kármán vortex streets. After generating a Kármán street in a controlled environment, we examine the hydrodynamic images obtained with digital particle image velocimetry (DPIV). On the grounds that positioning in the flow and interaction with the vortices govern bio-inspired underwater locomotion, we inspect the fluid in the swimming robot frame of reference. We spatially subsample the flow field obtained using DPIV to emulate the local flow around the body. In particular, we look at various sensor configurations in order to reliably identify the vortex shedding frequency, wake wavelength and downstream flow speed. Moreover, we propose methods that differentiate between being in and out of the Kármán street with >70% accuracy, distinguish right from left with respect to Kármán vortex street centreline (>80%) and highlight when the sensor system enters the vortex formation zone (>75%). Finally, we present a method that estimates the relative position of a sensor array with respect to the vortex formation point within 15% error margin.

Take-off and landing forces and the evolution of controlled gliding in northern flying squirrels Glaucomys sabrinus.

Flying squirrels are well known for their ability to glide between trees at the top of a forest canopy. We present experimental performance and behavioural evidence that flight in flying squirrels may have evolved out of a need to control landing forces. Northern flying squirrels were filmed jumping from a horizontal branch to a much larger vertical pole. These were both slightly compliant (less than 1.9 mm N(-1)), and instrumented using strain gauges so that forces could be measured. Take-off and landing forces were both positively correlated with horizontal range between 0.5 and 2.5 m (r=0.355 and r=0.811, respectively, P<0.05), but not significantly different to each other at each range tested. Take-off forces ranged from 1 to 10 bodyweights, and landing forces were between 3 and 10 bodyweights. Glide angles increased rapidly with horizontal range, approaching 45 degrees at 3 m, above which they gradually decreased, suggesting that northern flying squirrels are optimised for long distance travel. We show that northern flying squirrels initiate full gliding posture at ranges of less than 1 m, without landing any higher than an equivalent ballistic projectile. However, this gliding posture enables them to pitch upwards, potentially stalling the wing, and spreads the landing reaction force over all four extended limbs. At steeper approach angles of close to 45 degrees , flying squirrels were unable to pitch up sufficiently and landed forelimbs first, consequently sustaining higher impact forces. We investigate four hypotheses to explain the origin of flight in these animals and conclude that the need to reduce landing impact forces was most likely to have stimulated the development of aerial control in flying squirrels.

Frequency tuning in animal locomotion.

In locomotion that involves repetitive motion of propulsive structures (arms, legs, fins, wings) there are resonant frequencies f(*) at which the energy consumption is a minimum. As animals need to change their speed, they can maintain this energy minimum by tuning their body resonances. We discuss the physical principles of frequency tuning, and how it relates to forces, damping, and oscillation amplitude. The resonant frequency of pendulum-type oscillators (e.g. swinging arms and legs) may be changed by varying the mass moment of inertia, or the vertical acceleration of the pendulum pivot. The frequency of elastic vibrations (e.g. the bell of a jellyfish) can be tuned with a non-linear modulus of elasticity: soft for low deflection amplitudes (low resonant frequency), and stiff for large displacements (high resonant frequency). Tuning of elastic oscillations can also be achieved by changing the effective length or cross-sectional area of the elastic members, or by allowing springs in parallel or in series to become active. We propose that swimming and flying animals generate oscillating propulsive forces from precisely placed shed vortices and that these tuned motions can only occur when vortex shedding and the simple harmonic motion of the elastic elements of the propulsive structures are in resonance.

The modulus of elasticity of fibrillin-containing elastic fibres in the mesoglea of the hydromedusa Polyorchis penicillatus.

Hydromedusan jellyfish swim by rhythmic pulsation of their mesogleal bells. A single swimming muscle contracts to create thrust by ejecting water from the subumbrellar cavity. At the end of the contraction, energy stored in the deformation of the mesogleal bell powers the refilling stage, during which water is sucked back into the subumbrellar cavity. The mesoglea is a mucopolysaccharide gel reinforced with radially oriented fibres made primarily of a protein homologous to mammalian fibrillin. Most of the energy required to power the refill stroke is thought to be stored by stretching these fibres. The elastic modulus of similar fibrillin-rich fibres has been measured in other systems and found to be in the range of 0.2 to 1.1 MPa. In this paper, we measured the diameters of the fibres, their density throughout the bell, and the mechanical behaviour of the mesoglea, both in isolated samples and in an intact bell preparation. Using this information, we calculated the stiffness of the fibres of the hydromedusa Polyorchis penicillatus, which we found to be approximately 0.9 MPa, similar in magnitude to other species. This value is two orders of magnitude more compliant than the stiffness of the component fibrillin microfibrils previously reported. We show that the structure of the radial fibres can be modelled as a parallel fibre-reinforced composite and reconcile the stiffness difference by reinterpreting the previously reported data. We separate the contributions to the bell elasticity of the fibres and mesogleal matrix and calculate the energy storage capacity of the fibres using the calculated value of their stiffness and measured densities and diameters. We conclude that there is enough energy potential in the fibres alone to account for the energy required to refill the subumbrellar cavity.