Julian Estevez

Quasi-stationary state transportation of a hose with quadrotors

A hose is a flexible almost unidimensional object. Transportation of a hose by means of a team of collaborating robots poses a new multi-robot control paradigm, because the hose introduces strong non-linear interaction effects in the dynamics of the overall system. In this paper, we consider that a team ( n ? 2 ) of unmanned aerial robots, specifically quadrotors, carry out the hose transportation task. A hose is a Deformable Linear Object (DLO). In this paper, a hose hanging from hovering quadrotors, after reaching a quasi-stationary state is modeled by a catenary curve. We consider the control problem of driving the entire system to a state in which each robot is subjected to the same vertical force (i.e. weight), thus each robot energy consumption will be the same, aiming to prevent that any robot runs out of energy much earlier than the others. This problem can be posed only when we deal with multicatenary systems ( n ? 3 ). We have taken care of defining visually measurable system parameters, allowing visual servoing in real life experimentation. In this paper we present the system model, its dynamic simulation, and the derivation of a control system reaching the desired equiload state. Propose the transportation of hose-like objects by teams of quadrotors.We provide the modeling of the dynamics of the hose by catenary curves.Definition of control to ensure equal energy consumption by all team members.Control is achieved by PID.

Particle Swarm Optimization Quadrotor Control for Cooperative Aerial Transportation of Deformable Linear Objects

ABSTRACT We present a cooperative aerial robot system for the transportation of hoses. The hose–robot attachment makes the whole system physically interconnected but not rigid, so that control design becomes a difficult nonlinear optimization problem. The hose in quasistationary state can be modeled by sections of catenary curves. We use proportional integral derivative (PID) controllers for both quadrotor attitude and trajectory control, tuned by particle swarm optimization (PSO). In this work we test PSO minimizing an energy function to achieve the PID controller tuning for horizontal motion of quadrotor teams transporting hoses under different stress conditions.

Robust Control Tuning by PSO of Aerial Robots Hose Transportation

This work presents a method to build a robust controller for a hose transportation system performed by aerial robots. We provide the system dynamic model, equations and desired equilibrium criteria. Control is obtained through PID controllers tuned by particle swarm optimization (PSO). The control strategy is illustrated for three quadrotors carrying two sections of a hose, but the model can be easily expanded to a bigger number of quadrotors system, due to the approach modularity. Experiments demonstrate the PSO tuning method convergence, which is fast. More than one solution is possible, and control is very robust.

Online fuzzy modulated adaptive PD control for cooperative aerial transportation of deformable linear objects

The aim of this work is to design robust control algorithms of aerial robots, i.e. quadrotors, for team transportation of a deformable linear object (DLO). The DLO-robot attachment makes the whole system physically coupled by an elastic element, which introduces strong non-linearities in the system dynamics. Sections in quasi-stationary state of the DLO hanging freely from two extreme points can be modeled by catenary curves, so we are able to build a detailed and accurate simulation of the system as the workbench for the evaluation of alternative quadrotor control approaches. DLO transportation is an instance of a follow the leader team strategy, in which the local quadrotor control must cope with the dynamic perturbations due to the DLO linking the quadrotors. The quadrotor team control has two phases, one achieving a spatial configuration with equal energy consumption, the other is to manage the horizontal motion which is the transportation process per se. In this paper we deal with the second control problem, assuming the first solved. We use proportional derivative (PD) controllers for both quadrotor attitude and trajectory control. Offline PD parameter setting carried by metaheuristic optimization can not cope with the perturbations induced by the DLO and environmental conditions, i.e. wind shear. Therefore we propose an adaptive controller for the quadrotors carrying out the DLO transportation task which uses fuzzy modeling of the error in order to modulate the activation of the PD parameters adaptation rules, which are error gradient descent rules. Computational experiments on our system simulation workbench show that our adaptive approach scales well when increasing the number of quadrotors, providing smooth follow-the-leader team strategy navigation behaviors even under strong wind perturbation conditions. We compare our approach with other state-of-the- art online and offline approaches.

