An overview of a Lagrangian method for analysis of animal wake dynamics

The fluid dynamic analysis of animal wakes is becoming increasingly popular in studies of animal swimming and flying, due in part to the development of quantitative flow visualization techniques such as digital particle imaging velocimetry (DPIV). In most studies, quasi-steady flow is assumed and the flow analysis is based on velocity and/or vorticity fields measured at a single time instant during the stroke cycle. The assumption of quasi-steady flow leads to neglect of unsteady (time-dependent) wake vortex added-mass effects, which can contribute significantly to the instantaneous locomotive forces. In this paper we review a Lagrangian approach recently introduced to determine unsteady wake vortex structure by tracking the trajectories of individual fluid particles in the flow, rather than by analyzing the velocity/vorticity fields at fixed locations and single instants in time as in the Eulerian perspective. Once the momentum of the wake vortex and its added mass are determined, the corresponding unsteady locomotive forces can be quantified. Unlike previous studies that estimated the time-averaged forces over the stroke cycle, this approach enables study of how instantaneous locomotive forces evolve over time. The utility of this method for analyses of DPIV velocity measurements is explored, with the goal of demonstrating its applicability to data that are typically available to investigators studying animal swimming and flying. The methods are equally applicable to computational fluid dynamics studies where velocity field calculations are available

Dynamic Control of the Quattro Robot by the Leg Edges

International audienceThis paper discusses variable selection for the efficient dynamic control of the Quattro parallel robot through an inverse dynamic model expressed by means of leg orientations. A selection is made within a group of variables where each can imply the state of the robot. Besides, in this work, steering a parallel robot dynamically using its self-projection onto the image plane (where the edges of the lower-legs are exploited in control) is proposed and validated for the first time. In the light of the realistic control simulation, the formative points of better control of the Quattro robot are figured out

Predictive Control for Alleviation of Gust Loads on Very Flexible Aircraft

In this work the dynamics of very flexible aircraft are described by a set of non-linear, multi-disciplinary equations of motion. Primary structural components are represented by a geometrically-exact composite beam model which captures the large dynamic deformations of the aircraft and the interaction between rigid-body and elastic degrees-of-freedom. In addition, an implementation of the unsteady vortex-lattice method capable of handling arbitrary kinematics is used to capture the unsteady, three-dimensional flow-eld around the aircraft as it deforms. Linearization of this coupled nonlinear description, which can in general be about a nonlinear reference state, is performed to yield relatively high-order linear time-invariant state-space models. Subsequent reduction of these models using standard balanced truncation results in low-order models suitable for the synthesis of online, optimization-based control schemes that incorporate actuator constraints. Predictive controllers are synthesized using these reduced-order models and applied to nonlinear simulations of the plant dynamics where they are shown to be superior to equivalent optimal linear controllers (LQR) for problems in which constraints are active

Dynamic whole-body motion generation under rigid contacts and other unilateral constraints

The most widely used technique for generating wholebody motions on a humanoid robot accounting for various tasks and constraints is inverse kinematics. Based on the task-function approach, this class of methods enables the coordination of robot movements to execute several tasks in parallel and account for the sensor feedback in real time, thanks to the low computation cost. To some extent, it also enables us to deal with some of the robot constraints (e.g., joint limits or visibility) and manage the quasi-static balance of the robot. In order to fully use the whole range of possible motions, this paper proposes extending the task-function approach to handle the full dynamics of the robot multibody along with any constraint written as equality or inequality of the state and control variables. The definition of multiple objectives is made possible by ordering them inside a strict hierarchy. Several models of contact with the environment can be implemented in the framework. We propose a reduced formulation of the multiple rigid planar contact that keeps a low computation cost. The efficiency of this approach is illustrated by presenting several multicontact dynamic motions in simulation and on the real HRP-2 robot

Recognition-Based Motion Capture and the HumanEva II Test Data

Quantitative comparison of algorithms for human motion capture have been hindered by the lack of standard benchmarks. The development of the HumanEva I & II test sets provides an opportunity to assess the state of the art by evaluating existing methods on the new standardized test videos. This paper presents a comprehensive evaluation of a monocular recognition-based pose recovery algorithm on the HumanEva II clips. The results show that the method achieves a mean relative error of around 10-12 cm per joint

An articulated assistive robot for intuitive hands-on-payload manipulation

This paper presents an intelligent assistive robot designed to help operators in lifting and moving large payloads through direct physical contact (hands-on-payload mode). The mechanical design of the robot is first presented. Although its kinematics are similar to that of a cable-suspended system, the proposed mechanism is based on articulated linkages, thereby allowing the payload to be offset from the rail support on which it is suspended. A dynamic model of the robot is then developed. It is shown that a simplified dynamic model can be obtained using geometric assumptions. Based on the simplified dynamic model, a controller is then presented that handles the physical human-robot interaction and that provides the operator with an intuitive direct control of the payload. Experimental validation on a full-scale prototype is presented in order to demonstrate the effectiveness of the proposed robot and controller

On the mechanical contribution of head stabilization to passive dynamics of anthropometric walkers

During the steady gait, humans stabilize their head around the vertical orientation. While there are sensori-cognitive explanations for this phenomenon, its mechanical e fect on the body dynamics remains un-explored. In this study, we take profit from the similarities that human steady gait share with the locomotion of passive dynamics robots. We introduce a simplified anthropometric D model to reproduce a broad walking dynamics. In a previous study, we showed heuristically that the presence of a stabilized head-neck system significantly influences the dynamics of walking. This paper gives new insights that lead to understanding this mechanical e fect. In particular, we introduce an original cart upper-body model that allows to better understand the mechanical interest of head stabilization when walking, and we study how this e fect is sensitive to the choice of control parameters