Constrained motion planning and execution for soft robots

There are many reasons why a compliant robot is expected to perform better than a rigid one in interaction tasks, which include limitation of interaction forces, resilience to modeling errors, robustness, naturalness of motion, and energy efficiency. Most of these reasons are apparent if one thinks of how the human body interacts with its environment. However, most of the work in robotic planning and control of interaction has been traditionally developed for rigid robot models. Indeed, planning and control for compliant robots can be substantially harder. In this thesis, I propose the point of view that the difficulties encountered in planning and control for soft robots are at least in part due to the fact that the same approaches previously used for rigid robots are used as a starting point and adapted. On the opposite, if new methods are considered that start from consideration of compliance from the very beginning, the planning and control problems can be of comparable difficulty, or even substantially simpler, than their rigid counterpart. I will argue this thesis with two main examples. The first part of this thesis presents a new approach to integrate motion planning and control for robots in interaction. One of the peculiarities of interaction tasks is that the robot limbs and the environment form "closed kinematic chains". If rigid models are considered, the dynamics of robots in interaction become constrained, and Differential Algebraic Equations replace Ordinary Differential Equations, i.e. typically a much harder problem to deal with. However, in the thesis I show that this is not necessarily so. Indeed, consideration of compliance allows to have a more tractable mathematical model of interacting systems, and to introduce more sophisticated control approaches. Specifically, we present a novel geometric control scheme under which for constrained robot systems we achieve decoupled interaction control (i.e. make position errors irrelevant to force control, and viceversa). Based on this result, it is possible to decouple the planning problem in two separate aspects. On one side, we make dealing with motion planning of the constrained system easier by relaxing the geometric constraint, i.e. replacing the lower--dimensional constraint manifold with a narrow but full-dimensional boundary layer. This allows us to plan motion using state-of-the-art methods, such as RRT*, on points within the boundary layer, which we can efficiently sample. On the other side we control interaction forces, i.e. forces generated by displacements in the perpendicular direction to the tangent space of the constraint manifold. Thanks to the (locally) noninteracting control characteristic of our scheme, the two controllers can be applied separately and in sequence, so that the interaction force controller can correct for any discrepancies resulting from the boundary layer approximation used in the constrained position controller. The geometric noninteracting controller can be applied both in simulation for planning, and in real time for execution control. Moreover, while it does rely on considering a model of compliance in the system, it does not make any assumption on the amount of compliance in the system - or in other words, it applies equally well to stiff but elastic robots. The final outcome of the two-stage planner is an effective (possibly optimal from RRT*) trajectory that satisfies constraint with arbitrarily good approximation, asymptotically rejecting perturbations coming from sampled displacements. The second part of this thesis is dedicated to study grasp planning for hands that are simple -- in the sense of low number of actuated degrees of freedom -- but soft, i.e. continuously deformable in an infinity of possible shapes through interaction with objects. Once again, the use of such "soft hands" brings about a change of paradigm in grasp planning with respect to classical rigid multi-dof grasp planning, which only apparently makes the problem harder. However, in this thesis I show that thanks to the correct combination of compliance and underactuation of soft hands, together with the set of all possible physical interactions between the hand, the object and the environment, the grasping problem can be redefined. The new definition includes the possible combination of hand-object functional interactions which I address as "Enabling Constraints". The use of Enabling Constraints constitutes a rather new challenge for existing grasping algorithms: adaptation to totally or partially unknown scenes remains a difficult task, toward which only some approaches have been investigated so far. In this thesis I present a first approach to the study of this novel kind of manipulation. It is based on an accurate simulation tool and starts from the considerations that hand compliance can be used to adapt to the shape of the surrounding objects and that rather than considering the environment as and obstacle to avoid, it can be used in turn to functionally shape the hand. I show that thanks to this functionality the problem of generating grasping postures for soft hands can be reduced to grasp basic geometries (e.g. cylinders or boxes) in which the geometry of the object can be decomposed

