Alexander B. Miller

UAV Control on the Basis of 3D Landmark Bearing-Only Observations

The article presents an approach to the control of a UAV on the basis of 3D landmark observations. The novelty of the work is the usage of the 3D RANSAC algorithm developed on the basis of the landmarks’ position prediction with the aid of a modified Kalman-type filter. Modification of the filter based on the pseudo-measurements approach permits obtaining unbiased UAV position estimation with quadratic error characteristics. Modeling of UAV flight on the basis of the suggested algorithm shows good performance, even under significant external perturbations.

Developing algorithms of object motion control on the basis of Kalman filtering of bearing-only measurements

Consideration was given to the problem of controlling the motion of an object subject to random disturbances. It was assumed that the bearings to beacons with certain coordinates are open to measurements. The bearing angles are nonlinear functions of the current coordinates. Therefore, the present paper suggested a modified method of pseudomeasurements proving bias-free estimates of the current object coordinates using the algorithm of conditionallyoptimal filtering. These estimates underlie the object control algorithm demonstrating the possibility of high-quality tracing of the given motion trajectory.

Tracking of the UAV trajectory on the basis of bearing-only observations

This work considers the tracking of the UAV (unmanned aviation vehicle) path on the basis of bearing-only observations including azimuth and elevation angles. The significance of this research becomes clear in the case when GPS either does not work at all or produce the high level of the measurement errors. It is assumed that either UAVs opto-electronic cameras or radar systems are able to capture the angular position of objects with known coordinates and to measure the azimuth and elevation angles of the sight line. Such measurements involve the real position of UAV in implicit form, and therefore some of nonlinear filters such as Extended Kalman filter (EKF) or others may be used in order to implement these measurements for UAV control. However, all such approximate nonlinear filters produce the estimations with unknown bias and quadratic errors. This peculiarity prevents the data fusion in more or less regular way. Meanwhile, there is well-known method of pseudomeasurements which reduces the estimation problem to the linear settings. In this article we develop the modified pseudomeasurement method without bias and with the possibility to evaluate the second moments of the UAV position errors which helps to realize the data fusion. On the basis of this filtering algorithm we develop the control algorithm for tracking of given reference path under external perturbation and noised angular measurements. Modelling examples show the nice performance of the control algorithm.

3D path planning in a threat environment

We address optimal path planning in three dimensional space for an unmanned aerial vehicle (UAV) in the stationary risk environment. We separate the task into two stage, in the first one we determine the risk optimal 2D path for fixed time problem. Then we solve the series of BVPs (Boundary Value Problems) with different UAV speeds and determine the admissible 2D path, which satisfies the time and risk constraints. In the last step one takes into account the relief along the chosen path and determine the approximated 3D path, which minimizes 2D threat along the path and satisfies other constraints.

UAV Navigation On The Basis Of The Feature Points Detection On Underlying Surface.

This work relates to the intelligent systems tracking such as UAV’s (unmanned aviation vehicle) navigation in GPS-denied environment. Generally it considers the tracking of the UAV path on the basis of bearing-only observations including azimuth and elevation angles. It is assumed that UAV’s cameras are able to capture the angular position of reference points and to measure the directional angles of the sight line. Such measurements involve the real position of UAV in implicit form, and therefore some of nonlinear filters such as Extended Kalman filter (EKF) or others must be used in order to implement these measurements for UAV control. Meanwhile, there is well-known method of pseudomeasurements which reduces the estimation problem to the linear settings, though these method has a bias. Recently it was shown that the application of the modified filter based on the pseudomeasurements approach provides the reliable UAV control on the basis of the observation of reference points nominated before the flight. This approach uses the known coordinates of reference points and then applies the optimal linear Kalman type filter. The principal difference with the usage of location of reference points nominated in advance is that here we use the observed reference points detected on-line during the flight. This approach permits to reduce the necessary on-board memory up to reasonable size. In this article the modified pseudomeasurement method without bias for estimation of the UAV position has been suggested. On the basis of this estimation the control algorithm which provides the tracking of reference path in case of external perturbation and the angles measurements errors has been developed. Another principal novelty of this work is the usage of RANSAC approach to detection of reference landmarks which used further for estimation of the UAV position.

