Identificación, estimación y control de sistemas no-lineales mediante RGO

Se trata la identificación de sistemas, esto es: la estimación de modelos de sistemas dinámicos a partir de los datos observados. La estimación trata de evaluar y diseñar los estimadores de estado operando antes en un entorno estocástico. Se busca la mejora de la resolución de los problemas de identificación y estimación de estados de sistemas dinámicos no-lineales y el control adaptativo de los mismos. Se presenta un nuevo método híbrido para la optimización de funciones no lineales y no diferenciales que varían con el tiempo sin la utilización de demandas numéricas. Este método está basado en los Algoritmos Genéticos con una menor técnica de búsqueda que se ha llamado Optimización Genética Restringida. A partir de este algoritmo se presenta un método de altas prestaciones para la identificación de sistemas no lineales variables con el tiempo con modelos lineales y no lineales. Se presentan dos aplicaciones diferentes de estos métodos. _________________________________________________The system identification deals with the problem of estimating modeis of dynamical systems from observed data. The estimation tries to evaluate and to design state estimators. The two of them are supposed to operate in a stochastic environment. In this thesis, It has been tried to improve the methods of identification and state estimation of non-linear dynamical systems and their adaptive control. A new optimization hybrid method of non-linear and non-differentiable, time varying functions without using numerical derivatives is presented. This is important because of noise. This method based on Genetic Algorithms introduces a new technique called Restricted Genetié Optimization (ROO). This optimization method unifies the thesis and due to the fact that it is a basic method, it can be applied to a lot of problems related with non-differentiable and time-varying functions. Based on this algorithm, a high performance method for the identification of non-linear, time-varying systems with linear and non-linear modeis, is presented. This method can be used on-line and in a closed loop. For this reason, it is well adapted to control. This method uses an on line identification algorithm that begins by calculating what ARX is the best adapted to the system. This way the order and the delay of the system are known. Then, an ARMAX that is used as a seed to start the RGO and to create a NARMAX model, is calculated. The RGO algorithm can describe a new non-linear estimator for filtering of systems with non-linear processes and observation modeis based on the RGO optimization. The simulation results are used to compare the performance of this method with EKF (Extended Kaiman Filter), IEKF (Iterated Extended Kaiman Filter), SNF (Second-order Non-linear Filter), SIF (Single-stage Iterated Filter) y MSF (Montecarlo Simulation Filter) with different levels of noise. When this method is applied to the state space identification a new method is obtained. This method begins by calculating an ARX and then uses RGO in order to improve the previous identification. This method is based on the fuil parametrization and balanced realizations. This way low sensitivity realizations are obtained and the structural issues of multivariable canonical parametrizations are circumvented. Two applications of this method are considered. The first application is the predictive control with RGO of the Twin Rotor MIMO System (TRMS), that is a laboratory set-up designed for control experiments. In certain aspects, its behaviour resembles that of a helicopter. From the control point of view, it exemplifies a high order non-linear system with significant cross-couplings. The second one is the robot localization based on different kind of sensor information. To fuse all the different information, an algorithm is necessary. In this case, it has been used an extension of the Kalman algorithm with RGO

Mobile robot path planning using Voronoi diagram and fast marching

For navigation in complicated environments, a robot must reach a compromise between efficient trajectories and ability to react to unexpected environmental events. This paper presents a new sensorbased path planner, which gives a fast local or global motion plan capable to incorporate new obstacles data. Within the first step, the safest areas in the environment are extracted by means of a Voronoi Diagram. Within the second step, the fast marching method is applied to the Voronoi extracted areas so as to get the trail. This strategy combines map-based and sensor-based designing operations to supply a reliable motion plan, whereas it operates at the frequency of the sensor. The most interesting characteristics are high speed and reliability, as the map dimensions are reduced to a virtually one-dimensional map and this map represents the safest areas within the environment. Additionally, the Voronoi Diagram is calculated in open areas with all reasonably shaped obstacles. This fact permits to use the planned trajectory methodology in complex environments wherever different Voronoi-based strategies will not work.Publicad

Robot formation motion planning using Fast Marching

This paper presents the application of the Voronoi Fast Marching (VFM) method to path planning of mobile formation robots. The VFM method uses the propagation of a wave (Fast Marching) operating on the world model to determine a motion plan over a viscosity map (similar to the refraction index in optics) extracted from the updated map model. The computational efficiency of the method allows the planner to operate at high rate sensor frequencies. This method allows us to maintain good response time and smooth and safe planned trajectories. The navigation function can be classified as a type of potential field, but it has no local minima, it is complete (it finds the solution path if it exists) and it has a complexity of order n(O(n)), where n is the number of cells in the environment map. The results presented in this paper show how the proposed method behaves with mobile robot formations and generates trajectories of good quality without problems of local minima when the formation encounters non-convex obstacles.This work has been supported by the CAM Project S2009/DPI-1559/ROBOCITY2030 II, developed by the research team RoboticsLab at the University Carlos III of Madrid.Publicad

Marine applications of the fast marching method

Path planning is general problem of mobile robots, which has special characteristics when applied to marine applications. In addition to avoid colliding with obstacles, in marine scenarios, environment conditions such as water currents or wind need to be taken into account in the path planning process. In this paper, several solutions based on the Fast Marching Method are proposed. The basic method focus on collision avoidance and optimal planning and, later on, using the same underlying method, the influence of marine currents in the optimal path planning is detailed. Finally, the application of these methods to consider marine robot formations is presented.The research leading to these results has received funding from HEROITEA-Sistema Inteligente Heterogéneo Multirobot para la Asistencia de Personas Mayores-RTI2018-095599-BC21 and from RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Motion planning using fast marching squared method