Composing Deep Learning and Bayesian Nonparametric Methods

Recent progress in Bayesian methods largely focus on non-conjugate models featured with extensive use of black-box functions: continuous functions implemented with neural networks. Using deep neural networks, Bayesian models can reasonably fit big data while at the same time capturing model uncertainty. This thesis targets at a more challenging problem: how do we model general random objects, including discrete ones, using random functions? Our conclusion is: many (discrete) random objects are in nature a composition of Poisson processes and random functions}. Thus, all discreteness is handled through the Poisson process while random functions captures the rest complexities of the object. Thus the title: composing deep learning and Bayesian nonparametric methods. This conclusion is not a conjecture. In spacial cases such as latent feature models , we can prove this claim by working on infinite dimensional spaces, and that is how Bayesian nonparametric kicks in. Moreover, we will assume some regularity assumptions on random objects such as exchangeability. Then the representations will show up magically using representation theorems. We will see this two times throughout this thesis. One may ask: when a random object is too simple, such as a non-negative random vector in the case of latent feature models, how can we exploit exchangeability? The answer is to aggregate infinite random objects and map them altogether onto an infinite dimensional space. And then assume exchangeability on the infinite dimensional space. We demonstrate two examples of latent feature models by (1) concatenating them as an infinite sequence (Section 2,3) and (2) stacking them as a 2d array (Section 4). Besides, we will see that Bayesian nonparametric methods are useful to model discrete patterns in time series data. We will showcase two examples: (1) using variance Gamma processes to model change points (Section 5), and (2) using Chinese restaurant processes to model speech with switching speakers (Section 6). We also aware that the inference problem can be non-trivial in popular Bayesian nonparametric models. In Section 7, we find a novel solution of online inference for the popular HDP-HMM model

Sampling Based Motion Planning with Reachable Volumes

Motion planning for constrained systems is a version of the motion planning problem in which the motion of a robot is limited by constraints. For example, one can require that a humanoid robot such as a PR2 remain upright by constraining its torso to be above its base or require that an object such as a bucket of water remain upright by constraining the vertices of the object to be parallel to the robot’s base. Grasping can be modeled by requiring that the end effectors of the robot be located at specified handle positions. Constraints might require that the robot remain in contact with a surface, or that certain joints of the robot remain in contact with each other (e.g., closed chains). Such problems are particularly difficult because the constraints form a manifold in C-space, and planning must be restricted to this manifold. High degree of freedom motion planning and motion planning for constrained systems has applications in parallel robotics, grasping and manipulation, computational biology and molecular simulations, and animation. In this work, we introduce a new concept, reachable volumes, that are a geometric representation of the regions the joints and end effectors of a robot can reach, and use it to define a new planning space, called RV-space, where all points automatically satisfy a problem’s constraints. Visualizations of reachable volumes can enable operators to see the regions of workspace that different parts of the robot can reach. Samples and paths generated in RV-space naturally conform to constraints, making planning for constrained systems no more difficult than planning for unconstrained systems. Consequently, constrained motion planning problems that were previously difficult or unsolvable become manageable and in many cases trivial. We provide tools and techniques to extend the state of the art sampling based motion planning algorithms to RV-space. We define a reachable volume sampler, a reachable volume local planner and a reachable volume distance metric. We showcase the effectiveness of RV-space by applying these tools to motion planning problems for robots with constraints on the end effectors and/or internal joints of the robot. We show that RV-based planners are more efficient than existing methods, particularly for higher dimensional problems, solving problems with 1000+ degrees of freedom for multi-loop, and tree-like linkages

