Vision-Based Tracking and Motion Estimation for Moving Targets Using Unmanned Air Vehicles

Dobrokhodov, Vladimir; Kaminer, Isaac; Jones, Kevin D.; Ghabcheloo, Reza

doi:10.2514/1.33206

Cited by 67 publications

(56 citation statements)

References 22 publications

(56 reference statements)

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“…Several different approaches to solving the dual control problem have been proposed to make the problem more tractable. Geometric considerations on estimation have been removed by focusing simply on "camera-on-target" time or homing, and solved with several methods, such as direct collocation [28,29], neural networks [30], or heuristics [31][32][33]. Others have reduced the problem to one physical dimension, usually mandating a constant closure to a target and controlling in an orthogonal dimension with pure localization [34,35].…”

Section: The Dual Control Problemmentioning

confidence: 99%

Stochastic Real-Time Optimal Control for Bearing-Only Trajectory Planning

Ross

Cobb²,

Baker³

2014

International Journal of Micro Air Vehicles

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A method is presented to simultaneously solve the optimal control problem and the optimal estimation problem for a bearing-only sensor. For bearing-only systems that require a minimum level of certainty in position relative to a source for mission accomplishment, some amount of maneuver is required to measure range. Traditional methods of trajectory optimization and optimal estimation minimize an information metric. This paper proposes constraining the final value of the information states with known time propagation dynamics relative to a given trajectory which allows for attainment of the required level of information with minimal deviation from a general performance index that can be tailored to a specific vehicle. The proposed method does not suffer from compression of the information metric into a scalar, and provides a route that will attain a particular target estimate quality while maneuvering to a desired relative point or set. An algorithm is created to apply the method in real-time, iteratively estimating target position with an Unscented Kalman Filter and updating the trajectory with an efficient pseudospectral method. Methods and tools required for hardware implementation are presented that apply to any real-time optimal control (RTOC) system. The algorithm is validated with both simulation and flight test, autonomously landing a quadrotor on a wire. INTRODUCTIONBearing-only tracking is a classical navigation problem. Many real-world systems depend on angleonly sensors for target state estimation, such as submarines using only passive sonar, high-speed antiradiation missiles (HARM), robots, and unmanned aerial vehicles (UAVs) using images from an optical sensor. The inability to sense range with each measurement, combined with the inherent nonlinearity of the problem make estimation of a target's location and motion problematic. Moving the sensor orthogonal to the line-of-sight (LOS) generates observability for range estimation, and many researchers have pursued optimal methods for trajectory planning in this context [1][2][3][4][5][6]. Optimal trajectory planning of any sort, however, is notoriously difficult to implement for on-line systems (excepting those simple enough to have a closed-form analytical feedback solution). Notably, recent advances in direct optimization through techniques such as pseudospectral methods (PSM) [7][8][9] have increased the efficiency and stability of obtaining a numeric optimal solution to the point where realtime optimal control (RTOC) of systems with moderate speed dynamics is now possible. This paper proposes a new method of approaching the bearing-only trajectory planning problem that enables simultaneous consideration of both the optimal control problem and a system's prescribed final estimation requirements, overcoming the typical limitations of previous approaches which address each requirement separately. The trajectory planning goal is to provide an optimal trajectory for arrival at a point, or set of points, potentially offset from a relative targe...

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Section: The Dual Control Problemmentioning

confidence: 99%

Stochastic Real-Time Optimal Control for Bearing-Only Trajectory Planning

Ross

Cobb²,

Baker³

2014

International Journal of Micro Air Vehicles

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“…Koch [9] used random matrices to describe objects' states for air-ground target tracking. Dobrokhodov et al [10] proposed an object tracking system for unmanned small aircrafts. Zhu and Wang [11] used a bang-bang heading rate controller to achieve circular tracking around the target.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Building Tracking from UAVs Based on Image Manifold Learning

Zhang¹,

Ren²

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Abstract-Fast and accurate visual tracking of ground buildings can provide unmanned aerial vehicles (UAVs) with rich perceptual information, which is very important for target recognition, navigation and system control. However, when an UAV moves fast, both background and buildings in visual scenes change relatively and rapidly. Consequently, there are no constant features for objects' appearance, which poses great challenges for visual tracking of buildings. In this paper, we first build an image manifold of buildings, which can encode the continuous variation of appearance. We then propose an efficient approach to learn this manifold and obtain more robust feature extraction results. By using a simple tracking framework, we successfully apply the extracted lowdimensional features to real-time building tracking. Experimental results demonstrate the effectiveness of the proposed method.

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“…A good survey of target state estimation is a series of papers by X. Rong Li and Vesselin P. Jilkov [11,12,13]. And the estimation methods based on visual measurement can be found in [14,15].…”

Section: (4) Target State Estimatormentioning

confidence: 99%

A motion controller for a pan-tilt camera on an autonomous helicopter

Deng

Zhao

Hou

2010

2010 11th International Conference on Control Automation Robotics &Amp; Vision

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In this paper, a motion controller for a pan-tilt camera mounted on an autonomous helicopter is presented. The motion planner is designed according to the kinematics of the pan-tilt camera. However, the solution of the inverse kinematics of the pan-tilt camera is not unique. To deal with this situation, a decision maker is designed. The decision maker makes use of a cost function to decide which solution should be chose. And the error signal is defined as the difference between pantilt joint angles and the desired joint angles. The dynamics of the error system is derived, and proportional controllers are designed according to the error dynamics to control the pan and tilt joints respectively. This system is validated in a virtual reality environment, and the simulation results show that this system can track the target successfully.

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Vision-Based Tracking and Motion Estimation for Moving Targets Using Unmanned Air Vehicles

Cited by 67 publications

References 22 publications

Stochastic Real-Time Optimal Control for Bearing-Only Trajectory Planning

Stochastic Real-Time Optimal Control for Bearing-Only Trajectory Planning

Dynamic Building Tracking from UAVs Based on Image Manifold Learning

A motion controller for a pan-tilt camera on an autonomous helicopter

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