332 research outputs found
Shared Control Based on Extended Lipschitz Analysis With Application to Human-Superlimb Collaboration
This paper presents a quantitative method to construct voluntary manual
control and sensor-based reactive control in human-robot collaboration based on
Lipschitz conditions. To collaborate with a human, the robot observes the
human's motions and predicts a desired action. This predictor is constructed
from data of human demonstrations observed through the robot's sensors.
Analysis of demonstration data based on Lipschitz quotients evaluates a)
whether the desired action is predictable and b) to what extent the action is
predictable. If the quotients are low for all the input-output pairs of
demonstration data, a predictor can be constructed with a smooth function. In
dealing with human demonstration data, however, the Lipschitz quotients tend to
be very high in some situations due to the discrepancy between the information
that humans use and the one robots can obtain. This paper a) presents a method
for seeking missing information or a new variable that can lower the Lipschitz
quotients by adding the new variable to the input space, and b) constructs a
human-robot shared control system based on the Lipschitz analysis. Those
predictable situations are assigned to the robot's reactive control, while
human voluntary control is assigned to those situations where the Lipschitz
quotients are high even after the new variable is added. The latter situations
are deemed unpredictable and are rendered to the human. This human-robot shared
control method is applied to assist hemiplegic patients in a bimanual eating
task with a Supernumerary Robotic Limb, which works in concert with an
unaffected functional hand
Hybrid Open-Loop Closed-Loop Control of Coupled Human-Robot Balance During Assisted Stance Transition with Extra Robotic Legs
A new approach to the human-robot shared control of the Extra Robotic Legs
(XRL) wearable augmentation system is presented. The XRL system consists of two
extra legs that bear the entirety of its backpack payload, as well as some of
the human operator's weight. The XRL System must support its own balance and
assist the operator stably while allowing them to move in selected directions.
In some directions of the task space the XRL must constrain the human motion
with position feedback for balance, while in other directions the XRL must have
no position feedback, so that the human can move freely. Here, we present
Hybrid Open-Loop / Closed-Loop Control Architecture for mixing the two control
modes in a systematic manner. The system is reduced to individual joint
feedback control that is simple to implement and reliable against failure. The
method is applied to the XRL system that assists a human in conducting a
nuclear waste decommissioning task. A prototype XRL system has been developed
and demonstrated with a simulated human performing the transition from standing
to crawling and back again while coupled to the prototype XRL system
Learning of Causal Observable Functions for Koopman-DFL Lifting Linearization of Nonlinear Controlled Systems and Its Application to Excavation Automation
Effective and causal observable functions for low-order lifting linearization
of nonlinear controlled systems are learned from data by using neural networks.
While Koopman operator theory allows us to represent a nonlinear system as a
linear system in an infinite-dimensional space of observables, exact
linearization is guaranteed only for autonomous systems with no input, and
finding effective observable functions for approximation with a low-order
linear system remains an open question. Dual Faceted Linearization uses a set
of effective observables for low-order lifting linearization, but the method
requires knowledge of the physical structure of the nonlinear system. Here, a
data-driven method is presented for generating a set of nonlinear observable
functions that can accurately approximate a nonlinear control system to a
low-order linear control system. A caveat in using data of measured variables
as observables is that the measured variables may contain input to the system,
which incurs a causality contradiction when lifting the system, i.e. taking
derivatives of the observables. The current work presents a method for
eliminating such anti-causal components of the observables and lifting the
system using only causal observables. The method is applied to excavation
automation, a complex nonlinear dynamical system, to obtain a low-order lifted
linear model for control design
Dynamic Modeling of Bucket-Soil Interactions Using Koopman-DFL Lifting Linearization for Model Predictive Contouring Control of Autonomous Excavators
A lifting-linearization method based on the Koopman operator and Dual Faceted
Linearization is applied to the control of a robotic excavator. In excavation,
a bucket interacts with the surrounding soil in a highly nonlinear and complex
manner. Here, we propose to represent the nonlinear bucket-soil dynamics with a
set of linear state equations in a higher-dimensional space. The space of
independent state variables is augmented by adding variables associated with
nonlinear elements involved in the bucket-soil dynamics. These include
nonlinear resistive forces and moment acting on the bucket from the soil, and
the effective inertia of the bucket that varies as the soil is captured into
the bucket. Variables associated with these nonlinear resistive and inertia
elements are treated as additional state variables, and their time evolution is
represented as another set of linear differential equations. The lifted linear
dynamic model is then applied to Model Predictive Contouring Control, where a
cost functional is minimized as a convex optimization problem thanks to the
linear dynamics in the lifted space. The lifted linear model is tuned based on
a data-driven method by using a soil dynamics simulator. Simulation experiments
verify the effectiveness of the proposed lifting linearization compared to its
counterpart
Wearable Conductive Fiber Sensors for Multi-Axis Human Joint Angle Measurements
BACKGROUND: The practice of continuous, long-term monitoring of human joint motion is one that finds many applications, especially in the medical and rehabilitation fields. There is a lack of acceptable devices available to perform such measurements in the field in a reliable and non-intrusive way over a long period of time. The purpose of this study was therefore to develop such a wearable joint monitoring sensor capable of continuous, day-to-day monitoring. METHODS: A novel technique of incorporating conductive fibers into flexible, skin-tight fabrics surrounding a joint is developed. Resistance changes across these conductive fibers are measured, and directly related to specific single or multi-axis joint angles through the use of a non-linear predictor after an initial, one-time calibration. Because these sensors are intended for multiple uses, an automated registration algorithm has been devised using a sensitivity template matched to an array of sensors spanning the joints of interest. In this way, a sensor array can be taken off and put back on an individual for multiple uses, with the sensors automatically calibrating themselves each time. RESULTS: The wearable sensors designed are comfortable, and acceptable for long-term wear in everyday settings. Results have shown the feasibility of this type of sensor, with accurate measurements of joint motion for both a single-axis knee joint and a double axis hip joint when compared to a standard goniometer used to measure joint angles. Self-registration of the sensors was found to be possible with only a few simple motions by the patient. CONCLUSION: After preliminary experiments involving a pants sensing garment for lower body monitoring, it has been seen that this methodology is effective for monitoring joint motion of the hip and knee. This design therefore produces a robust, comfortable, truly wearable joint monitoring device
DYNAMIC PERFORMANCE OF NONLINEAR 100X DISPLACEMENT AMPLIFICATION PIEZOELECTRIC ACTUATOR
ABSTRACT Th
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