Soft Sensor-based Servo Press Monitoring

The force that a servo press exerts forming a workpiece is one the most important magnitudes in any metal forming operation. The process force, along with the characteristics of the die, is what shapes the workpiece. When the process force is greater than the maximum force for which the servo press was designed, the servo press integrity can be damaged. Therefore, the knowledge of the process force is of great interest for both, press manufacturers and users. As such, the metal forming sector is seeking systems that can monitor the process force and the operation of the servo press to analyse process’s performance and predict future deviations in the forming operation. Servo press users want to guarantee the quality of the formed parts and reduce facility downtimes due to malfunctions of the press. This dissertation addressed the monitoring of the process force and the dynamic performance of a servo press based on a model based statistical signal processing algorithm known as the dual particle filter (dPF). Initially both, the developed model of a servo press and the proposed dPF, have been experimentally evaluated and validated in a reduced scale test bench. The test bench has been designed and manufactured based on a design methodology that allows to replicate the kinematic and dynamic behaviour of different servo press facilities in the same test bench. The experimental validation has been also carried out in an industrial servo press under three different metal forming processes. The estimation results have proved the ability of the dPF to track the process force throughout the evaluated processes, obtaining a deviation lower than 5% with respect to the measured force signals at the maximum force position. The dPF algorithm has been accelerated by means of a field programmable gate array (FPGA) to achieve a real time estimation.Serbo prentsa batek pieza gordin bat eraldatzeko egindako prozesuko indarra edozein konformatu eragiketako magnitude garrantzitsuenetarikoa da. Prozesuko indarra da, trokelaren ezaugarriekin batera, pieza gordina eraldatzen duena. Prozesuko indarra prentsak diseinuaren arabera jasan dezakeena baino handiagoa bada, prentsak kalteak izan ditzake bere osotasunean. Beraz, prozesuko indarraren ezagutza interes handikoa da, prentsa egileentzat zein erabiltzaileentzat. Hori dela eta, metal eraldatzearen sektoreak prozesuko indarra eta prentsa beraren funtzionamendua monitoriza ditzaketen sistemen bila diardute, prentsaren jarduera aztertu eta eraldatzeko operazioetan etorkizunean izan daitezkeen desbideraketak aurreikusteko. Prentsa erabiltzaileek fabrikatutako piezen kalitatea bermatzea eta funtzionamendu akatsengatiko prentsaren geldialdiak murriztea bilatzen dute. Tesi honek servo prentsa baten prozesuko indarra eta portarea dinamikoaren monitorizazioa jorratzen ditu, dual particle filter (dPF) izeneko modeloetan oinarritutako seinalaren prozesamendu estadistikoko algoritmo baten bitartez. Lehenik eta behin, garatutako servo prentsaren modeloa eta proposatutako dPFa eskalatutako entsegutarako banku batean ebaluatu eta balioztatu dira. Eskalatutako entsegutarako bankua serbo prentsa desberdinen portaera zinematiko eta dinamikoa erreplikatzea ahalbidetzen duen metodologia baten bitartez diseinatu eta gauzatu da. Esperimentu bidezko balioztatzea serbo prentsa industrial batean ere gauzatu da hiru konformatuko prozesu desberdinetan. Estimazio emaitzek dPFak prozesuko indarrari jarraitzeko duen ahalmena forgatu dute, neurtutako indarrarekiko %5ekoa baino txikiagoko desbideraketa lortuz indar maximoa egiten den puntuan. dPF algoritmoa field programmable gate array (FPGA) baten bitartez azeleratu da, denbora errealeko estimazioa lortzeko.La fuerza que una servo prensa ejerce conformando una pieza es la magnitud más importante en cualquier operación de conformado. La fuerza aplicada, junto a las características del troquel, es la magnitud que da forma a la pieza. Cuando la fuerza de proceso es más grande que la fuerza máxima para la que fue diseñada la servo prensa, la integridad de ésta puede verse afectada. Por lo tanto, el conocimiento de la fuerza de proceso es de gr´an interés tanto para los fabricantes de prensas como para los usuarios de las mismas. Así pues, el sector del conformado está buscando sistemas capaces de monitorizar la fuerza de proceso y el funcionamiento de la servo prensa para analizar el proceso y predecir futuras desviaciones de las operaciones de conformado. Los usuarios de las servo prensas quieren garantizar la calidad de las piezas fabricadas y reducir las paradas de las servo prensas debidas al mal funcionamiento de las mismas. Esta tesis aborda la monitorización de la fuerza de proceso y el comportamiento dinámico de una servo prensa mediante un algoritmo de tratamiento estadístico de la señal conocido como el dual Particle Filter (dPF). Inicialmente, tanto el modelo desarrollado como el dPF propuesto han sido evaluados y validados experimentalmente en un banco de ensayos de escala reducida. El banco de ensayos ha sido diseñado y fabricado mediante una metodología de diseño que permite replicar el comportamiento cinem´atico y din´amico de distintas servo prensas en el mismo banco. La validación experimental también se ha llevado a cabo en una servo prensa industrial mediante tres procesos de conformado distintos. Los resultados de estimación han provado la habilidad del dPF para seguir la fuerza de proceso en los procesos evaluados, obteniendo una desviación menor que un 5% con respecto a las señales medidas en el punto donde se da la fuerza máxima. El algoritmo dPF ha sido acelerado mediante un filed programmable gate array (FPGA) para lograr estimaciones en tiempo real

Orbital Effects in Spaceborne Synthetic Aperture Radar Interferometry

This book reviews and investigates orbit-related effects in synthetic aperture Radar interferometry (InSAR). The translation of orbit inaccuracies to error signals in the interferometric phase is concisely described; estimation and correction approaches are discussed and evaluated with special focus on network adjustment of redundantly estimated baseline errors. Moreover, the effect of relative motion of the orbit reference frame is addressed