Robotic motion planning have been, and still is, a very intense research field. Many problems have been already solved and even real-time, optimal motion planning algorithms have been proposed and successfully tested in real-world scenarios. However, other problems are not satisfactory solved yet and also new motion planning subproblems are appearing. In this chapter we detail our proposed solution for two of these problems with the same underlying method: non-holonomic planning and outdoor motion planning. The first is characterized by the fact that many vehicles cannot move in any direction at any time (car-like robots). Therefore, kinematic constrains need to be taken into account when planning a new path. Outoor motion planning focuses on the problem that has to be faced when a robot is going to work in scenarios with non-flat ground, with different floor types (grass, sand, etc.). In this case the path computed should take into account the capabilities of the robot to properly model the environment. In order to solve these problems we are using the Fast Marching Square method, which has proved to be robust and efficient in the recent past when applied to other robot motion planning subproblems.Publicad

SLAM and exploration using differential evolution and fast marching

The exploration and construction of maps in unknown environments is a challenge for robotics. The proposed method is facing this problem by combining effective techniques for planning, SLAM, and a new exploration approach based on the Voronoi Fast Marching method. The final goal of the exploration task is to build a map of the environment that previously the robot did not know. The exploration is not only to determine where the robot should move, but also to plan the movement, and the process of simultaneous localization and mapping. This work proposes the Voronoi Fast Marching method that uses a Fast Marching technique on the Logarithm of the Extended Voronoi Transform of the environment"s image provided by sensors, to determine a motion plan. The Logarithm of the Extended Voronoi Transform imitates the repulsive electric potential from walls and obstacles, and the Fast Marching Method propagates a wave over that potential map. The trajectory is calculated by the gradient method

Application of the fast marching method for outdoor motion planning in robotics

In this paper, a new path planning method for robots used in outdoor environments is presented. The proposed method applies Fast Marching to a 3D surface represented by a triangular mesh to calculate a smooth trajectory from one point to another. The method uses a triangular mesh instead of a square one since this kind of grid adapts better to 3D surfaces. The novelty of this approach is that, before running the algorithm, the method calculates a weight matrix W based on the information extracted from the 3D surface characteristics. In the presented experiments these features are the height, the spherical variance, and the gradient of the surface. This matrix can be viewed as a difficulty map situated over the 3D surface and is used to limit the propagation speed of the Fast Marching wave in order to find the best path depending on the task requirements, e.g., the least energy consumption path, the fastest path, or the most plain terrain. The algorithm also gives the speed for the robot, which depends on the wave front propagation speed. The results presented in this paper show how, by varying this matrix W, the paths obtained are different. Moreover, as it is shown in the experimental part, this algorithm is also useful for calculating paths for climbing robots in much more complex environments. Finally, at the end of the paper, it is shown that this algorithm can also be used for robot avoidance when two robots approach each other, and they know each other's position.Comunidad de Madri

Differential evolution Markov chain filter for global localization

A key challenge for an autonomous mobile robot is to estimate its location according to the available information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evolution method that solves this problem in 2D and 3D environments. The robot’s pose is represented by a set of possible location estimates weighted by a fitness function. The Markov Chain Monte Carlo algorithms have been successfully applied to multiple fields such as econometrics or computing science. It has been demonstrated that they can be combined with the Differential Evolution method to solve efficiently many optimization problems. In this work, we have combined both approaches to develop a global localization filter. The algorithm performance has been tested in simulated and real maps. The population requirements have been reduced when compared to the previous version.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robotica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU.Publicad

Fast marching subjected to a vector field-path planning method for mars rovers

Path planning is an essential tool for the robots that explore the surface of Mars or other celestial bodies such as dwarf planets, asteroids, or moons. These vehicles require expert and intelligent systems to adopt the best decisions in order to survive in a hostile environment. The planning module has to take into account multiple factors such as the obstacles, the slope of the terrain, the surface roughness, the type of ground (presence of sand), or the information uncertainty. This paper presents a path planning system for rovers based on an improved version of the Fast Marching (FM) method. Scalar and vectorial properties are considered when computing the potential field which is the basis of the proposed technique. Each position in the map of the environment has a cost value (potential) that is used to include different types of variables. The scalar properties can be introduced in a component of the cost function that can represent characteristics such as difficulty, slowness, viscosity, refraction index, or incertitude. The cost value can be computed in different ways depending on the information extracted from the surface and the sensor data of the rover. In this paper, the surface roughness, the slope of the terrain, and the changes in height have been chosen according to the available information. When the robot is navigating sandy terrain with a certain slope, there is a landslide that has to be considered and corrected in the path calculation. This landslide is similar to a lateral current or vector field in the direction of the negative gradient of the surface. Our technique is able to compensate this vector field by introducing the influence of this variable in the cost function. Because of this modification, the new method has been called Fast Marching (subjected to a) vector field (FMVF). Different experiments have been carried out in simulated and real maps to test the method performance.Publicad

An anisotropic fast marching method applied to path planning for Mars rovers

This paper presents the application of the Anisotropic Fast Marching Method to the path planning problem of mobile robots moving in outdoors environments, such as Mars. Considering that at any point on a 3D surface there are two main slopes: the maximum, which is the slope of the gradient, and the minimum, the height map of a terrain can be considered as a tensor filed. Using the Anisotropic Fast Marching Method, the resulting trajectory of the path planning takes the tensor field into account so that the slopes in the trajectory are minimized. Numerical simulations are presented to show the advantage of this method over its isotropic version. Besides, the influence of the anisotropic index and the traversability of the resultant paths are analyzed.This work was supported in part by the projects: "RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), in part by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU" and in part by "Investigación para la mejora competitiva del ciclo de perforación y voladura en minería y obras subterráneas, mediante la concepción de nuevas técnicas de ingeniería, explosivos, prototipos y herramientas avanzadas (TUÑEL)"