Control of connected Markov chains. Application to congestion avoidance in the Internet

The article considers the optimal control for the system of finite number of controlled connected Markov chains (CMC). Such models come from queuing systems with many service lines and/or from the control of resources of multiple connected dams. The state of such CMC is represented as a tensor of the depth d; where d is the number of controlled chains. This tensor form is much more convenient for derivation of the dynamic programming equation. We give a tensor form for the control problems arising in the router control which is aimed to the congestion avoidance with the aid of two telecommunication lines having different properties and cost of services.

Stochastic control of light UAV at landing with the aid of bearing-only observations

This work considers the tracking of the UAV (unmanned aviation vehicle) at landing on unprepared field. Despite the advantages in UAV guidance the autonomous landing remains to be one of most serious problems. The principal difficulties are the absence of the precise UAV position measurements with respect to the landing field and the action of external atmospheric perturbations (turbulence and wind). So the control problem for UAV landing is the nonlinear stochastic one with incomplete information. The aim of the article is the development of stochastic control algorithms based on pseudomeasurement Kalman filter in the problem of the UAV autonomous landing with the aid of ground-based optical/radio radars in the case of strong wind and large initial error of the UAV entrance into the area covered by radars. The novelty of the article is the joint control-observation algorithm based on unbiased pseudomeasurement Kalman filter which provides the quadratic characteristics of the estimation errors. The later property is highly important for the UAV control based on the data fusion from INS (inertial navigation system) and the bearing observations obtained from external terrain based locators. The principal difficulty in the UAV landing control is the absence of the direct control tools at the terrain end, so the possible control can be based on the angular-range data obtained by terrain locators which must be transmitted from terrain location station to the UAV control unit. Thus the stochastic approach looks very effective in this challenging problem of the UAV landing.

Using methods of stochastic control to prevent overloads in data transmission networks

For active users in the data transmission network, a dynamic model of control of resource access and servicing rate was proposed. The problem of optimal control was solved with allowance for the cost of servicing and losses caused by rejecting customers at overload of the servicing system.

Visual navigation of the UAVs on the basis of 3D natural landmarks

This work considers the tracking of the UAV (unmanned aviation vehicle) on the basis of onboard observations of natural landmarks including azimuth and elevation angles. It is assumed that UAVs cameras are able to capture the angular position of reference points and to measure the angles of the sight line. Such measurements involve the real position of UAV in implicit form, and therefore some of nonlinear filters such as Extended Kalman filter (EKF) or others must be used in order to implement these measurements for UAV control. Recently it was shown that modified pseudomeasurement method may be used to control UAV on the basis of the observation of reference points assigned along the UAV path in advance. However, the use of such set of points needs the cumbersome recognition procedure with the huge volume of on-board memory. The natural landmarks serving as such reference points which may be determined on-line can significantly reduce the on-board memory and the computational difficulties. The principal difference of this work is the usage of the 3D reference points coordinates which permits to determine the position of the UAV more precisely and thereby to guide along the path with higher accuracy which is extremely important for successful performance of the autonomous missions. The article suggests the new RANSAC for ISOMETRY algorithm and the use of recently developed estimation and control algorithms for tracking of given reference path under external perturbation and noised angular measurements.

UAV control on the basis of bearing-only observations

This work considers the control of the UAV (unmanned aviation vehicle) on the basis of bearing-only observations including azimuth and elevation angles. During the autonomous mission UAV needs the navigation with the aid of optoelectronic camera or/and with passive radar systems which are able to capture the angular position of objects with known coordinates and to measure the angles of the sight line. Since these measurements involve the real position of UAV in implicit form some of nonlinear filters such as Extended Kalman filter (EKF) or others must be used in order to implement these measurements for UAV control. Meanwhile all these approaches to filtering give the UAV position estimation with unknown and uncontrollable bias [3], [18], which make the precise navigation rather difficult. At the same time there is well-known method of pseudomeasurements which reduces the estimation problem to the linear settings, though these methods have a bias also [7]. In this article we suggest the application of V. S. Pugachev filter [16] to the modified pseudomeasurements method without bias. On its basis the estimation and control algorithms for tracking of given reference path under external perturbation and noised angular measurements have been developed. Another problem of tracking for randomly moving object is also considered and the proposed estimation algorithm shows the good results as well.