Adaptive Estimation of the Pennes' Bio-Heat Equation - I: Observer Design

In this paper, we propose a multiple-model adaptive estimation setup for a class of uncertain parabolic reaction-diffusion PDEs encompassing the Pennes' bio-heat equation, which is a motivating case study from the perspective of biomedical applications such as hyperthermia. The efficacy of the approach in estimating the system solution and recovering the value of the reaction coefficient is validated through numerical simulations in MATLAB. The validation step has highlited some limitations of classical numerical simulation tools that we propose to handle through an implementation of the estimator relying on Deep Learning libraries. This alternative approach is reported in a companion paper (Part II of this work)

Estructuras geométricas jerárquicas para la modelización de escenas 3D

This work surveys on the principal hierarchical geometric structures used to represent 3D scenes. We also present the basic algorithms to work with them, an overview on some recent works and a comparative discussion. This work has been the outcomes of the graduate coursePostprint (published version

Tracking contact transitions during force-controlled compliant motion using an interacting multiple model estimator

This work concerns both monitoring of contact transitions and estimation of the unknown first-order geometric parameters during force-controlled motions. The robotic system is required to move an object among a sequence of contact configurations with the environment, under partial knowledge of geometric parameters (positions and orientations) of the manipulated objects and of the environment itself. An example of a compliant motion task with multiple contacts is considered, that of moving a cube into a corner. It is shown that by describing the contact configurations with different models, and by using the multiple model approach it is possible: i) to detect effectively at each moment the current contact configuration and ii) to estimate accurately the unknown parameters. The reciprocity constraints between ideal reaction forces and velocities are used as measurement equations. An Interacting Multiple Model (IMM) estimator is implemented and its performance is evaluated based on experimental data

Analytic Formulation of the Kinestatics of Robot Manipulators with Arbitrary Topology

In this paper an analytic formulation of the statics and the instantaneous kinematics of robot manipulators based on the Grassmann-Cayley algebra is presented. The notions of twist, wrench, twist space and wrench space are mathematically represented by the concept of extensors of this algebra and the reciprocity relation between twist and wrench spaces of partially constrained rigid bodies is reflected by its inherent duality. Kinestatic analysis of manipulators implies the computation of sums and intersections of the twist and wrench spaces of the composing chains which are carried out by means of the join and meet operators of this algebra when the linear subspaces involved in the kinestatic analysis of manipulators are represented by extensors. The importance of the Grassmann-Cayley algebra in kinestatics is that it has an explicit formula for the meet operator that gives analytical expressions of the twist and wrench space of robot manipulators with arbitrary topology

Analysis of Rigid Body Interactions for Compliant Motion Tasks Using the Grassmann-Cayley Algebra

This paper studies the statics and the instantaneous kinematics of a rigid body constrained to keep an arbitrary number of surface-surface contacts with a rigid static environment during its motion. These properties are analyzed under the frictionless assumption by modelling each contact with a kinematic chain that instantaneously gives the same motion freedom as the contact itself and by studying the resulting parallel chain using the Grassmann-Cayley Algebra. With this algebra twists and wrenches can be expressed by means of extensors and operated using the join and meet operators. Moreover, the duality inherent in this algebra is used to reect the reciprocity condition between possible twists and admissible wrenches between partially constrained rigid bodies. 1 Introduction Compliant motion tasks are manipulation tasks that involve contacts between the manipulated object, say M , and the static environment, say S, in which the trajectory of the manipulator is modied depending ..

Contact-state classification of human- demonstrated robot compliant motion tasks using the boosting algorithm

Robot programming by demonstration is a robot programming paradigm in which a human operator directly demonstrates the task to be performed. In this paper, we focus on programming by demonstration of compliant motion tasks, which are tasks that involve contacts between an object manipulated by the robot and the environment in which it operates. Critical issues in this paradigm are to distinguish essential actions from those that are not relevant for the correct execution of the task and to transform this information into a robot-independent representa- tion. Essential actions in compliant motion tasks are the contacts that take place, and therefore, it is important to understand the sequence of contact states that occur during a demonstration, called contact classification or contact segmentation. We propose a contact classification algorithm based on a supervised learning algorithm, in particular on a stochastic gradient boosting algo- rithm. The approach described in this paper is accurate and does not depend on the geometric model of the objects involved in the demonstration. It neither relies on the kinestatic model of the contact interactions nor on the contact state graph, whose computation is usually of prohibitive complexity even for very simple geometric object models

Robot vision for autonomous object learning and tracking

Iberoamerican Congress on Pattern Recognition (CIARP), 2003, Havana (Cuba)In this paper we present a summary of some of the research that we are developing in the Institute of Robotics of the CSIC-UPC, in the field of Learning and Robot Vision for autonomous mobile robots. We describe the problems that we have found and some solutions that have been applied in two issues: tracking objects and learning and recognition of 3D objects in robotic environments. We will explain some of the results accomplished.Peer Reviewe

A Gaussian process iterative learning control for aircraft trajectory tracking

This paper proposes a recursive Gaussian process regression with a joint optimization-based iterative learning control algorithm to estimate and predict disturbances and model uncertainties affecting a flight. The algorithm proactively compensates for the predicted disturbances, improving precision in aircraft trajectory tracking. Higher precision in trajectory tracking implies an improvement of the aircraft trajectory predictability and therefore of the air traffic management system efficiency. Airlines can also benefit from this higher predictability by reducing the number of alterations when following their designed trajectories, which entails a reduction of costs and emissions. The iterative learning control algorithm is divided into two steps: first, a recursive Gaussian process regression estimates and predicts perturbations and model errors with no need for prior knowledge about their dynamics and with low computational cost, and second, this information is used to update the control inputs so that the subsequent aircraft intending to fly the same planned trajectory will follow it with greater precision than the previous ones. This method is tested on a simulated commercial aircraft performing a continuous climb operation and compared to an iterative learning algorithm using a Kalman filter estimator in a similar scenario. The results show that the proposed approach provides 62% and 42% precision improvement in tracking the desired trajectory, as compared to the Kalman filter approach, in two experiments where no prior knowledge of the unmodeled dynamics was available, also achieving it in less iterations