Claudio Gaz

Identifying the dynamic model used by the KUKA LWR: A reverse engineering approach

An approach is presented for the model identification of the so-called link dynamics used by the KUKA LWR-IV, a lightweight manipulator with elastic joints that is very popular in robotics research but for which a complete and reliable dynamic model is not yet publicly available. The control software interface of this robot provides numerical values of the link inertia matrix and the gravity vector at each configuration, together with link position and joint torque sensor data. Taking advantage of this information, a general procedure is set up for determining the structure and identifying the value of the relevant dynamic coefficients used by the manufacturer in the evaluation of these robot model terms. We call this a reverse engineering approach, because our main goal is to match the numerical data provided by the software interface, using a suitable symbolic model of the robot dynamics and the inertial and gravity coefficients that are being estimated. Only configuration-dependent terms are involved in this process, and thus static experiments are sufficient for this task. The main issues of dynamic model identification for robots with elastic joints are discussed in general, highlighting the pros and cons of the approach taken for this class of KUKA lightweight manipulators. The main identification results, including training and validation tests, are reported together with additional dynamic validation experiments that use the complete identified model and joint torque sensor data.

Plants as sensing devices: the PLEASED experience

In this paper we discuss the first results of the PLEASED project [1] which aims at employing plants as biosensors of a new generation of pervasive and organic wireless sensor networks.

A Unifying Organ Model of Pancreatic Insulin Secretion

The secretion of insulin by the pancreas has been the object of much attention over the past several decades. Insulin is known to be secreted by pancreatic β-cells in response to hyperglycemia: its blood concentrations however exhibit both high-frequency (period approx. 10 minutes) and low-frequency oscillations (period approx. 1.5 hours). Furthermore, characteristic insulin secretory response to challenge maneuvers have been described, such as frequency entrainment upon sinusoidal glycemic stimulation; substantial insulin peaks following minimal glucose administration; progressively strengthened insulin secretion response after repeated administration of the same amount of glucose; insulin and glucose characteristic curves after Intra-Venous administration of glucose boli in healthy and pre-diabetic subjects as well as in Type 2 Diabetes Mellitus. Previous modeling of β-cell physiology has been mainly directed to the intracellular chain of events giving rise to single-cell or cell-cluster hormone release oscillations, but the large size, long period and complex morphology of the diverse responses to whole-body glucose stimuli has not yet been coherently explained. Starting with the seminal work of Grodsky it was hypothesized that the population of pancreatic β-cells, possibly functionally aggregated in islets of Langerhans, could be viewed as a set of independent, similar, but not identical controllers (firing units) with distributed functional parameters. The present work shows how a single model based on a population of independent islet controllers can reproduce very closely a diverse array of actually observed experimental results, with the same set of working parameters. The model’s success in reproducing a diverse array of experiments implies that, in order to understand the macroscopic behaviour of the endocrine pancreas in regulating glycemia, there is no need to hypothesize intrapancreatic pacemakers, influences between different islets of Langerhans, glycolitic-induced oscillations or β-cell sensitivity to the rate of change of glycemia.

Extracting feasible robot parameters from dynamic coefficients using nonlinear optimization methods

We consider the problem of extracting a complete set of numerical parameters that characterize the robot dynamics, starting from the identified values of dynamic coefficients that linearly parametrize the robot dynamic equations. This information is relevant when realistic dynamic simulations have to be performed using standard packages, or when addressing the efficient numerical implementation of model-based control laws using recursive Newton-Euler algorithms. The formulated problem is highly nonlinear and is solved through the use of global optimization techniques, while imposing also physical bounds on the dynamic parameters. The identification and parameter extraction process is illustrated and experimentally validated on the link dynamics of a KUKA LWR IV+ robot.

An islet population model of pancreatic insulin production

Glucose-induced pancreatic insulin release is the fundamental mechanism responsible for glucose homeostasis, its failure determining the clinical picture of Diabetes Mellitus. The details of the feedback loop controlling glycemia through insulin secretion have been an important subject of investigation and modeling for decades. In this note, a recently published population model is considered, whose purpose is to replicate in silico different observed phenomena such as low frequency glycemia-insulinemia oscillations, as well as concordant induction of high-frequency insulin oscillations. The basic idea underlying this model is that the pancreas behaves like a population of independent controllers (each consisting of a fundamental secreting unit, a pancreatic islet), all reacting to the same glucose stimulus, but with varying performance characteristics. This idea has been supported by a relatively wide range of simulations, aiming to replicate most important in vivo experiments concerning pancreatic insulin release. It will be shown in this note that the same mathematical structure can also replicate a set of in vitro experiments, provided that the model context is adapted to the structure of the different experiments to be simulated. More in details, the model will be shown to reproduce the double phase of insulin release during a prolonged glucose stimulus: a first phase of impulsive insulin release, immediately upon glucose administration, and a second phase of more gradual release, dependent on the potentiation effect of the secretory units.

Simulation of Trauma Incidents

Mathematical modeling and simulation with medical applications has gained much interest in the last few years, mainly due to the widespread availability of low-cost technology and computational power. This paper presents an integrated platform for the in-silico simulation of trauma incidents, based on a suite of interacting mathematical models. The models cover the generation of a scenario for an incident, a model of physiological evolution of the affected individuals, including the possible effect of the treatment, and a model of evolution in time of the required medical resources. The problem of optimal resource allocation is also investigated. Model parameters have been identified according to the expertise of medical doctors and by reviewing some related literature. The models have been implemented and exposed as web services, while some software clients have been built for the purpose of testing. Due to its extendability, our integrated platform highlights the potential of model-based simulation in different health-related fields, such as emergency medicine and personal health systems. Modifications of the models are already being used in the context of two funded projects, aiming at evaluating the response of health systems to major incidents with and without model-based decision support.

Data-Driven Modeling of Diabetes Progression

A realistic representation of the long-term physiologic adaptation to developing insulin resistance would facilitate the effective design of clinical trials evaluating diabetes prevention or disease modification therapies. In the present work, a realistic, robust description of the evolution of the compensation of the glucose-insulin system in healthy and diabetic individuals, with particular attention to the physiological compensation to worsening insulin resistance is formulated, its physiological assumptions are presented, and its performance over the span of a lifetime is simulated. Model-based simulations of the long-term evolution of the disease and of its response to therapeutic interventions are consistent with the transient benefits observed with conventional therapies, and with promising effects of radical improvement of insulin sensitivity (as by metabolic surgery) or of β-cell protection. The mechanistic Diabetes Progression Model provides a credible tool by which long-term implications of anti-diabetic interventions can be evaluated.