Construcción, simulación y programación de un robot cuadrúpedo multipropósito de código abierto

En este proyecto se pretende modelar, simular y construir un robot cuadrúpedo basado en la plataforma open source Spot Micro (robot inspirado en Spot mini de Boston Dynamics). El objetivo es mediante el estudio de los distintos sistemas actualmente disponibles, desarrollar una plataforma robótica de bajo coste multipropósito, por fases de desarrollo. Se explorará el sistema actual, se corregirán los defectos y finalmente se mejorará dotándolo con la posibilidad de alcanzar cierto nivel de autonomía. Se pretende también generar un programa de gestión que permita el control de sus articulaciones y de los distintos sensores incorporados así como establecer las bases para el entrenamiento de un agente con aprendizaje por refuerzo que sirva como futuro controlador del robot vía teleoperación o control manual

Multi-Objective Optimization for Speed and Stability of a Sony Aibo Gait

Locomotion is a fundamental facet of mobile robotics that many higher level aspects rely on. However, this is not a simple problem for legged robots with many degrees of freedom. For this reason, machine learning techniques have been applied to the domain. Although impressive results have been achieved, there remains a fundamental problem with using most machine learning methods. The learning algorithms usually require a large dataset which is prohibitively hard to collect on an actual robot. Further, learning in simulation has had limited success transitioning to the real world. Also, many learning algorithms optimize for a single fitness function, neglecting many of the effects on other parts of the system. As part of the RoboCup 4-legged league, many researchers have worked on increasing the walking/gait speed of Sony AIBO robots. Recently, the effort shifted from developing a quick gait, to developing a gait that also provides a stable sensing platform. However, to date, optimization of both velocity and camera stability has only occurred using a single fitness function that incorporates the two objectives with a weighting that defines the desired tradeoff between them. However, the true nature of this tradeoff is not understood because the pareto front has never been charted, so this a priori decision is uninformed. This project applies the Nondominated Sorting Genetic Algorithm-II (NSGA-II) to find a pareto set of fast, stable gait parameters. This allows a user to select the best tradeoff between balance and speed for a given application. Three fitness functions are defined: one speed measure and two stability measures. A plot of evolved gaits shows a pareto front that indicates speed and stability are indeed conflicting goals. Interestingly, the results also show that tradeoffs also exist between different measures of stability