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A fast 3-D route planning method for unmanned air vehicle is proposed which can generate physically realizable 3-D route within a reasonable time. Our method includes two steps: First, 2-D route planning generates a route which satisfies turning radius constraint(abbreviated as R-constrained below); second, 3-D route planning generates 3-D route in vertical profile of the 2-D route. To make 2-D route R-constrained, a method is proposed by supposing 2-D route of air vehicle is composed of a sequence of arc route segments and tangential points between neighboring arcs are searching nodes. 3 -D route planning is considered as optimal control problem, and its route can be determined by applying motion equations of air vehicle. The experiments show that our method can produce feasible 3-D routes within a reasonable time, and ensure the planned 3-D routes satisfy aerodynamics constraints of air vehicle.
The covariance intersection (CI) framework represents a generalization of the Kalman filter that permits filtering and estimation to be performed in the presence of unmodeled correlations. As described in previous papers, unmodeled correlations arise in virtually all real-world problems; but in many applications the correlations are so significant that they cannot be 'swept under the rug' simply by injecting extra stabilizing noise within a traditional Kalman filter. In this paper we briefly describe some of the properties of the CI algorithm and demonstrate their relevance to the notoriously difficult problem of simultaneous map building and localization for autonomous vehicles.
Constructing unified identification algorithms using a small number of observations for adaptive control and navigation systems
The paper deals with the development of identification algorithms for adaptive control systems, as well as with specific features related to the realization of the algorithms. We develop an algebraic approach to the construction of identification algorithms. This involves a more realistic formulation of the task as compared with that adopted in the theory of statistical estimation. In particular, we construct the procedures of analysis and selection of the most informative data, based on the degrees of conditionality proposed in the paper. A qualitative theory of identification is developed.
System based on fiber optic gravimeters for a measurement of gravitational field gradient vector for navigation of flying objects
There are many tasks, which are required simultaneously to measure value and direction of gravitation field vector in 3D space. For instance, the task of flying objects navigation by measurement of full gravitation field vector horizontal gradient. To solve this problem is suggested the system, consists of two remote fiber-optic gravimeters, situated in different ends of flying object. Fiber-optic gravimeter based on effect of influence of longitudinal strains of silicon single-mode fibers on lightwave phase shift. Fiber-optic gravimeter includes fiber-optic interferometers Mach-Zehnder and electronic scheme for measurement of phase shift. Sensitivity of this fiber-optic gravimeter is 10-7 g, where g-acceleration of free fall, if resolution of electronic scheme is 10-3 rad. As source of radiation for fiber-optic gravimeter is employed tunable diode laser on GaAlAs-GaAs with external dispertial cavity and frequency modulation of radiation. Frequency modulation of radiation is applied for measurement of phase shift in fiber-optic interferometers.
The problem of dynamic phase ambiguity resolution and filtering for interferometric GPS attitude determination is considered. Traditionally, the resolution of the phase ambiguity and the filtering stages were performed separately, with the filter formulated on the basis that the phase ambiguity is correctly resolved. Should the pre- processing stage not resolve the ambiguity correctly, erroneous results may occur. In response, a unified approach is proposed in which the ambiguity resolution and filtering processes are combined under a Gaussian sum filtering (GSF) framework. In addition, the consideration of the carrier Doppler shift information leads to a dynamic phase ambiguity resolution model which is naturally enveloped by the GSF paradigm. Simulated results in both static and dynamic attitude scenarios illustrate the statistical robustness of the GSF methodology.
Finite element modeling and simulation on a novel microstructure silicon accelerometer with direct frequency output
This paper establishes the finite element method (FEM) model of a practical silicon beam resonator attached to a E-type round diaphragm, which is used for measuring the acceleration, in details, based on sensing mechanism of a novel resonant silicon accelerometer. The relationship between the basic natural frequency of the beam resonator and the measured acceleration is calculated, analyzed and investigated by making use of the above FEM model. Some important qualitative and quantitative results on the natural frequency-acceleration relationship of the above bema resonator and the microsensor are obtained. Finally, based on the differential output scheme, a set of appropriate parameters of the above sensing structure is determined, for measuring the acceleration within (-100, +100)m/s2. The simulation result shows that the differential frequency output is within (-8833, 8910) Hz and reference zero frequency is 126914 Hz for zero acceleration, corresponding to the determined parameters of the above sensing structure.
