José Cappelletto

Colony geometry and structural complexity of the endangered species Acropora cervicornis partly explains the structure of their associated fish assemblage

In the past decade, significant efforts have been made to describe fish-habitat associations. However, most studies have oversimplified actual connections between fish assemblages and their habitats by using univariate correlations. The purpose of this study was to identify the features of habitat forming corals that facilitate and influences assemblages of associated species such as fishes. For this we developed three-dimensional models of colonies of Acropora cervicornis to estimate geometry (length and height), structural complexity (i.e., volume, density of branches, etc.) and biological features of the colonies (i.e., live coral tissue, algae). We then correlated these colony characteristics with the associated fish assemblage using multivariate analyses. We found that geometry and complexity were better predictors of the structure of fish community, compared to other variables such as percentage of live coral tissue or algae. Combined, the geometry of each colony explained 40% of the variability of the fish assemblage structure associated with this coral species; 61% of the abundance and 69% of fish richness, respectively. Our study shows that three-dimensional reconstructions of discrete colonies of Acropora cervicornis provides a useful description of the colonial structural complexity and may explain a great deal of the variance in the structure of the associated coral reef fish community. This demonstration of the strongly trait-dependent ecosystem role of this threatened species has important implications for restoration and conservation efforts.

Through the development of a biomechatronic knee prosthesis for transfemoral amputees: Mechanical design and manufacture, human gait characterization, intelligent control strategies and tests

This paper presents the development of a biomechatronic knee prosthesis for transfemoral amputees. This kind of prostheses are considered ‘intelligent’ because they are able to automatically adapt their response at the knee axis, as a natural knee does. This behavior is achieved by characterizing the amputees gait through the signals captured with instrumentation of a prosthesis, which provides feedback about its current state along the gait cycle and therefore responds with the corresponding control action. In this case, unlike other commercially available intelligent knee prostheses, gait cycle characterization is based on accelerometers signals processed by an events detection algorithm. Two intelligent control strategies are presented: a bio-inspired approach, that consists of using a central pattern generator to generate a knee angle reference to be followed by the prosthesis during walking, and an adaptive scheme, that applies a control action proportional to the knee angle according to an auto-adaptive parameter dependant on gait speed. The mechanical design of the prosthesis is also presented, showing the knee joint mechanism and part of the manufacturing process. Results obtained from walking tests with both able body and amputees are shown, demonstrating the positive performance of the prosthesis in several aspects. Future works aimed at a finished product are also stated.

Model predictive control of remotely operated underwater vehicles

This paper describes the implementation of a model predictive controller novel in an underwater robot vehicle. This work also shows the development of an underwater vehicle model that accounts for physical, hydrodynamic and restorative effects, while the damping coefficients are neglected in the prediction of the vehicle position and orientation. The vehicle kinematic and dynamic models are linearized and arranged into the state space form inside the predictive controller. The model helps to determine the future position and orientation of the vehicle to track a predefined underwater trajectory in an optimal way. The results show that the predictive controller offered significant benefits compared to PID controllers by reducing the MSE and RMS by 40% and 76% respectively.

Vision-Based Dynamic Velocity Field Generation for Mobile Robots

A control strategy much studied in the last years is the velocity field control (VFC). In 1995, velocity fields are defined as a set of velocity vectors located at each possible position of the robot platform inside its workspace coding a specified task [1]. The employment of a control scheme whose reference is a velocity field has allowed to encourage different control problems rather than the conventional timed trajectory tracking, such as contour following, position control, etc.

Object recognition for obstacle avoidance in mobile robots

In this paper is shown an obstacle avoidance strategy based on object recognition using an artificial vision application. Related works focus on the implementation of efficient algorithms for image processing. This work emphasizes in using minimum information from an image in order to generate free obstacles trajectories. The algorithm used is based on Pattern Matching for detection of the robot and Classification for the rest of objects. Each form of detection has its particular algorithm: Cross Correlation for Pattern matching and Nearest Neighbor for Classification. The objective pursued is to demonstrate that, with a very simple system, precise information can be provided to a navigation system in order to find free obstacle paths.

