A Trajectory-Generation Framework for Time-Critical Cooperative Missions

Choe, Ronald; Cichella, Venanzio; Xargay, Enric; Hovakimyan, Naira; Trujillo, Anna C.; Kaminer, Isaac

doi:10.2514/6.2013-4582

Cited by 24 publications

(12 citation statements)

References 18 publications

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“…Finally, Fig. 9 shows the distance between the vehicles throughout the mission, which is (26) in three different cases: i) blue line-ideal path-following performance; ii) green line-the path-following error is introduced, and the time-coordination control law given in (20) is employed; and iii) red line-the path-following error is introduced, and the coordination law employed does not depend on the path-following error [i.e., (20) without the third term ]. While in case i) temporal separation is guaranteed at the trajectory generation level, when the UAVs are away from the desired position, the time-coordination algorithm must take into account the path-following error in order to ensure that the actual UAVs' positions are separated.…”

Section: Non-ideal Communication-non-ideal Path-followingmentioning

confidence: 99%

“…where denotes the commanded speed profile to be tracked by the UAV at time , while represents the speed profile generated by the trajectory-generation algorithm [26]. Hence, is limited to the physical speed constraints of the vehicle Using (5), these speed constraints result in the following inequalities:…”

Section: A Trajectory Generationmentioning

confidence: 99%

“…However, for the clarity of presentation, in this section we briefly describe the trajectory-generation and the path-following problems. For further details, the reader is referred to [26]- [28], where the authors tackle the trajectory-generation and path-following problems.…”

Section: General Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Cooperative Path Following of Multiple Multirotors Over Time-Varying Networks

Cichella

Kaminer

Dobrokhodov

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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This paper addresses the problem of time-coordination of a team of cooperating multirotor unmanned aerial vehicles that exchange information over a supporting time-varying network. A distributed control law is developed to ensure that the vehicles meet the desired temporal assignments of the mission, while flying along predefined collision-free paths, even in the presence of faulty communication networks, temporary link losses, and switching topologies. In this paper, the coordination task is solved by reaching consensus on a suitably defined coordination state. Conditions are derived under which the coordination errors converge to a neighborhood of zero. Simulation and flight test results are presented to validate the theoretical findings.Note to Practitioners-This paper presents an approach which enables a fleet of multirotor UAVs to follow a set of desired trajectories and coordinate along them, thus satisfying specific spatial and temporal assignments. The proposed solution can be employed in applications in which multiple vehicles are tasked to execute cooperative, collision-free maneuvers, and accomplish a common goal in a safely manner. An example is sequential monitoring, in which the UAVs have to visit and monitor a set of points of interest, while maintaining a desired temporal separation between each other. In this paper, we also simulate a scenario in which the vehicles, positioned in a square room, are required to exchange position with each other. It is shown that the proposed control algorithm not only ensures that the UAVs arrive at the final destinations at the same time, but also guarantees safety, i.e., the vehicles avoid collision with each other at all times.

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Section: Non-ideal Communication-non-ideal Path-followingmentioning

confidence: 99%

Section: A Trajectory Generationmentioning

confidence: 99%

See 1 more Smart Citation

Cooperative Path Following of Multiple Multirotors Over Time-Varying Networks

Cichella

Kaminer

Dobrokhodov

et al. 2015

IEEE Trans. Automat. Sci. Eng.

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“…The trajectory-generation algorithm adopted in this work is based on the methods described in [9,10]. Given the problem formulation above, this can be formally expressed as…”

Section: A Optimization-based Bézier Curve Generationmentioning

confidence: 99%

Bézier Curves for Safe Cooperative Atmospheric Missions with Multiple Heterogeneous UAS

Mehdi

Puig-Navarro

Choe

et al. 2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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Autonomous operation of UAS holds promise for greater productivity of atmospheric science missions. However, several challenges need to be overcome before such missions can be made autonomous. This paper presents a framework for safe autonomous operations of multiple vehicles, particularly suited for atmospheric science missions. The framework revolves around the use of piecewise Bézier curves for trajectory representation, which in conjunction with path-following and time-coordination algorithms, allows for safe coordinated operations of multiple vehicles.

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“…There has recently been considerable interest in using Pythagorean-hodograph (PH) curves to specify paths for swarms of unmanned aerial vehicles (UAVs) or other autonomous or remotely-operated vehicles [1,3,4,6,20,21,23,24,25,26,27,28,29,30,31,33]. A polynomial PH curve r(ξ) = (x(ξ), y(ξ), z(ξ)) incorporates a special algebraic structure [9], ensuring that the components of the hodograph (derivative) r ′ (ξ) = (x ′ (ξ), y ′ (ξ), z ′ (ξ)) satisfy a Pythagorean condition -i.e., x ′2 (ξ)+y ′2 (ξ)+z ′2 (ξ) is equal to the perfect square of a single polynomial.…”

Section: Introductionmentioning

confidence: 99%

Helical polynomial curves interpolating G 1 data with prescribed axes and pitch angles

Farouki

2017

Computer Aided Geometric Design

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A helical curve, or curve of constant slope, offers a natural flight path for an aerial vehicle with a limited climb rate to achieve an increase in altitude between prescribed initial and final states. Every polynomial helical curve is a spatial Pythagorean-hodograph (PH) curve, and the distinctive features of the PH curves have attracted growing interest in their use for Unmanned Aerial Vehicle (UAV) path planning. This study describes an exact algorithm for constructing helical PH paths, corresponding to a constant climb rate at a given speed, between initial and final positions and motion directions. The algorithm bypasses the more sophisticated algebraic representations of spatial PH curves, and instead employs a simple "lifting" scheme to generate helical PH paths from planar PH curves constructed using the complex representation. In this context, a novel scheme to construct planar quintic PH curves that interpolate given end points and tangents, with exactly prescribed arc lengths, plays a key role. It is also shown that these helical paths admit simple closed-form rotation-minimizing adapted frames. The algorithm is simple, efficient, and robust, and can accommodate helical axes of arbitrary orientation through simple rotation transformations. Its implementation is illustrated by several computed examples.

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A Trajectory-Generation Framework for Time-Critical Cooperative Missions

Cited by 24 publications

References 18 publications

Cooperative Path Following of Multiple Multirotors Over Time-Varying Networks

Cooperative Path Following of Multiple Multirotors Over Time-Varying Networks

Bézier Curves for Safe Cooperative Atmospheric Missions with Multiple Heterogeneous UAS

Helical polynomial curves interpolating G 1 data with prescribed axes and pitch angles

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