The power dissipation method and kinematic reducibility of multiple-model robotic systems

This paper develops a formal connection between the power dissipation method (PDM) and Lagrangian mechanics, with specific application to robotic systems. Such a connection is necessary for understanding how some of the successes in motion planning and stabilization for smooth kinematic robotic systems can be extended to systems with frictional interactions and overconstrained systems. We establish this connection using the idea of a multiple-model system, and then show that multiple-model systems arise naturally in a number of instances, including those arising in cases traditionally addressed using the PDM. We then give necessary and sufficient conditions for a dynamic multiple-model system to be reducible to a kinematic multiple-model system. We use this result to show that solutions to the PDM are actually kinematic reductions of solutions to the Euler-Lagrange equations. We are particularly motivated by mechanical systems undergoing multiple intermittent frictional contacts, such as distributed manipulators, overconstrained wheeled vehicles, and objects that are manipulated by grasping or pushing. Examples illustrate how these results can provide insight into the analysis and control of physical systems

Nonprehensile Dynamic Manipulation: A Survey

Nonprehensile dynamic manipulation can be reason- ably considered as the most complex manipulation task. It might be argued that such a task is still rather far from being fully solved and applied in robotics. This survey tries to collect the results reached so far by the research community about planning and control in the nonprehensile dynamic manipulation domain. A discussion about current open issues is addressed as well

Non-linear estimation is easy

Non-linear state estimation and some related topics, like parametric estimation, fault diagnosis, and perturbation attenuation, are tackled here via a new methodology in numerical differentiation. The corresponding basic system theoretic definitions and properties are presented within the framework of differential algebra, which permits to handle system variables and their derivatives of any order. Several academic examples and their computer simulations, with on-line estimations, are illustrating our viewpoint

Developing Design and Analysis Framework for Hybrid Mechanical-Digital Control of Soft Robots: from Mechanics-Based Motion Sequencing to Physical Reservoir Computing

The recent advances in the field of soft robotics have made autonomous soft robots working in unstructured dynamic environments a close reality. These soft robots can potentially collaborate with humans without causing any harm, they can handle fragile objects safely, perform delicate surgeries inside body, etc. In our research we focus on origami based compliant mechanisms, that can be used as soft robotic skeleton. Origami mechanisms are inherently compliant, lightweight, compact, and possess unique mechanical properties such as– multi-stability, nonlinear dynamics, etc. Researchers have shown that multi-stable mechanisms have applications in motion-sequencing applications. Additionally, the nonlinear dynamic properties of origami and other soft, compliant mechanisms are shown to be useful for ‘morphological computation’ in which the body of the robot itself takes part in performing complex computations required for its control. In our research we demonstrate the motion-sequencing capability of multi-stable mechanisms through the example of bistable Kresling origami robot that is capable of peristaltic locomotion. Through careful theoretical analysis and thorough experiments, we show that we can harness multistability embedded in the origami robotic skeleton for generating actuation cycle of a peristaltic-like locomotion gait. The salient feature of this compliant robot is that we need only a single linear actuator to control the total length of the robot, and the snap-through actions generated during this motion autonomously change the individual segment lengths that lead to earthworm-like peristaltic locomotion gait. In effect, the motion-sequencing is hard-coded or embedded in the origami robot skeleton. This approach is expected to reduce the control requirement drastically as the robotic skeleton itself takes part in performing low-level control tasks. The soft robots that work in dynamic environments should be able to sense their surrounding and adapt their behavior autonomously to perform given tasks successfully. Thus, hard-coding a certain behavior as in motion-sequencing is not a viable option anymore. This led us to explore Physical Reservoir Computing (PRC), a computational framework that uses a physical body with nonlinear properties as a ‘dynamic reservoir’ for performing complex computations. The compliant robot ‘trained’ using this framework should be able to sense its surroundings and respond to them autonomously via an extensive network of sensor-actuator network embedded in robotic skeleton. We show for the first time through extensive numerical analysis that origami mechanisms can work as physical reservoirs. We also successfully demonstrate the emulation task using a Miura-ori based reservoir. The results of this work will pave the way for intelligently designed origami-based robots with embodied intelligence. These next generation of soft robots will be able to coordinate and modulate their activities autonomously such as switching locomotion gait and resisting external disturbances while navigating through unstructured environments

Data-driven nonlinear aeroelastic models of morphing wings for control

Accurate and efficient aeroelastic models are critically important for enabling the optimization and control of highly flexible aerospace structures, which are expected to become pervasive in future transportation and energy systems. Advanced materials and morphing wing technologies are resulting in next-generation aeroelastic systems that are characterized by highly-coupled and nonlinear interactions between the aerodynamic and structural dynamics. In this work, we leverage emerging data-driven modeling techniques to develop highly accurate and tractable reduced-order aeroelastic models that are valid over a wide range of operating conditions and are suitable for control. In particular, we develop two extensions to the recent dynamic mode decomposition with control (DMDc) algorithm to make it suitable for flexible aeroelastic systems: 1) we introduce a formulation to handle algebraic equations, and 2) we develop an interpolation scheme to smoothly connect several linear DMDc models developed in different operating regimes. Thus, the innovation lies in accurately modeling the nonlinearities of the coupled aerostructural dynamics over multiple operating regimes, not restricting the validity of the model to a narrow region around a linearization point. We demonstrate this approach on a high-fidelity, three-dimensional numerical model of an airborne wind energy (AWE) system, although the methods are generally applicable to any highly coupled aeroelastic system or dynamical system operating over multiple operating regimes. Our proposed modeling framework results in real-time prediction of nonlinear unsteady aeroelastic responses of flexible aerospace structures, and we demonstrate the enhanced model performance for model predictive control. Thus, the proposed architecture may help enable the widespread adoption of next-generation morphing wing technologies

Review on the Modeling of Electrostatic MEMS

Electrostatic-driven microelectromechanical systems devices, in most cases, consist of couplings of such energy domains as electromechanics, optical electricity, thermoelectricity, and electromagnetism. Their nonlinear working state makes their analysis complex and complicated. This article introduces the physical model of pull-in voltage, dynamic characteristic analysis, air damping effect, reliability, numerical modeling method, and application of electrostatic-driven MEMS devices

An empirical mean-field model of symmetry-breaking in a turbulent wake

Improved turbulence modeling remains a major open problem in mathematical physics. Turbulence is notoriously challenging, in part due to its multiscale nature and the fact that large-scale coherent structures cannot be disentangled from small-scale fluctuations. This closure problem is emblematic of a greater challenge in complex systems, where coarse-graining and statistical mechanics descriptions break down. This work demonstrates an alternative data-driven modeling approach to learn nonlinear models of the coherent structures, approximating turbulent fluctuations as state-dependent stochastic forcing. We demonstrate this approach on a high-Reynolds number turbulent wake experiment, showing that our model reproduces empirical power spectra and probability distributions. The model is interpretable, providing insights into the physical mechanisms underlying the symmetry-breaking behavior in the wake. This work suggests a path toward low-dimensional models of globally unstable turbulent flows from experimental measurements, with broad implications for other multiscale systems