Gait Synthesis in Legged Robot Locomotion using a CPG-Based Model

Biology has always been a source of inspiration and ideas for the robotics community. Legged locomotion problem is not an exception, and many experiences have taken ideas from animals, both for morphological and behavioral issues. The first ideas for gait generation came from animal observation, but they were mainly focused on mimicking legs movements. It was not until the nineties that the first relevant works appeared trying to identify the principles behind the generation of those movements in animals. The proposed models were based on neurophysiologic principles, and most of them tried to include characteristics of animal locomotion by the addition of neural networks, dynamic oscillators, or using a set of “movement rules”. Although many models have been suggested, most of them share some common aspects: 1. Motion signals generation and processing are very slow and highly distributed processes. 2. The brain tends to perform high level feed-forward movement control and prediction. 3. The locomotion system has local feedback, from pressure sensors, force sensors, intramuscular sensors, etc. In some processes these characteristics are obvious, like in the heart beating or breathing. In these processes there is no need for the intervention of a complex processing unit like the brain, since most of the coordinated oscillatory behavior of the muscles is carried out locally and distributed. The oscillatory nature of locomotion patterns has attracted studies about the existence of a similar structure in charge of this problem. The biologic and electrochemical bases of the system in animals are fairly well explained in the works on neural networks by Hodgkin-Huxley (Hodgkin, 1952). Another important characteristic of animal systems is that biological neural networks can perform timing tasks through oscillatory networks, and also can modulate neuromuscular excitatory signals, thus giving the ability to

Analysis of Oscillators for the Generation of Rhythmic Patterns in Legged Robot Locomotion

This paper presents a study of several oscillators for the generation of rhythmic patterns that are used in the locomotion of legged robots using a central pattern generator (CPG). This work also shows a comparative analysis of these oscillators focused on their implementation as part of the CPG system, based on simulations made using software tools. Three models were implemented and simulated: the Hopf model as an example of a harmonic oscillator, a reflexive oscillator using the Van der Pol oscillator, and the ACPO (amplitude coupled phase oscillator) corresponding to the phase oscillators category. It is established that a phase oscillator is the best choice when working with CPGs offering a simple yet versatile way to connect the limbs.

Cybernetic knee prosthesis: application of an adaptive central pattern generator

Purpose – The purpose of this paper is to generate a virtual knee angle reference to be followed by a knee prosthesis control, using an adaptive central pattern generator (CPG). Also, to study the feasibility of this approach to implement a continuous control strategy on the prosthesis.Design/methodology/approach – A CPG based on amplitude controlled phase oscillators (ACPOs) to track the current percentage of gait cycle on the prosthesis is proposed. Then, the virtual knee angle reference is generated along gait cycle, by interpolation with the corresponding angle of a sound knee. The structure and coupling of the CPG, as well as the control strategy are presented.Findings – The coupling of the CPG with real gait on the prosthesis was proven, regardless of gait speed. Also, it was found that the maximum knee angle reached during walking is proportional to gait speed. Finally, generation of virtual knee angle reference to be followed by a prosthesis is demonstrated.Research limitations/implications – As o...

The effect of social learning in a small population facing environmental change: an agent-based simulation

Learning is defined as behavioral modification due to experience, social or asocial. Social learning might be less costly than asocial learning and allow the rapid accumulation of learned traits across generations. However, the benefits of social learning in a small population of individuals relying on local interactions and experiencing environmental change are not well understood yet. In this study, we used agent-based simulations to address this issue by comparing the performance of social learning to asocial learning and innate behavior, in both a static and a changing environment. Learning was modeled using neural networks, and innate behavior was modeled using genetically coded behaviors. The performance of 10 mobile simulated agents was measured under three environmental scenarios: static, abrupt change and gradual change. We found that social learning allows for a better performance (in terms of survival) than asocial learning in static and abrupt-change scenarios. In contrast, when changes are gradual, social learning delays achieving the correct alternative, while asocial learning facilitates innovation; interestingly, a mixed population (social and asocial learners) performs the best.

Frequency to Voltage Converter as a Phase Controller in Phase Shifting Interference Microscopy

A new system for the control of a piezoelectric device is introduced. It works as a phase shifter in an interference optical microscope. This circuit translates controllable constant frequency signals, generated with a standard sound card, to electrical signals with constant voltage through a frequency-to-voltage converter; this full analog conversion allows for producing voltage steps of 16 µv, which is a better resolution than a commercial digital to analog converter working (in the desired voltage range), and hence, results in an arbitrary phase uncertainty under 3%. The calibration method allows for verification of the displacements steadfastness in the arbitrary phase, and for diagnosing of errors that could alter the experimental interferograms. To check the system calibration, it was experimentally and successfully tested on a silicon surface with rectangular holes of known depth.