Trajectory modelling of a planar magnetic cell micropusher.

International audienceThe improving of the efficiency and the automation of biological cell technologies is a current high stake. One way is to build biological micro-factories which are able to perform a complete biotechnological processes automatically. This technology requires the development of new automatic cell transport system to feed work stations in microfactories. An original magnetic cell micropusher is described in this paper. The ferromagnetic pusher which is submerged in the biological medium follows the movement of a permanent magnet located in the air. This paper focuses on the modelling of the dynamic behaviour of the micropusher in function of the magnet trajectory. The generic model proposed is able to determine pusher trajectory according to the micropusher magnetic properties and the permanent magnet shape and properties. This simulation tool will allow to optimize and to study cell trajectory control in further works

A flexible framework for data exchange and presentation between wireless sensor networks and personal devices

This paper presents the framework of an interface between wireless sensor networks and personal devices like PDAs, cellular phones and laptops. It possesses a flexible architecture that can be adapted easily to display any kind of data received from the network sensors using content description. The most relevant properties of this system are: portability (it can be used on any device with a standard browser), expandability (it can act as a gateway for higher systems), adaptability (the information can be presented in many different ways) and scalability (several sensor networks can be controlled at the same time). This system is currently being used display information retrieved from a Bluetooth based wireless sensor network platform which operates onboard of an unmanned aerial vehicle (UAV)

Quasi optimal sagittal gait of a biped robot with a new structure of knee joint

The design of humanoid robots has been a tricky challenge for several years. Due to the kinematic complexity of human joints, their movements are notoriously difficult to be reproduced by a mechanism. The human knees allow movements including rolling and sliding, and therefore the design of new bioinspired knees is of utmost importance for the reproduction of anthropomorphic walking in the sagittal plane. In this article, the kinematic characteristics of knees were analyzed and a mechanical solution for reproducing them is proposed. The geometrical, kinematic and dynamic models are built together with an impact model for a biped robot with the new knee kinematic. The walking gait is studied as a problem of parametric optimization under constraints. The trajectories of walking are approximated by mathematical functions for a gait composed of single support phases with impacts. Energy criteria allow comparing the robot provided with the new rolling knee mechanism and a robot equipped with revolute knee joints. The results of the optimizations show that the rolling knee brings a decrease of the sthenic criterion. The comparisons of torques are also observed to show the difference of energy distribution between the actuators. For the same actuator selection, these results prove that the robot with rolling knees can walk longer than the robot with revolute joint knees.ANR R2A

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Modern service robots will soon become an essential part of modern society. As they have to move and act in human environments, it is essential for them to be provided with a fast and reliable tracking system that localizes people in the neighbourhood. It is therefore important to select the most appropriate filter to estimate the position of these persons. This paper presents three efficient implementations of multisensor-human tracking based on different Bayesian estimators: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Sampling Importance Resampling (SIR) particle filter. The system implemented on a mobile robot is explained, introducing the methods used to detect and estimate the position of multiple people. Then, the solutions based on the three filters are discussed in detail. Several real experiments are conducted to evaluate their performance, which is compared in terms of accuracy, robustness and execution time of the estimation. The results show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue

A new geometrical approach to solve inverse kinematics of hyper redundant robots with variable link length

In this paper a new approach that generates a general algorithm for n-link hyper-redundant robot is presented. This method uses repetitively the basic inverse kinematics solution of a 2- link robot on some virtual links,where the virtual links are defined following some geometric proposition. Thus, it eliminates the mathematical complexity in computing inverse kinematics solution of n-link hyper redundant robot. Further, this approach can handle planar manipulator with variable links eliminating singularity. Numerical simulations for planar hyper redundant models are presented in order to illustrate the competency of the model

A bank of unscented Kalman filters for multimodal human perception with mobile service robots

A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics