Lyapunov Guidance Vector Fields for Unmanned Aircraft Applications

Frew, Eric W.; Lawrence, Dale; Dixon, Cory; Elston, Jack; Pisano, William J.

doi:10.1109/acc.2007.4282974

Cited by 67 publications

(46 citation statements)

References 9 publications

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“…The Lyapunov method has been used by several authors, in different ways such as for example [15,16,20,22,24,28,33,35,50]. Here we present an original Lyapunov strategy, serving the advantage to be completely smooth, very simple to apply, and with the peculiarity that the circular final manifold is with exactly minimum turning radius.…”

Section: Lyapunov-lasalle-based Stabilizationmentioning

confidence: 99%

Lyapunov and Minimum-Time Path Planning for Drones

et al. 2014

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In this paper, we study the problem of controlling an unmanned aerial vehicle (UAV) to provide a target supervision and/or to provide convoy protection to ground vehicles.We first present a control strategy based upon a Lyapunov-LaSalle stabilization method to provide supervision of a stationary target. The UAV is expected to join a pre-designed admissible circular trajectory around the target which is itself a fixed point in the space.Our strategy is presented for both HALE (High Altitude Long Endurance) and MALE (Medium Altitude Long Endurance) types UAVs. A UAV flying at a constant altitude (HALE type) is modeled as a Dubins vehicle (i.e. a planar vehicle with constrained turning radius and constant forward velocity). For a UAV that might change its altitude (MALE type), we use the general kinematic model of a rigid body evolving in R 3 . Both control strategies presented are smooth and unlike what is usually proposed in the literature these strategies asymptotically track a circular trajectory of exact minimum turning radius.We also present the time-optimal control synthesis for tracking a circle by a Dubins vehicle. This optimal strategy, although much simpler than the point-to-point time-optimal strategy obtained by P. Souères and J.-P. Laumond in the 1990s (see [45]), is very rich.Finally, we propose control strategies to provide supervision of a moving target, that are based upon the previous ones.

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Section: Lyapunov-lasalle-based Stabilizationmentioning

confidence: 99%

Lyapunov and Minimum-Time Path Planning for Drones

et al. 2014

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“…The control objective for this problem is to have a UAV orbit a moving target at a fixed, planar standoff distance. Frew et al use a Lyapunov guidance vector field (LGVF) approach to achieve the said objective [8]. Summers builds upon the work of Frew and further assumes the target and wind speeds are unknown.…”

Section: Related Workmentioning

confidence: 99%

“…While the existing literature offers methods for target tracking using continuous-time feedback control laws [6][7][8][9][10][11][12] or optimization based methods [13][14][15], these individual works make assumptions that simplify the UAV dynamics, target motion, and/or sensor visibility constraints, thereby hindering the feasibility of a real world implementation with actual hardware. This paper has detailed the design of two optimization-based control policies for vision-based target tracking, where strict trajectories must be flown by an underactuated UAV to maintain visibility and proximity to an unpredictable ground target.…”

Section: Overall Conclusion and Future Workmentioning

confidence: 99%

Vision-based target tracking with a small UAV: Optimization-based control strategies

Quintero

Hespanha

2014

Control Engineering Practice

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This paper considers the problem of a small, fixed-wing UAV equipped with a gimbaled camera autonomously tracking an unpredictable moving ground vehicle. Thus, the UAV must maintain close proximity to the ground target and simultaneously keep the target in its camera's visibility region. To achieve this objective robustly, two novel optimizationbased control strategies are developed. The first assumes an evasive target motion while the second assumes a stochastic target motion. The resulting optimal control policies have been successfully flight tested, thereby demonstrating the efficacy of both approaches in a real-world implementation and highlighting the advantages of one approach over the other.

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“…However, the analysis was performed in simulation and an evaluation in a real-world experiment has not been reported yet. The mapping of the RF environment experienced by a real flying robot is shown in (Frew et al, 2007).…”

Section: Relative Positioningmentioning

confidence: 99%

Underwater Robot Swarms: Challenges and Opportunities

2013

Handbook of Collective Robotics

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Lyapunov Guidance Vector Fields for Unmanned Aircraft Applications

Cited by 67 publications

References 9 publications

Lyapunov and Minimum-Time Path Planning for Drones

Lyapunov and Minimum-Time Path Planning for Drones

Vision-based target tracking with a small UAV: Optimization-based control strategies

Underwater Robot Swarms: Challenges and Opportunities

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