Organic distributed feedback laser to monitor solvent extraction upon thermal annealing in solution-processed polymer films

Solution-processed polymer films are used in multiple technological applications. The presence of residual solvent in the film, as a consequence of the preparation method, affects the material properties, so films are typically subjected to post-deposition thermal annealing treatments aiming at its elimination. Monitoring the amount of solvent eliminated as a function of the annealing parameters is important to design a proper treatment to ensure complete solvent elimination, crucial to obtain reproducible and stable material properties and therefore, device performance. Here we demonstrate, for the first time to our knowledge, the use of an organic distributed feedback (DFB) laser to monitor with high precision the amount of solvent extracted from a spin-coated polymer film as a function of the thermal annealing time. The polymer film of interest, polystyrene in the present work, is doped with a small amount of a laser dye as to constitute the active layer of the laser device and deposited over a reusable DFB resonator. It is shown that solvent elimination translates into shifts in the DFB laser wavelength, as a consequence of changes in film thickness and refractive index. The proposed method is expected to be applicable to other types of annealing treatments, polymer-solvent combinations or film deposition methods, thus constituting a valuable tool to accurately control the quality and reproducibility of solution-processed polymer thin films.

Control framework for dexterous manipulation using dynamic visual servoing and tactile sensors’ feedback

Tactile sensors play an important role in robotics manipulation to perform dexterous and complex tasks. This paper presents a novel control framework to perform dexterous manipulation with multi-fingered robotic hands using feedback data from tactile and visual sensors. This control framework permits the definition of new visual controllers which allow the path tracking of the object motion taking into account both the dynamics model of the robot hand and the grasping force of the fingertips under a hybrid control scheme. In addition, the proposed general method employs optimal control to obtain the desired behaviour in the joint space of the fingers based on an indicated cost function which determines how the control effort is distributed over the joints of the robotic hand. Finally, authors show experimental verifications on a real robotic manipulation system for some of the controllers derived from the control framework.

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.