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We analyze the observability of 3-D pose from the fusion of visual and inertial sensors. Because the model contains unknown parameters, such as sensor biases, the problem is usually cast as a mixed filtering/identification, with the resulting observability analysis providing necessary conditions for convergence to a unique point estimate. Most models treat sensor bias rates as “noise,” independent...
We study the problem of building a sensor model for the purpose of simulation. Our work is motivated by the potential impact of realistic simulators on the development cycle of software for real robots. The case is made for building models from approximate state information, relieving the burden of ground truth. Unlike calibration, where the goal is to identify and remove error from a signal, our...
This paper relies on the properties of a continuous-time epidemic model with the subpopulations of susceptible-exposed-infectious-recovered epidemic model with finitely distributed delays under a very general, feedback vaccination control rule. The process is subject to eventual perturbations from the equilibrium points which are modeled by Wiener-type noises.
For the purpose of rarefying the effect of interior perturbations and exterior noise on path tracking performance of Unmanned Aerial Vehicles (UAVs), a robust control method is introduced. We earn the dynamic equations of an UAV from Euler-Lagrange formulation and convert it into state space representation. In order to achieve the tracking objective, a mixed H2/H∞ controller is utilized to stabilize...
Parametric filters, such as the Extended Kalman Filter and the Unscented Kalman Filter, typically scale well with the dimensionality of the problem, but they are known to fail if the posterior state distribution cannot be closely approximated by a density of the assumed parametric form.
In this paper, we consider the problem of tracking a reference trajectory for a simplified car model based on unicycle kinematics, whose position only is measured, and where the control input and the measurements are corrupted by independent Gaussian noises. To tackle this problem we devise a novel observer-controller: the invariant Linear Quadratic Gaussian controller (ILQG). It is based on the Linear...
Dynamic Movement Primitives (DMPs) are a common method for learning a control policy for a task from demonstration. This control policy consists of differential equations that can create a smooth trajectory to a new goal point. However, DMPs only have a limited ability to generalize the demonstration to new environments and solve problems such as obstacle avoidance. Moreover, standard DMP learning...
Robust, scalable place recognition is a core competency for many robotic applications. However, when revisiting places over and over, many state-of-the-art approaches exhibit reduced performance in terms of computation and memory complexity and in terms of accuracy. For successful deployment of robots over long time scales, we must develop algorithms that get better with repeated visits to the same...
The realization and utilization of multimodal locomotion to enable robots to accomplish useful tasks is a significantly challenging problem in robotics. Related to the challenge, it is crucial to notice that the locomotion dynamics of the robots is a result of interactions between a particular control structure and its body-environment dynamics. From this perspective, this paper presents a simple...
This paper proposes a novel approach to generate trajectories that generalize given demonstrations according to optimality criteria. By formulating the problem as a quadratic program we can efficiently incorporate constraints to adapt to new desired motion requirements while achieving the main goal of matching the acceleration profile of the demonstration. This makes our method particularly suited...
The paper deals with noise decontamination of chaotic time series under the assumption that some a priori information about the system which produced the time series is known in advance. We show that this a priori information can be quite naturally used in standard maximum likelihood approaches. The obtained results show attractive capabilities for on line and low cost implementation.
This paper presents a comparative study of the temporal structure of the glottal flow derivative estimates in relation to an idealized view of voice source realizations as defined by Liljencrants-Fant's model. Specifically, we endeavor to ascertain the extent by which Liljencrants-Fant's model can be used to represent the glottal flow derivative estimates obtained via closed-phase pitch synchronous...
A novel algorithm for the reduction of several types of noise that occur typically in wireless digital speech communications is described in this paper. The algorithm aims at reducing the spectral discontinuities of the signal by analyzing the 2D spectral map and closing the gaps between the frames using heuristic rules. Some experimental evaluations are reported.
In this paper we consider the problem of synchronization of coupled chaotic systems. First we show the limitations of existing techniques for studying of the synchronization. Then we introduce the notion of local conditional (transversal) Lyapunov exponents. We show that they can be successfully used in investigations of synchronization properties. We develop a new criterion for synchronization based...
A stochastic linear hybrid system is said to be observable if the hybrid state of the system can be uniquely determined from its output. In this paper, we derive conditions for the observability of stochastic linear hybrid systems by exploiting the information obtained from system noise characteristics. Having established the necessary criteria for observability, we study the effect of these conditions...
Iterative Learning Control (ILC) is a known technique for improving the performance of systems or processes that operate repetitively over a fixed time interval. ILC generates a feedforward signal effective for providing good tracking control. However, there still exist a number of problems which hinder extensions of ILC schemes. The major obstacle is perhaps the requirement that the trajectory (or...
We proposo a design method of optimal FIR filter which selectively extracts the particular moving object from other moving objects and noise. Stochastic approach is applied to the problem using the information of signals and the probability distribution of velocity vectors. In the method, the frequency response of the proposed Linear Trajectory Filter (LTF) specified by a priori information of the...
Much research attention in recent years has been focussed on the subject of oversampled analogue-to-digital and digital-to-analogue conversion, based on the principle of sigma-delta modulation. Theoretical analysis of these conversion methods has been complicated by their nonlinear nature, precluding the application of standard linear circuit analysis methods. In recent years a number of researchers...
Target motion analysis (TMA) for a rectilinear source movement (RSM) has been intensively studied in the last ten years. But difficulties still exist, especially when source heading or speed changes are within the same time as the conventional TMA convergence time. This paper is concerned with a new method of batch TMA for maneuvering sources using a non-linear least-squares fit between the whole...
Quantification of spin density, R∗2 decay and off-resonance frequency maps is very important in some applications of magnetic resonance imaging (MRI). To reconstruct these parameter maps, a time-varying model such as mono-exponentials must be used to represent the signal from each voxel. When only a single-shot trajectory is adopted, the underlying reconstruction problem is significantly nonlinear...
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