Passive position location using bearings only information is a classical navigation problem, Various methods proposed to date use either triangulation or circulation rules in nonlinear filtering framework; like nonlinear least squares filtering method providing approximate maximum likelihood estimates and extended Kalman filtering method providing approximate minimum variance estimates. Both are approximate filters due to inherent linearization in these methods. A completely optimal nonlinear filter, referred to as Bayes' conditional density filter is presented in this paper. This method is not subjected to any linearization mechanisms as in other methods currently in use. However, the method is subjected to increased computational burden.
An advanced control architecture for autonomous vehicles is presented. The hierarchical architecture consists of four levels: a vehicle level, a control level, a rule-based level and a knowledge-based level. A special focus is on forms of internal representation, which have to be chosen adequately for each level. The control scheme is applied to VaMP, a Mercedes passenger car which autonomously performs missions on German freeways. VaMP perceives the environment with its sense of vision and conventional sensors. It controls its actuators for locomotion and attention focusing. Modules for perception, cognition and action are discussed.
We describe a 3D vision system TILT, designed and constructed at ONERA in France. The system was developed for mobile robotics applications and successfully tested on an outdoor vehicle. The system consists of two main units: the optical head and the electronic rack. Original aspects of TILT concern its fast frame rate and its angular resolution. Main parts are described and some results are discussed. Some images are analyzed, giving typical examples of possible performances.
Obstacles to the ground vehicles on the road sometimes is very difficult to detect by active sensors. For example, a fallen tree on the road may not be higher than a speed bump. It is known that human vision system is very good at perception of depth discontinuity and surface orientation change, particularly deep concavity, while comparatively weak at absolute measurement of 3D distance. This is just opposite to the active sensor which are accurate at absolute measurements at isolated surface points, but lack of direct or indirect sensing mechanism of these tow aspects of scene. The obstacle avoidance is more relied on the detection of occlusion edges than that of absolute depth structure. In the past we have modeled the early vision process of primate's visual cortex for binocular and motion based stereo vision using Lie group theory and implemented the algorithm in computers. The algorithm provides surface orientation and range information at places in a scene where the surface regular, namely, locally flat and our energy minimization scheme converges. In a real scene there are plenty of places where the surface structure are not regular, and the energy minimization scheme does not converge. The 'high energy' location will mark the occlusion edge. A further test of 'structure lost' will confirm the occlusion edge, and thus provide the essential information for occlusion avoidance.
This paper presents a qualitative approach based on visual cues for man-made landmark recognition in outdoor as a navigational aid. This work is part of an exploratory effort in which the primary objective is to demonstrate an alternate navigation mode using landmarks for unmanned ground vehicle systems. The proposed approach uses color video as the data input and consists of the following three major functions: (a) eliminating most of the natural background clutter in given imagery, and generating regions of interest (ROIs), (b) extracting arcs, ellipses, circles, rectangles and prescribed geometric shapes in the ROIs, and (c) recognition of man-made landmarks by parts. We present details of each of the modules and the results on real image sequences.
A new method for automatic map building and vehicle localization is described. The method utilizes relative measurements of distance and angle between features of the environment. The noise in successive relative measurements is uncorrelated, hence a significant saving in memory and computation is achieved over other techniques, such as the augmented state Kalman filter.
In the gyrocompass system, the use of the fiber optic gyroscope (FOG) makes this traditional system considerably attractive because it has strong points in terms of weight, power, warming-up time, and cost. In this paper, a novel dynamic north-finding scheme based on a FOG, which can be applied to the gyrocompass system, is presented. The analytical model for the earth signal of the FOG is described, and the earth signals passed through lock-in amplifiers are modeled. Additionally, a north-finding algorithm using two lock-in amplifier outputs is developed, and the proposed scheme is organized by the developed algorithm. Simulation results are included to verify the performance of the proposed scheme.