Diseño de un laboratorio de robótica autónoma

Versión electrónica de la ponencia presentada en VII Congreso Tecnologías Aplicadas a la Enseñanza de la Electrónica, TAEE 2006, celebrado en Madrid en 2006En este trabajo se presenta una nueva plataforma hardware/software destinada a la realización de prácticas de robótica autónoma que se está aplicando actualmente en titulaciones de Ingeniería Informática y de Telecomunicación, pero que es lo suficientemente abierta en su concepción y manejo para ser utilizada en otro tipo de estudios superiores. El diseño abierto de esta plataforma permite al alumno centrarse, bien en el desarrollo de algoritmos y pruebas de software, utilizando cualquier lenguaje y bajo cualquier SO o bien centrarse en el diseño e implementación de nuevos sistemas periféricos sensores o de control

On the problem of task planning in Multi-Robot Systems

International audienceMulti-robot task planning (MRTP) is one of the fundamental problems for multi-robot systems. An important question facing this research topic is, which robot should execute which task so as the expected overall system performance can be maximized? Many approaches have been proposed for such a purpose. This paper investigates the existing works in the field. The approaches have been surveyed and some representatives are compared with detailed results. A brief discussion and further research perspectives are also given at the end of the paper

Spatial context-aware person-following for a domestic robot

Domestic robots are in the focus of research in terms of service providers in households and even as robotic companion that share the living space with humans. A major capability of mobile domestic robots that is joint exploration of space. One challenge to deal with this task is how could we let the robots move in space in reasonable, socially acceptable ways so that it will support interaction and communication as a part of the joint exploration. As a step towards this challenge, we have developed a context-aware following behav- ior considering these social aspects and applied these together with a multi-modal person-tracking method to switch between three basic following approaches, namely direction-following, path-following and parallel-following. These are derived from the observation of human-human following schemes and are activated depending on the current spatial context (e.g. free space) and the relative position of the interacting human. A combination of the elementary behaviors is performed in real time with our mobile robot in different environments. First experimental results are provided to demonstrate the practicability of the proposed approach

Dual adaptive dynamic control of mobile robots using neural networks

This paper proposes two novel dual adaptive neural control schemes for the dynamic control of nonholonomic mobile robots. The two schemes are developed in discrete time, and the robot's nonlinear dynamic functions are assumed to be unknown. Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are used for function approximation. In each scheme, the unknown network parameters are estimated stochastically in real time, and no preliminary offline neural network training is used. In contrast to other adaptive techniques hitherto proposed in the literature on mobile robots, the dual control laws presented in this paper do not rely on the heuristic certainty equivalence property but account for the uncertainty in the estimates. This results in a major improvement in tracking performance, despite the plant uncertainty and unmodeled dynamics. Monte Carlo simulation and statistical hypothesis testing are used to illustrate the effectiveness of the two proposed stochastic controllers as applied to the trajectory-tracking problem of a differentially driven wheeled mobile robot.peer-reviewe

Dynamic bayesian networks for learning interactions between assistive robotic walker and human users

Detection of individuals intentions and actions from a stream of human behaviour is an open problem. Yet for robotic agents to be truly perceived as human-friendly entities they need to respond naturally to the physical interactions with the surrounding environment, most notably with the user. This paper proposes a generative probabilistic approach in the form of Dynamic Bayesian Networks (DBN) to seamlessly account for users attitudes. A model is presented which can learn to recognize a subset of possible actions by the user of a gait stability support power rollator walker, such as standing up, sitting down or assistive strolling, and adapt the behaviour of the device accordingly. The communication between the user and the device is implicit, without any explicit intention such as a keypad or voice.The end result is a decision making mechanism that best matches the users cognitive attitude towards a set of assistive tasks, effectively incorporating the evolving activity model of the user in the process. The proposed framework is evaluated in real-life condition. © 2010 Springer-Verlag Berlin Heidelberg

Minimal Representation for the Control of Gough-Stewart Platforms via Leg Observation Considering a Hidden Robot Model

International audienceThis paper presents new insights about the sensor-based control of Gough-Stewart (GS) platforms. Previous works have shown that it was possible to control the GS platform by observing its legs directions instead of using the encoders values or the measurement of the platform pose. It was demonstrated that observing only three legs directions was enough for the control but no physical explanations were given. Moreover, sometimes, the GS platform was not converging to the desired pose and the reasons of these divergences were not disclosed. This paper aims at answering to this two opened problems. It is shown that observing three leg directions involves controlling the displacement of a hidden robot whose models differs from those of the usual GS platform. This robot has assembly modes and singular configurations different from those of the GS platform. This involves that the legs to observe should be chosen carefully in order to avoid inaccuracy problems. In this sense, the accuracy analysis of the new robot is performed to show the importance of the leg selection. All these results are validated on a GS platform simulator created using ADAMS/Controls and interfaced with Matlab/Simulink

Probabilistic models versus discriminate classifiers for human activity recognition with an instrumented mobility-assistance aid

Detection of individuals' intentions and actions from a stream of human behaviour is an open and complex problem. There is however an intrinsic need to automatically recognise the activities performed by users of mobility assistive aids to better understand their behavioural patterns, with the ultimate objective of improving the utility of these devices. While discriminative algorithms such as Support Vector Machines (SVM) are well understood, generative probabilistic approaches to machine learning such as Dynamic Bayesian Networks (DBN) have only recently started gaining increasing interest within the robotics community. In this paper, a comprehensive evaluation of these techniques is carried out for human activity recognition in the context of their applicability to assistive robotics. Results show comparable recognition rates, offering valuable insights into the advantageous characteristics of DBN in relation to their dynamic and unsupervised nature for realistic human-robot interaction modelling