Marco A. Pérez Cisneros

POLYNOMIAL TRAJECTORY ALGORITHM FOR A BIPED ROBOT

Building trajectories for biped robot walking is a complex task considering all degrees of freedom (DOFs) commonly bound within the mechanical structure. A typical problem for such robots is the instability produced by violent transitions between walking phases in particular when a swinging leg impacts the surface. Although extensive research on novel efficient walking algorithms has been conducted, falls commonly appear as the walking speed increases or as the terrain condition changes. This paper presents a polynomial trajectory generation algorithm (PTA) to implement the walking on biped robots following the cubic Hermitian polynomial interpolation between initial and final conditions. The proposed algorithm allows smooth transitions between walking phases, significantly reducing the possibility of falling. The algorithm has been successfully tested by generating walking trajectories under different terrain conditions on a biped robot of 10 DOFs. PTA has shown to be simple and suitable to generate real time walking trajectories, despite reduced computing resources of a commercial embedded microcontroller. Experimental evidence and comparisons to other state-of-the-art methods demonstrates a better performance of the proposed method in generating walking trajectories under different ground conditions.

Industry 4.0 & Internet of Things in Supply Chain

Industry 4.0 based in Internet of things must provide data and information to management process. Protocol of communication between things accelerates the different steps in the supply chain. One order with a n quantity of components located in different vendors, the ones that can be in any location in the word, connected with internet providing basic information of the component and tracking them on logistic process. In the process to arrive to manufacturing place all components generate a master signal to complete the order, all this process fallow by internet tracking system. Complete order is fallow for the system and send signals to tracking the process of the order in the back end windows.

Forecast of pollutants in the rio santiago, using data fusion technique using statistical and regression methods

Data fusion aims at combining the results of sample shots in order to get a forecast on the concentration of pollutants in water, based on the presence of another. This paper presents the relationship between pollutants in the Santiago River by merging of data using statistical analysis methods.

Artificial neural networks applied in the forecast of pollutants into the Rio Santiago, based on the sample of a pollutant, by data fusion

Application of Artificial Neural Networks shows the results obtained by merging data from previous shots show them, considering a contaminant based on the Santiago River to forecast relative to other contaminants in the water, without having to perform specific and independent testing of each pollutant. This article presents the results of recent analysis.