UAV Coordination for Autonomous Target Tracking

Wise, R.; Rysdyk, Rolf

doi:10.2514/6.2006-6453

Cited by 102 publications

(56 citation statements)

References 7 publications

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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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“…Consider the particle model (1). The turn-rate control, u k , for a spatially uniform, time-invariant flowfield is (see (10))…”

Section: Iva Turn-rate Constraint In a Uniform Time-invariant Flowmentioning

confidence: 99%

“…14 A particular application highlighted is the coordinated encirclement of a maneuvering target. 5,6,10,15 One obstacle limiting the performance of existing control algorithms is the presence of an external flowfield that may represent a significant fraction of a vehicle's velocity. Some existing algorithms support operations in spatially uniform flowfields.…”

Section: Introductionmentioning

confidence: 99%

“…The benefits of cooperating vehicles has prompted much research in developing stable, decentralized control algorithms for a variety of autonomous platforms and configurations. [1][2][3][4][5][6][7][8][9][10] These algorithms have been adapted for use in numerous applications including aerobiological sampling, 11 gathering in situ measurements of severe storms, 12 path planning of autonomous underwater gliders, 13 and environmental boundary tracking. 14 A particular application highlighted is the coordinated encirclement of a maneuvering target.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Vehicle Coordination in an Unknown Flowfield

Peterson

Paley

2010

AIAA Guidance, Navigation, and Control Conference

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Cooperative control of autonomous vehicles is a robust approach for many spatiotemporal applications, including area surveillance, target tracking and environmental sampling. Prior research has generated stable control algorithms that promote collaboration of autonomous vehicles. A persistent difficulty in creating control algorithms is accounting for effects due to flowfields such as winds or tides. In this paper we propose decentralized control algorithms that stabilize a group of autonomous vehicles to a circular formation in either an unknown, spatially uniform flowfield or an unknown, spatially uniform flowfield whose direction rotates uniformly in time. The center of the formation can be prescribed or arbitrary. Prescribing the center formation allows us to apply these algorithms to a targettracking problem. We also show how a bounded turning-rate constraint, which is present in many autonomous vehicles, can be incorporated into control algorithms for a known flowfield. The theoretical results are supported by application examples that illustrate multiple vehicles tracking an accelerating target in a circular formation.

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“…To simplify optimization formulation and decrease computation burden, a new manipulation using inner product of position vectors of the UAVs with respect to the target position is proposed instead of using the relative phase angle difference which were used in most of the existing works. 2,4,8 To accomplish the safety under realistic situations, inequality constraints are also considered for collision avoidance between UAV members and control input saturations in the model predictive control scheme. Lastly, simulations with a pair of UAVs are done using realistic car trajectory data in urban traffic in the UK to verify the feasibility and benefits of the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Model Predictive Coordinated Standoff Tracking of Moving Ground Vehicle

Kim

Tsourdos

2011

AIAA Guidance, Navigation, and Control Conference

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This paper proposes a nonlinear model predictive control framework for coordinated standoff tracking by a pair of unmanned aerial vehicles. The benefit of this approach is to get optimal performance compared to using a decoupled controller structure: heading control for standoff-distance keeping and speed control for phase-keeping. The overall controller structure is fully decentralized in a fact that each unmanned aerial vehicle optimizes its controller based solely on the future propagation of the pair vehicle states and the target estimates received via communication. This paper uses an acceleration model for a sophisticated and realistic target dynamics, which can consider more reasonable system noise covariance matrix reflecting the target's motion characteristics. To simplify optimization formulation and decrease computation burden, a new manipulation using inner product of position vectors of the unmanned aerial vehicles with respect to the target position is proposed for antipodal tracking instead of using the relative phase angle difference. To consider a more realistic situation, inequality constraints are considered for collision avoidance between unmanned aerial vehicles and control input saturations using penalty functions in the model predictive control scheme. Simulations with a pair of unmanned aerial vehicles are done using a realistic car trajectory data in a urban environment in the United Kingdom to verify the feasibility and benefit of the proposed approach with comparing to a Lyapunov vector field guidance.

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UAV Coordination for Autonomous Target Tracking

Cited by 102 publications

References 7 publications

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

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

Multi-Vehicle Coordination in an Unknown Flowfield

Nonlinear Model Predictive Coordinated Standoff Tracking of Moving Ground Vehicle

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