Electrical Behavior Modeling of Solar Panels Using Extreme Learning Machines

Predicting the response of solar panels has a big potential impact on the economical viability of the insertion of alternative energy sources in our societies, diminishing the dependence on polluting fossil fuels. In this paper we approach the modeling of the electrical behavior of a commercial photovoltaic module Atersa A-55 using Extreme Learning Machines (ELMs). The training and validation data were extracted from the response of a real photovoltaic module installed at the Faculty of Engineering of Vitoria-Gasteiz (Basque Country University, Spain). The resulting predictive model has one input (\(V_{PV}\)) and one output (\(I_{PV}\)) variables. We achieve a Root Mean Squared Error (RMSE) of 0.026 in the electrical current measured in Amperes.

Making physical proofs of concept of reinforcement learning control in single robot hose transport task complete

Abstract This paper deals with the realization of physical proof of concept experiments in the paradigm of Linked Multi-Component Robotic Systems (LMCRS). The main objective is to demonstrate that the controllers learned through Reinforcement Learning (RL) algorithms with different state space formalizations and different spatial discretizations in a simulator are reliable in a real world configuration of the task of transporting a hose by a single robot. This one is a prototypical example of LMCRS task (extendable to much more complex tasks). We describe how the complete system has been designed and implemented. Two different previously learned RL controllers have been tested solving two different LMCRS control problems, using different state space modeling and discretization step in each case. The physical realizations validate previously published simulation based results, giving a strong argument in favor of the suitability of RL techniques to deal with LMCRS systems.

Improved Control of DLO Transportation by a Team of Quadrotors

Quasi-stationary sections of a deformable linear object (DLO) hanging freely from two extreme points can be modeled either by catenaries or parabolic curves, depending on the conditions of the UAVs. DLO transportation is an instance of a leader-follower platoon team strategy, in which the local quadrotor control must cope with the dynamic perturbations due to the DLO linking the quadrotors. The quadrotor team control has two phases, one achieving a spatial configuration with equal energy consumption, the other is to manage the horizontal motion which is the transportation process per se. We propose a Model Reference Adaptive Control (MRAC) for the quadrotors team, which uses fuzzy modeling of the error in order to modulate the activation of the adaptation rules applied to proportional-derivative (PD) controller parameters, which are derived as error gradient descent rules. In this paper, we contribute the parabolic representation of the DLO and improved follow the leader control, testing the MRAC stability and robustness under a series of experiments.

Towards Hospitalization After Readmission Risk Prediction Using ELMs

A criteria to evaluate the performance of Emergency Departments (ED) is the number of readmissions and hospitalizations short time after discharge of patients because the problem was not solved in the first admission. Such events contribute to overload the care system and to worsening the health of patients. In this paper we address the problem of predicting hospitalization events after readmission in ED, facing it as a classification problem and using Extreme Learning Machines (ELM). We have carried out experiments with a dataset with 45,089 admission events of 21,269 pediatric patients recorded in the Hospital Jose Joaquin Aguirre of the University of Chile during 3 years and 4 months, improving the state-of-the-art sensitivity results on the same dataset by 17%.

Neuronal Electrical Behavior Modeling of Solar Panels

In this paper authors model the electrical behavior of a commercial solar panel composed of solar cells connected in series through an Artificial Neural Network (ANN) with one hidden layer. The real solar panel that has been used as proof of concept is of the commercial model ATERSA A55, and it is placed at the Faculty of Engineering of Vitoria-Gasteiz (Basque Country University, Spain). The resulting model consists on one input (\(V_{PV}\)) and one output (\(I_{PV}\)), since the standard deviation of the temperature and irradiance magnitudes in the used dataset was residual.

The Quadrotor Workshop in Science Week. Spread of Technical and Scientific Applications in Society

Along the Science Week in San Sebastian, a workshop about quadrotors was presented where basic concepts of these machines and physical laws in which they are based were explained. This activity was framed in a workshop for high school pupils and families.