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Author: Mohsen Majnoon Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Control of unmanned aerial vehicles is a very active topic in research with lots of applications ranging from civilian to military. To control a UAV, its attitude is often controlled using gyroscopes, but to control its position, inertial sensors together with GPS are often used. However, obtaining accurate current position is difficult using inertial sensors because of the integration drift. GPS on the other hand is not functional in indoor applications since it cannot connect to GPS satellites. Since vision has been proved to be an inexpensive and consistent source of relative position information, vision-based control is getting more popular in UAVs recently, but then again, using vision in outdoor applications is challenging as the target can move fast and out of the vision sensor field of view. So, in order to keep the target inside the field of view, two algorithms are being developed and tested via simulation in this research. Using pan/tilt/zoom cameras or multi camera systems, the target is guaranteed to stay in vision system field of view and hence, the vision based pose estimation can provide the control system with proper relative position. Two case studies - vision-based mobile-target tracking of a quadrotor using a multi-camera vision sensor and vision-based mobile-target tracking of a tilting rotor aircraft equipped with a zooming camera - are presented in this research to show the applicability of these methods in UAV control.
Author: Mohsen Majnoon Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Control of unmanned aerial vehicles is a very active topic in research with lots of applications ranging from civilian to military. To control a UAV, its attitude is often controlled using gyroscopes, but to control its position, inertial sensors together with GPS are often used. However, obtaining accurate current position is difficult using inertial sensors because of the integration drift. GPS on the other hand is not functional in indoor applications since it cannot connect to GPS satellites. Since vision has been proved to be an inexpensive and consistent source of relative position information, vision-based control is getting more popular in UAVs recently, but then again, using vision in outdoor applications is challenging as the target can move fast and out of the vision sensor field of view. So, in order to keep the target inside the field of view, two algorithms are being developed and tested via simulation in this research. Using pan/tilt/zoom cameras or multi camera systems, the target is guaranteed to stay in vision system field of view and hence, the vision based pose estimation can provide the control system with proper relative position. Two case studies - vision-based mobile-target tracking of a quadrotor using a multi-camera vision sensor and vision-based mobile-target tracking of a tilting rotor aircraft equipped with a zooming camera - are presented in this research to show the applicability of these methods in UAV control.
Author: Steven Andrew Provencio Quintero Publisher: ISBN: 9781321349955 Category : Languages : en Pages : 215
Book Description
Small unmanned aerial vehicles (UAVs) are relatively inexpensive mobile sensing platforms capable of reliably and autonomously performing numerous tasks, including mapping, search and rescue, surveillance and tracking, and real-time monitoring. The general problem of interest that we address is that of using small, fixed-wing UAVs to perform vision-based target tracking, which entails that one or more camera-equipped UAVs is responsible for autonomously tracking a moving ground target. In the single-UAV setting, the underactuated UAV must maintain proximity and visibility of an unpredictable ground target while having a limited sensing region. We provide solutions from two different vantage points. The first regards the problem as a two-player zero-sum game and the second as a stochastic optimal control problem. The resulting control policies have been successfully field-tested, thereby verifying the efficacy of both approaches while highlighting the advantages of one approach over the other.
Author: Panagiotis Theodorakopoulos Publisher: ISBN: Category : Languages : en Pages : 150
Book Description
Most applications of Unmanned Aerial Vehicles are related to events that occur on the ground. In particular, ground target tracking, be the target static, slowly moving or maneuvering at high speeds, is an essential task for UAVs. The overall objective of this thesis is to provide methods to endow a drone to autonomously track a moving ground target, under the following conditions: - A fixed wing UAV equipped with a monocular camera. - Presence of obstacles that hinder ground visibility. - No Fly Zones that limit the airspace. - Restrictions on the field of view of the observing sensor (a camera) - Various target dynamics and behavior: the target may be either moving on an open field or on a road network, and also has dynamic constraints (e.g. if it is a car). It can be neutral or evasive: in the latter case, it can exploit the presence of obstacles, denoted as "shadows" to avoid being tracked by the UAV, making the problem akin to a "hide and seek" game. The thesis proposes three approaches to tackle this problem: - A control based navigation method, - An adversarial predictive method, - And a discrete game theoretic approach. Results obtained in realistic simulations and with an actual UAV are presented to evaluate and compare the pros and cons of each approach. Extensions to the multi-drone case are also considered.
Author: Christopher Sconyers Publisher: ISBN: Category : Drone aircraft Languages : en Pages :
Book Description
Research in the areas of target detection, tracking, and geo-location is most important for enabling an unmanned aerial vehicle (UAV) platform to autonomously execute a mission or task without the need for a pilot or operator. Small-class UAVs and video camera sensors complemented with "soft sensors" realized only in software as a combination of a priori knowledge and sensor measurements are called upon to replace the cumbersome precision sensors on-board a large class UAV. The objective of this research is to develop a geo-location solution for use on-board multiple UAVs with mounted video camera sensors only to accurately geo-locate and track a target. This research introduces an estimation solution that combines the power of the particle filter with the utility of the video sensor as a general solution for passive target geo-location on-board multiple UAVs. The particle filter is taken advantage of, with its ability to use all of the available information about the system model, system uncertainty, and the sensor uncertainty to approximate the statistical likelihood of the target state. The geo-location particle filter is tested online and in real-time in a simulation environment involving multiple UAVs with video cameras and a maneuvering ground vehicle as a target. Simulation results show the geo-location particle filter estimates the target location with a high accuracy, the addition of UAVs or particles to the system improves the location estimation accuracy with minimal addition of processing time, and UAV control and trajectory generation algorithms restrict each UAV to a desired range to minimize error.
