Predictive and Multi-rate Sensor-Based Planning under Uncertainty

Email Print Request Permissions In this paper, a general formulation of a predictive and multirate (MR) reactive planning method for intelligent vehicles (IVs) is introduced. The method handles path planning and trajectory planning for IVs in dynamic environments with uncertainty, in which the kinodynamic vehicle constraints are also taken into account. It is based on the potential field projection method (PFP), which combines the classical potential field (PF) method with the MR Kalman filter estimation. PFP takes into account the future object trajectories and their associated uncertainties, which makes it different from other look-ahead approaches. Here, a new PF is included in the Lagrange-Euler formulation in a natural way, accounting for the vehicle dynamics. The resulting accelerations are translated into control inputs that are considered in the estimation process. This leads to the generation of a local trajectory in real time (RT) that fully meets the constraints imposed by the kinematic and dynamic models of the IV. The properties of the method are demonstrated by simulation with MATLAB and C++ applications. Very good performance and execution times are achieved, even in challenging situations. In a scenario with 100 obstacles, a local trajectory is obtained in less than 1 s, which is suitable for RT applications

Estimación de posturas de agarre en base a ACP y RN

Comunicación presentada en el XXI Congreso Nacional de Ingeniería Mecánica, celebrado en Elche en Noviembre de 2016.La investigación en manos antropomorfas robóticas y protésicas experimenta un auge en la actualidad. Unos diseños intentan lograr un agarre estable y diestro mientras que otros pretenden alcanzar un elevado grado de antropomorfismo y de apariencia cosmética. Sin embargo, las prótesis de mano existentes suelen ser muy simples desde un punto de vista biomecánico. Esto se debe a la complejidad de establecer una interacción adecuada entre el amputado y una prótesis de múltiples grados de libertad, que requeriría numerosas señales de control independientes y un controlador inteligente. Una innovación en el ámbito del control de manos artificiales podría derivarse de la observación e imitación del comportamiento biomecánico natural, en base a un espacio de dimensionalidad reducida. El presente trabajo plantea la utilización del análisis de componentes principales (ACP), para reducir la dimensionalidad del problema de control, en combinación con las redes neuronales (RN), para predecir la posturas de la mano en dos tipos de agarre sobre objetos cilíndricos: un agarre de potencia (cilíndrico) y un agarre de precisión (pinza con 5 dedos). El objetivo es determinar el mínimo número de entradas de control necesarias para que una mano protésica avanzada pueda realizar actividades de la vida diaria en base a patrones posturales identificados y evaluar su posibilidad de control real. Para ello, se realizaron experimentos de agarre con 16 sujetos diestros y 4 cilindros de diversos diámetros durante los que se registró la posición de 32 marcadores. A partir de estos datos se calcularon los ángulos de articulación de la mano para cada postura de agarre (PA). Posteriormente, se realizó un ACP sobre los datos de PA, obteniendo 7 componentes principales (posturas propias de agarre, PPA) que determinaron las sinergias posturales producidas durante el agarre. El resultado se simuló mediante OpenSim. Los datos obtenidos se utilizaron para entrenar y validar una RN para estimar PA a partir de las PPA, con una arquitectura previamente validada compuesta por dos capas. Finalmente, se calculó la raíz cuadrada del error cuadrático medio global y por articulación de la predicción realizada por la RN con respecto a la postura experimental, obteniendo resultados alentadores.El presente trabajo está financiado por la Generalitat Valenciana a través del proyecto GV/2015/101

Hand posture prediction using neural networks within a biomechanical model

This paper proposes the use of artificial neural networks (ANNs) in the framework of a biomechanical hand model for grasping. ANNs enhance the model capabilities as they substitute estimated data for the experimental inputs required by the grasping algorithm used. These inputs are the tentative grasping posture and the most open posture during grasping. As a consequence, more realistic grasping postures are predicted by the grasping algorithm, along with the contact information required by the dynamic biomechanical model (contact points and normals). Several neural network architectures are tested and compared in terms of prediction errors, leading to encouraging results. The performance of the overall proposal is also shown through simulation, where a grasping experiment is replicated and compared to the real grasping data collected by a data glove device. 

Robomaths: Robotics to Learn Matematics in a Architecture Degree

The abstract part of mathematics is a difficult matter included in many subjects in university degrees. Specifically, in architecture degrees students lack interest in this topic if they don’t experience its immediate application. In addition, technological skills are required at every educational level and the students of these degrees are usually more interested in art than in technology. With the aim of encouraging architecture students' interest in mathematics and technology, a methodology is presented here that includes the use of robotics in maths lectures. The key idea is to make the abstract part of mathematics understandable by means of robots

A Novel Real-Time MATLAB/Simulink/LEGO EV3 Platform for Academic Use in Robotics and Computer Science

