Esther Luna Colombini

sEMG feature evaluation for identification of elbow angle resolution in graded arm movement

Automatic and accurate identification of elbow angle from surface electromyogram (sEMG) is essential for myoelectric controlled upper limb exoskeleton systems. This requires appropriate selection of sEMG features, and identifying the limitations of such a system.This study has demonstrated that it is possible to identify three discrete positions of the elbow; full extension, right angle, and mid-way point, with window size of only 200 milliseconds. It was seen that while most features were suitable for this purpose, Power Spectral Density Averages (PSD-Av) performed best. The system correctly classified the sEMG against the elbow angle for 100% cases when only two discrete positions (full extension and elbow at right angle) were considered, while correct classification was 89% when there were three discrete positions. However, sEMG was unable to accurately determine the elbow position when five discrete angles were considered. It was also observed that there was no difference for extension or flexion phases.

TORP: The Open Robot Project

The development of robots has shown itself as a very complex interdisciplinary research field. The predominant procedure for these developments in the last decades is based on the assumption that each robot is a fully personalized project, with the direct embedding of hardware and software technologies in robot parts with no level of abstraction. Although this methodology has brought countless benefits to the robotics research, on the other hand, it has imposed major drawbacks: (i) the difficulty to reuse hardware and software parts in new robots or new versions; (ii) the difficulty to compare performance of different robots parts; and (iii) the difficulty to adapt development needs—in hardware and software levels—to local groups expertise. Large advances might be reached, for example, if physical parts of a robot could be reused in a different robot constructed with other technologies by other researcher or group. This paper proposes a framework for robots, TORP (The Open Robot Project), that aims to put forward a standardization in all dimensions (electrical, mechanical and computational) of a robot shared development model. This architecture is based on the dissociation between the robot and its parts, and between the robot parts and their technologies. In this paper, the first specification for a TORP family and the first humanoid robot constructed following the TORP specification set are presented, as well as the advances proposed for their improvement.

A Framework for Learning in Humanoid Simulated Robots

One of the most important characteristics of intelligent activity is the ability to change behaviour according to many forms of feedback. Through learning an agent can interact with its environment to improve its performance over time. However, most of the techniques known that involves learning are time expensive, i.e., once the agent is supposed to learn over time by experimentation, the task has to be executed many times. Hence, high fidelity simulators can save a lot of time. In this context, this paper describes the framework designed to allow a team of real RoboNova-Ihumanoids robots to be simulated under USARSimenvironment. Details about the complete process of modeling and programming the robot are given, as well as the learning methodology proposed to improve robots performance. Due to the use of a high fidelity model, the learning algorithms can be widely explored in simulation before adapted to real robots.

Stable and fast model-free walk with arms movement for humanoid robots

Controlling a humanoid robot with its typical many degrees of freedom is a complex task, and many methods have been proposed to solve the problem of humanoid locomotion. In this work, we generate a gait for a Hitec Robonova-I robot using a model-free approach, where fairly simple parameterized models, based on truncated Fourier series, are applied to generate joint angular trajectories. To find a parameter set that generates a fast and stable walk, optimization algorithms were used, specifically a genetic algorithm and particle swarm optimization. The optimization process was done in simulation first, and the learned walk was then adapted to the real robot. The simulated model of the Robonova-I was made using the USARSim simulator, and tests made to evaluate the resulting walks verified that the best walk obtained is faster than the ones publicly available for the Robonova-I. Later, to provide an additional validation, the same process was carried out for the simulated Nao from the RoboCup 3D Soccer Simulation League. Again, the resulting walk was fast and stable, overcoming the speed of the publicly available magma-AF base team.