Author: Publisher: ISBN: Category : Cameras Languages : en Pages : 41
Book Description
In the tracking of a moving ground target by small unmanned air vehicle (UAV) via camera vision, the target position and motion cannot be measured directly. Two different types of filters were assessed for their ability to estimate target motion, namely target velocity, directional heading on flat ground and distance from the UAV to target. The first filter is a nonlinear deterministic filter with stability guarantee. The second filter is based on nonlinear Kalman Filter technique. The application and performance of these two filters are presented, for simulated vision based target tracking.
Author: Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
We are developing a real-time-multiple-target-tracking system using a wide-field-of-view (WFOV) camera. The high resolution WFOV camera was conceived as part of the Strategic Defense initiative Research at Lawrence Livermore National Laboratory. The camera system consists of a lens made of concentric solid blocks of index matching glasses, CCDs arrayed on the focal plane, and a custom VLSI image processor to extract the targets. References 1 and 2 describe the basic design of the WFOV camera and the prototype system that we have constructed. In this paper, we will briefly review the existing prototype system, the on-going effort to cover the full field of view using digital CCD cameras, the production of custom VLSI chips developed to extract centroids in real time, and the implementation of transputers to run the tracking algorithms.
Author: Mousumi Ahmed Publisher: ISBN: Category : Aerospace engineering Languages : en Pages :
Book Description
Designing the control technique for nonlinear dynamic systems is a signi cant challenge. Approaches to designing a nonlinear controller are studied and an extensive study on backstepping based technique is performed in this research with the purpose of tracking a moving target autonomously. Our main motivation is to explore the controller for cooperative and coordinating unmanned vehicles in a target tracking application. To start with, a general theoretical framework for target tracking is studied and a controller in three dimensional environment for a single UAV is designed. This research is primarily focused on nding a generalized method which can be applied to track almost any reference trajectory. The backstepping technique is employed to derive the controller for a simpli ed UAV kinematic model. This controller can compute three autopilot modes i.e. velocity, ground heading (or course angle), and ight path angle for tracking the unmanned vehicle. Numerical implementation is performed in MATLAB with the assumption of having perfect and full state information of the target to investigate the accuracy of the proposed controller. This controller is then frozen for the multi-vehicle problem. Distributed or decentralized cooperative control is discussed in the context of multi-agent systems. A consensus based cooperative control is studied; such consensus based control problem can be viewed from the algebraic graph theory concepts. The communication structure between the UAVs is represented by the dynamic graph where UAVs are represented by the nodes and the communication links are represented by the edges. The previously designed controller is augmented to account for the group to obtain consensus based on their communication. A theoretical development of the controller for the cooperative group of UAVs is presented and the simulation results for di erent communication topologies are shown. This research also investigates the cases where the communication topology switches to a di erent topology over particular time instants. Lyapunov analysis is performed to show stability in all cases. Another important aspect of this dissertation research is to implement the controller for the case, where perfect or full state information is not available. This necessitates the design of an estimator to estimate the system state. A nonlinear estimator, Extended Kalman Filter (EKF) is rst developed for target tracking with a single UAV. The uncertainties involved with the measurement model and dynamics model are considered as zero mean Gaussian noises with some known covariances. The measurements of the full state of the target are not available and only the range, elevation, and azimuth angle are available from an onboard seeker sensor. A separate EKF is designed to estimate the UAV's own state where the state measurement is available through on-board sensors. The controller computes the three control commands based on the estimated states of target and its own states. Estimation based control laws is also implemented for colored noise measurement uncertainties, and the controller performance is shown with the simulation results. The estimation based control approach is then extended for the cooperative target tracking case. The target information is available to the network and a separate estimator is used to estimate target states. All of the UAVs in the network apply the same control law and the only di erence is that each UAV updates the commands according to their connection. The simulation is performed for both cases of xed and time varying communication topology. Monte Carlo simulation is also performed with di erent sample noises to investigate the performance of the estimator. The proposed technique is shown to be simple and robust to noisy environments.
Author: Publisher: ISBN: Category : Kalman filtering Languages : en Pages : 141
Book Description
Currently, small unmanned aerial vehicles developed by NPS have been able to locate and track stationary and moving targets on the ground. New methods of continuous target tracking are always being developed to improve speed and accuracy, ultimately aiding the user of the system. This thesis describes one such method, utilizing an open loop filter as well as an external correction source: Perspective View Nascent Technologies (PVNT). While the PVNT correction can theoretically improve the accuracy from 20-30 meters to 1-2 meters, it does have a disadvantage in that the target position updates are delayed anywhere from 1-10 seconds. In order to account for the delay, an asynchronous filter is used to update the target position data given the external position correction from PVNT. Two cases have been tested including the general filter and one that utilizes a road model in the calculations. While an earlier thesis developed the basic simulation for the system, this thesis discusses improvements and corrections to the simulation model as well as the necessary steps for real-time implementation.
Author: Mohsen Majnoon
Author: Mohsen Majnoon
Author: Steven Andrew Provencio Quintero
Author: Panagiotis Theodorakopoulos
Author: Mingfeng Zhang
Author: Christopher Sconyers
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Author: Mousumi Ahmed
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