Over the last years, mobile robot platforms are having a key role in education worldwide. Among others, LEGO Robots and MATLAB/Simulink are being used mainly in universities to improve the teaching experience. Most LEGO systems used in the literature are based on NXT, as the EV3 version is relatively recent. In contrast to the previous versions, the EV3 allows the development of real-time applications for teaching a wide variety of subjects as well as conducting research experiments. The goal of the research presented in this paper was to develop and validate a novel real-time educational platform based on the MATLAB/Simulink package and the LEGO EV3 brick for academic use in the fields of robotics and computer science. The proposed framework is tested here in different university teaching situations and several case studies are presented in the form of interactive projects developed by students. Without loss of generality, the platform is used for testing different robot path planning algorithms. Classical algorithms like rapidly-exploring random trees or artificial potential fields, developed by robotics researchers, are tested by bachelor students, since the code is freely available on the Internet. Furthermore, recent path planning algorithms developed by the authors are also tested in the platform with the aim of detecting the limits of its applicability. The restrictions and advantages of the proposed platform are discussed in order to enlighten future educational applications

A tensor optimization algorithm for Bézier Shape Deformation

In this paper we propose a tensor based description of the Bézier Shape Deformation (BSD) algorithm, denoted as T-BSD. The BSD algorithm is a well-known technique, based on the deformation of a Bézier curve through a field of vectors. A critical point in the use of real-time applications is the cost in computational time. Recently, the use of tensors in numerical methods has been increasing because they drastically reduce computational costs. Our formulation based in tensors T-BSD provides an efficient reformulation of the BSD algorithm. More precisely, the evolution of the execution time with respect to the number of curves of the BSD algorithm is an exponentially increasing curve. As the numerical experiments show, the T-BSD algorithm transforms this evolution into a linear one. This fact allows to compute the deformation of a Bézier with a much lower computational cost

Numerical strategies for the Galerkin–proper generalized decomposition method

The Proper Generalized Decomposition or, for short, PGD is a tensor decomposition based technique to solve PDE problems. It reduces calculation and storage cost drastically and presents some similarities with the Proper Orthogonal Decomposition, for short POD. In this work, we propose an efficient implementation to improve the convergence of the PGD, toward the numerical solution of a discretized PDE problem, when the associated matrix is Laplacian-like.The first author was partially supported by PRCEU-UCH 30/10 by Universidad CEU Cardenal Herrer

A perspective of medical students on 3D printing for anatomy education

Ponencia presentada a INTED2020 14th International Technology, Education and Development Conference, Valencia (Spain), 2-4 Marzo de 2020.For centuries, the dissection of full-body corpses has been the gold standard in Anatomy education, promoting deep anatomical comprehension. In the last years, with the growth of medical training on several fronts, the hours devoted to Anatomy practice have been reduced. This reduction has led to dissection giving way to prosection: students do not dissect human bodies but study the morphology of corpses already dissected by another person

On the Existence of a Progressive Variational Vademecum based on the Proper Generalized Decomposition for a Class of Elliptic Parameterized Problems

In this study, we present the mathematical analysis needed to explain the convergence of a progressive variational vademecum based on the proper generalized decomposition (PGD). The PGD is a novel technique that was developed recently for solving problems with high dimensions, and it also provides new approaches for obtaining the solutions of elliptic and parabolic problems via the abstract separation of variables method. This new scenario requires a mathematical framework in order to justify its application to the solution of numerical problems and the PGD can help in the change to this paradigm. The main aim of this study is to provide a mathematical environment for defining the notion of progressive variational vademecum. We prove the convergence of this iterative procedure and we also provide the first order optimality conditions in order to construct the numerical approximations of the parameterized solutions. In particular, we illustrate this methodology based on a robot path planning problem. This is one of the common tasks when designing the trajectory or path of a mobile robot. The construction of a progressive variational vademecum provides a novel methodology for computing all the possible paths from any start and goal positions derived from a harmonic potential field in a predefined map

Grip force and force sharing in two different manipulation tasks with bottles

Grip force and force sharing during two activities of daily living were analysed experimentally in 10 right-handed subjects. Four different bottles, filled to two different levels, were manipulated for two tasks: transporting and pouring. Each test subject’s hand was instrumented with eight thin wearable force sensors. The grip force and force sharing were significantly different for each bottle model. Increasing the filling level resulted in an increase in grip force, but the ratio of grip force to load force was higher for lighter loads. The task influenced the force sharing but not the mean grip force. The contributions of the thumb and ring finger were higher in the pouring task, whereas the contributions of the palm and the index finger were higher in the transport task. Mean force sharing among fingers was 30% for index, 29% for middle, 22% for ring and 19% for little finger. Practitioner Summary: We analysed grip force and force sharing in two manipulation tasks with bottles: transporting and pouring. The objective was to understand the effects of the bottle features, filling level and task on the contribution of different areas of the hand to the grip force. Force sharing was different for each task and the bottles features affected to both grip force and force sharing.We wish to thank the Fundació Caixa-Castelló and the Universi- tat Jaume I for financial support through project P1-1B2009-40 and the Spanish Ministry of Economy and Competitiveness and FEDER through project DPI2014-60635-R. With the financial support of the Department of Mechanical Engineering and Con- struction at the Universitat Jaume I, Mark Andrews helped the authors with the English language edition of the manuscript