Davide Spinello

Review of modeling electrostatically actuated microelectromechanical systems

A wide range of microelectromechanical systems (MEMSs) and devices are actuated using electrostatic forces. Multiphysics modeling is required, since coupling among different fields such as solid and fluid mechanics, thermomechanics and electromagnetism is involved. This work presents an overview of models for electrostatically actuated MEMSs. Three-dimensional nonlinear formulations for the coupled electromechanical fluid–structure interaction problem are outlined. Simplified reduced-order models are illustrated along with assumptions that define their range of applicability. Theoretical, numerical and experimental works are classified according to the mechanical model used in the analysis.

Electromechanical Model of Electrically Actuated Narrow Microbeams

A consistent one-dimensional distributed electromechanical model of an electrically actuated narrow microbeam with width/height between 0.5–2.0 is derived, and the needed pull-in parameters are extracted with different methods. The model accounts for the position-dependent electrostatic loading, the fringing field effects due to both the finite width and the finite thickness of a microbeam, the mid-plane stretching, the mechanical distributed stiffness, and the residual axial load. Both clamped–clamped and clamped-free (cantilever) microbeams are considered. The method of moments is used to estimate the electrostatic load. The resulting nonlinear fourth-order differential equation under appropriate boundary conditions is solved by two methods. Initially, a one-degree-of-freedom model is proposed to find an approximate solution of the problem. Subsequently, the meshless local Petrov–Galerkin (MLPG) and the finite-element (FE) methods are used, and results from the three methods are compared. For the MLPG method, the kinematic boundary conditions are enforced by introducing a set of Lagrange multipliers, and the trial and the test functions are constructed using the generalized moving least-squares approximation. The nonlinear system of algebraic equations arising from the MLPG and the FE methods are solved by using the displacement iteration pull-in extraction (DIPIE) algorithm. Three-dimensional FE simulations of narrow cantilever and clamped–clamped microbeams are also performed with the commercial code ANSYS. Furthermore, computed results are compared with those arising from other distributed models available in the literature, and it is shown that improper fringing fields give inaccurate estimations of the pull-in voltages and of the pull-in deflections. 1641

Effects of Casimir force on pull-in instability in micromembranes

We analyze pull-in instability of electrostatically actuated microelectromechanical systems, and study changes in pull-in parameters due to the Casimir effect. When the size of the device is reduced, the magnitude of the Casimir force is comparable with that of the Coulomb force and it significantly alters pull-in parameters. We model the deformable conductor as an elastic membrane and consider different geometries. Beyond a certain critical size the pull-in instability occurs with zero applied voltage, and the device may collapse during the fabrication process.

Nonlinear Estimation With State-Dependent Gaussian Observation Noise

We consider the problem of estimating the state of a system when measurement noise is a function of the systems state. We propose generalizations of the extended Kalman filter and the iterated extended Kalman filter that can be utilized when the state estimate distribution is approximately Gaussian. The state estimate is computed by an iterative root-searching method that maximizes a maximum likelihood function. The new filter allows for the consistent treatment of a class of control problem involving nonlinear estimation from measurements with state-dependent noise. The effectiveness of the estimation algorithm is illustrated for a control problem with a mobile bearing-only sensor.

Information Flow in Animal-Robot Interactions

The nonverbal transmission of information between social animals is a primary driving force behind their actions and, therefore, an important quantity to measure in animal behavior studies. Despite its key role in social behavior, the flow of information has only been inferred by correlating the actions of individuals with a simplifying assumption of linearity. In this paper, we leverage information-theoretic tools to relax this assumption. To demonstrate the feasibility of our approach, we focus on a robotics-based experimental paradigm, which affords consistent and controllable delivery of visual stimuli to zebrafish. Specifically, we use a robotic arm to maneuver a life-sized replica of a zebrafish in a predetermined trajectory as it interacts with a focal subject in a test tank. We track the fish and the replica through time and use the resulting trajectory data to measure the transfer entropy between the replica and the focal subject, which, in turn, is used to quantify one-directional information flow from the robot to the fish. In agreement with our expectations, we find that the information flow from the replica to the zebrafish is significantly more than the other way around. Notably, such information is specifically related to the response of the fish to the replica, whereby we observe that the information flow is reduced significantly if the motion of the replica is randomly delayed in a surrogate dataset. In addition, comparison with a control experiment, where the replica is replaced by a conspecific, shows that the information flow toward the focal fish is significantly more for a robotic than a live stimulus. These findings support the reliability of using transfer entropy as a measure of information flow, while providing indirect evidence for the efficacy of a robotics-based platform in animal behavioral studies.

Cooperative localization of an acoustic source using towed hydrophone arrays

We describe field experiments in which a team of autonomous underwater vehicles cooperatively localize an acoustic source. The team implements a data fusion algorithm to enhance the localization performance of each individual vehicle and implements a decentralized motion control algorithm so that each vehicle maneuvers to minimize the joint localization error of the acoustic source. Each autonomous underwater vehicle is equipped with a custom-designed towed hydrophone array that measures the bearing angle between the array and the acoustic source. The noise statistics of the hydrophone arrays are state-dependent, and a generalized Kalman filter that accounts for the state-dependant measurement noise is utilized for localization.

Nonuniform Deployment of Autonomous Agents in Harbor-Like Environments

We propose a nonuniform deployment strategy of a group of homogeneous autonomous agents in harbor-like environments. High value units berthed in the area need to be secured against external attacks. Defenders deployed in the area are expected to monitor, intercept, engage, and neutralize threats. In the framework of decentralized coordinated multi-agent systems, we model and simulate the optimal deployment of a group of mobile autonomous agents that accounts for a risk map of the area and the optimal trajectories that minimize the energy consumed to intercept a threat in a given area of interest. Theoretical results are numerically illustrated through simulations in a realistic harbor protection scenario.

Distributed Full-State Observers With Limited Communication and Application to Cooperative Target Localization

We present a fully decentralized motion control algorithm for the coordination of platoons of mobile agents with highly restricted communication capabilities. In order to address very low bandwidth communication between agents and time varying communication network topologies, we utilize a distributed full-state observer onboard each agent. Agent motion and data fusion algorithms are implemented locally by each agent based on the state of the local full-state estimator. Although no separation principle exists between decentralized agent motion control and distributed data fusion in general, we introduce a gradient-based framework in which simultaneously we achieve asymptotic agreement among full-state estimators and convergence of desired agent motion.

Planar Kinematics Analysis of a Snake-Like Robot

This research was partially funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) and FedDev Ontario.

Path Following and Shape Morphing With a Continuous Slender Mechanism

We present the continuous model of a mobile slender mechanism that is intended to be the structure of an autonomous hyper-redundant slender robotic system. Rigid body degrees-of-freedom (DOF) and deformability are coupled through a Lagrangian weak formulation that includes control inputs to achieve forward locomotion and shape tracking. The forward locomotion and the shape tracking are associated to the coupling with a substrate that models a generic environment with which the mechanism could interact. The assumption of small deformations around rigid body placements allows to adopt the floating reference kinematic description. By posing the distributed parameter control problem in weak form, we naturally introduce an approximate solution technique based on Galerkin projection on the linear mode shapes of the Timoshenko beam model that is adopted to describe the body of the system. Simulation results illustrate coupling among forward motion and shape tracking as described by the equations governing the system.