Non-hierarchical UAV formation control for surveillance tasks

Walle, Dirk van der; Fi̇dan, Barış; Sutton, Andrew J.; Yu, Changbin; Anderson, Brian D. O.

doi:10.1109/acc.2008.4586587

Cited by 43 publications

(25 citation statements)

References 11 publications

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“…Furthermore, the speeds of the three UAVs are assumed to be constant and equal, which is typical for a fleet of three plane-type UAVs, i.e. v i (t) =v, [6], [7], draws a distinction line between the current paper and the other ones that deal with similar problems [3], [4], [8], and [9].…”

Section: A Agent Modelsmentioning

confidence: 99%

Close target reconnaissance using autonomous UAV formations

Shames

Fi̇dan

Anderson

2008

2008 47th IEEE Conference on Decision and Control

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In this paper the problem of close target reconnaissance by a formation of 3 unmanned aerial vehicles (UAVs) is considered. The overall close target reconnaissance (CTR) involves subtasks of avoiding obstacles or no-fly-zones, avoiding inter-agent collisions, reaching a close vicinity of a specified target position, and forming an equilateral triangular formation around the target. The UAVs performing the task fly at constant speeds. A decentralized control scheme is developed for this overall task considering unidirectional sensing/control architecture. Relevant analysis and simulation test results are provided.

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Section: A Agent Modelsmentioning

confidence: 99%

Close target reconnaissance using autonomous UAV formations

Shames

Fi̇dan

Anderson

2008

2008 47th IEEE Conference on Decision and Control

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“…As an active research field of control, formation control has been well studied. Variety of tasks are assigned for teams of autonomous robots within formations, for instance, target tracking [7], surveillance [8] and cooperative manipulation [9] and so forth. As an example, a task of tracking moving targets presented in [7] is chosen and solved by using Lyapunov guidance vector field approach.…”

Section: Coordinated Standoff Trackingmentioning

confidence: 99%

A virtual framework of robotic SWARM testbed

Hou

Summers

2009

2009 Chinese Control and Decision Conference

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A robotic SWARM testbed is presented in a companion paper, as complementarities, we present a virtual framework of this robotic SWARM testbed for the same objective. The similarities of these two approaches are both trying to implement formation control algorithms onto robots and generate certain criteria to adapt the algorithms to the dynamics of the agents. Comparing to the physical testbed, this virtual framework has a lot of benefits. For instance, the agents can be either any kind of commercialized robots or even the ones not existing that we can design by our own, which means it could have some advanced features, such as self localization capability and global knowledge of their circumstances. These are demonstrated using an example of coordinated standoff tracking task. Furthermore, a virtual framework enables provision of co-simulation capability which is critical for SWARM testbed

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“…In [13], a two-stage optimal coalition formation algorithm is proposed that assigns appropriate number of UAVs that satisfy the desired requirements for carrying out munitions in support of battlefield operations. In [14], motion and formation control of a team of three UAVs are considered for a particular surveillance task, in which a decentralised control scheme is designed and analysed based on a non-hierarchical sensing/control structure.…”

Section: Introductionmentioning

confidence: 99%

Cooperative actuator fault accommodation in formation flight of unmanned vehicles using absolute measurements

Azizi

Khorasani

2012

IET Control Theory Appl.

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In this study, the problem of cooperative fault accommodation in formation flight of unmanned vehicles represented by linear time-invariant models that are subject to loss-of-effectiveness actuator faults is investigated through a hierarchical framework. Three hierarchical levels are envisaged, namely a low-level fault recovery (LLFR), a formation-level fault recovery and a high-level supervisor. In the LLFR module, a recovery controller is designed by using an estimate of the actuator fault. A performance monitoring module is then introduced at the high-level to identify a 'partially low-level recovered' vehicle because of inaccuracy in the fault severity estimate that results in violating the 'error specification' of the formation mission. The high-level supervisor then activates the formation-level fault recovery module to compensate for the resulting performance degradations of the partially low-level (LL) recovered vehicle at the expense of other healthy vehicles. The fault is accommodated by reconfiguring the formation structure through the novel notion of the weighted absolute measurement formation digraph, activating a robust controller for the partially LL recovered vehicle, and imposing a constraint on the desired input signals. Numerical simulations for a formation flight of five satellites in the planetary orbital environment are presented to confirm the validity and effectiveness of the proposed analytical work.

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Non-hierarchical UAV formation control for surveillance tasks

Cited by 43 publications

References 11 publications

Close target reconnaissance using autonomous UAV formations

Close target reconnaissance using autonomous UAV formations

A virtual framework of robotic SWARM testbed

Cooperative actuator fault accommodation in formation flight of unmanned vehicles using absolute measurements

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