Carlos López-Franco

Omnidirectional Vision: Unified Model Using Conformal Geometry

It has been proven that a catadioptric projection can be modeled by an equivalent spherical projection. In this paper we present an extension and improvement of those ideas using the conformal geometric algebra, a modern framework for the projective space of hyper-spheres. Using this mathematical system, the analysis of diverse catadioptric mirrors becomes transparent and computationally simpler. As a result, the algebraic burden is reduced, allowing the user to work in a much more effective framework for the development of algorithms for omnidirectional vision. This paper includes complementary experimental analysis related to omnidirectional vision guided robot navigation.

Stability of nonlinear teleoperators using PD controllers without velocity measurements

This paper presents two Proportional-Derivative (PD) like controllers for nonlinear bilateral teleoperation systems. Compared to previous controllers of this kind, these schemes do not make use of velocity measurements. Under the assumptions that the human operator and the environment define passive maps from velocity to force, both controllers can ensure boundedness of velocities and position error. Moreover, in the case that the human and environment forces are zero, the controllers ensure velocity and position synchronization. Furthermore, the paper also presents a generalization to the case of teleoperation of networks of multiple robots. Simulations and real experiments, comparing the performance on free motion and interacting with a stiff wall, support the performance of the reported schemes. The experiments have been performed using two 3-degree-of-freedom nonlinear manipulators.

Smooth global and local path planning for mobile robot using particle swarm optimization, radial basis functions, splines and Bézier curves

An approach to plan smooth paths for mobile robots using a Radial Basis Function (RBF) neural network trained with Particle Swarm Optimization (PSO) was presented in [1]. Taking the previous approach as an starting point, in this paper it is shown that it is possible to construct a smooth simple global path and then modify this path locally using PSO-RBF, Ferguson splines or Bézier curves trained with PSO, in order to describe more complex paths in partially known environments. Experimental results show that our approach is fast and effective to deal with complex environments.

Bacterial foraging optimization algorithm to improve a discrete-time neural second order sliding mode controller

This paper deals with design parameter selection of a discrete-time neural second order sliding mode controller for unknown nonlinear systems, based on bacterial foraging optimization. First, a neural identifier is proposed in order to obtain a mathematical model for the unknown discrete-time nonlinear systems, then a novel second order sliding mode controller is proposed. Finally, both, the neural identifier and the controller are optimized using bacterial foraging algorithm. In order to illustrate the applicability of the proposed scheme, simulation results are included for a Van der Pol oscillator.

Neural identifier for unknown discrete-time nonlinear delayed systems

This work proposes a discrete-time nonlinear neural identifier based on a recurrent high-order neural network trained with an extended Kalman filter-based algorithm for discrete-time deterministic multiple-input multiple-output systems with unknown dynamics and time-delay. To prove the semi-globally uniformly ultimately boundedness of the proposed neural identifier, the stability analysis based on the Lyapunov approach is included. Applicability of the proposed identifier is shown via simulation and experimental results, all of them performed under the presence of unknown external and internal disturbances as well as unknown time-delays.

Omnidirectional Vision and Invariant Theory for Robot Navigation Using Conformal Geometric Algebra

The automatic landmark identification is very important in autonomous robot navigation tasks. In this work we use a monocular omnidirectional vision system to extract images features, and with help of the conformal geometric algebra (CGA), we show how these features can be used to calculate projective and permutation p2-invariants. These p2-invariants represent scene sub-landmarks and a set of them characterize a landmark

Neural-PSO second order sliding mode controller for unknown discrete-time nonlinear systems

This paper deals with adaptive tracking for unknown discrete-time MIMO nonlinear systems in presence of disturbances. A Particle Swarm Optimization (PSO) is used to improve a discrete-time neural second order sliding mode controller for unknown discrete-time nonlinear systems. In order to show the applicability of the proposed scheme, simulation results are included for a Van der Pol oscillator.

Real-time neural identification and inverse optimal control for a tracked robot

This work presents the implementation in real-time of a neural identifier based on a recurrent high-order neural network which is trained with an extended Kalman filter–based training algorithm and an inverse optimal control applied to a tracked robot. The recurrent high-order neural network identifier is developed without the knowledge of the plant model or its parameters; on the other hand, the inverse optimal control is designed for tracking velocity references. This article includes simulation and real-time results, both using MATLAB®, and also the experimental tests use a modified HD2® Treaded ATR Tank Robot Platform with wireless communication.

Image Classification Using PSO-SVM and an RGB-D Sensor

Image classification is a process that depends on the descriptor used to represent an object. To create such descriptors we use object models with rich information of the distribution of points. The object model stage is improved with an optimization process by spreading the point that conforms the mesh. In this paper, particle swarm optimization (PSO) is used to improve the model generation, while for the classification problem a support vector machine (SVM) is used. In order to measure the performance of the proposed method a group of objects from a public RGB-D object data set has been used. Experimental results show that our approach improves the distribution on the feature space of the model, which allows to reduce the number of support vectors obtained in the training process.

Vision-based robot control with omnidirectional cameras and conformal geometric algebra

Traditional cameras have a narrow field of view, to enlarge the field of view omnidirectional cameras can be used. In this work, we propose, a simple an elegant solution to the image formation model for omnidirectional cameras with parabolic mirrors. We propose the use of conformal geometric algebra (CGA), since the involved transformation operations in the model can be represented as an special group of multivectors. This representation is advantageous since the inversions are linearized, furthermore the transformation can be applied to all the geometric objects of the CGA. In consequence, the paracatadioptric image formation can be simplified, since the procedure is the same for points, point-pairs, lines, or circles. As an application example the control of a nonholonomic mobile robot using paracatadioptric line images and the proposed framework is described.