Guidance and control for cooperative search

Enns, Dale; Bugajski, Daniel; Pratt, Steve

doi:10.1109/acc.2002.1023916

Cited by 31 publications

(12 citation statements)

References 4 publications

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“…Extension to multiple vehicles typically takes the form of region assignment to avoid redundant coverage and produce multiple single-vehicle search tasks. Enns et al 3 suggest extending single-UAV sweeping to interleaved sweeps by multiple vehicles, addressing turning constraints. Beard and McLain 4 consider similar forms of sweeping while avoiding hazards and maintaining a minimum communication distance between vehicles should this be required.…”

Section: Related Workmentioning

confidence: 99%

Efficient Aerial Coverage Search in Road Networks

Dille

Singh

2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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Wide-area coverage is well suited for small unmanned aircraft, however common coverage algorithms are particularly inefficient in many common environments such as urban areas. Classical open-loop spiral or lawnmower patterns typically cannot represent large sub-regions that are unlikely to contain an object of interest that are thus wastefully covered. More general algorithms can maintain arbitrary likelihood distributions but fundamentally solve a complex continuous-space trajectory optimization problem, requiring a trade-off between look-ahead and computational complexity while providing at best asymptotic coverage guarantees. This paper considers generating global coverage patterns in the space of the coverage area, with specific focus on road networks, and mapping these to UAV actions. A new method is proposed for producing and following such patterns with dynamics-constrained vehicles, compared with existing coverage strategies, and shown to be advantageous in many realistic environments through simulations of real-world aircraft. Results suggest environment density as a metric for algorithm selection given a lack of dominance by any one strategy across all environments.

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Section: Related Workmentioning

confidence: 99%

Efficient Aerial Coverage Search in Road Networks

Dille

Singh

2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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“…Some notable exceptions, recently published, are the following. In [3] the problem of searching a wide area in a systematic fashion until the detection of a target, using multiple vehicles, is addressed. The total path length covered by the searching vehicles is minimized while exhaustively searching the area.…”

Section: Introductionmentioning

confidence: 99%

“…Typically, such operations have applications in military operations where interaction between components of large and complex systems are very common [2]. One specific application that has recently emerged due to advances in computing, wireless communications, and vehicular technologies, is the task of large scale deployment of multiple UAVs that can gather information about a dynamic threat/target environment, evade threats, and coordinate strikes against targets [3][4][5]. In this context we address the problem of a team of UAVs searching for targets in a search space.…”

Section: Introductionmentioning

confidence: 99%

Search using multiple UAVs with flight time constraints

Sujit

Ghose

2004

IEEE Trans. Aerosp. Electron. Syst.

121

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We consider a large scale system consisting of multiple unmanned aerial vehicles (UAVs) performing a search and surveillance task, based on the uncertainty map of an unknown region. The search algorithm is based on the k-shortest path algorithm that maximizes the effectiveness of the search in terms of searching through the maximum uncertainty region, given a constraint on the endurance time of the UAV and on the location of the base station from which the UAVs operate. These constraints set apart this class of problems from the usual search and surveillance problems. We compare the performance of this algorithm with a random search and a greedy strategy search. We also implement the algorithm for the case of multiple UAVs searching an unknown region. The cases of delayed and partial information are also considered. Simulation results that demonstrate the efficacy of the technique are also presented.

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“…Previous research conducted into the problem of multiple UAV agents searching a finite problem space varies from this paper in two important regards [1,2,3,4]. Firstly a high degree of direct communication is assumed in other papers, with agents discussing and agreeing upon search plans and also usually the assignment of duties to individuals.…”

Section: Introductionmentioning

confidence: 99%

Collective intelligence and bush fire spotting

Howden

Hendtlass

2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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Bush fires cause major damage each year in many areas of the world and the earlier that they can be detected the easier it is to minimize this damage. This paper describes a collective intelligence algorithm that performs localized rather than centralized control of a number of unmanned aerial vehicles (UAV) that can survey complex areas for fires, devoting attention in proportion to the user specified importance of each area. Simulation shows that not only is the algorithm able to perform this action successfully, it is also able to automatically adapt to a simulated malfunction in one of the UAVs.

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Guidance and control for cooperative search

Cited by 31 publications

References 4 publications

Efficient Aerial Coverage Search in Road Networks

Efficient Aerial Coverage Search in Road Networks

Search using multiple UAVs with flight time constraints

Collective intelligence and bush fire spotting

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