Coordinated decentralized search for a lost target in a bayesian world

Bourgault, F.; Furukawa, Tomonari; Durrant‐Whyte, Hugh

doi:10.1109/iros.2003.1250604

Cited by 126 publications

(105 citation statements)

References 4 publications

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“…From a robotic point of view the line of work most similar to our approach was performed by Furukawa and colleagues in a series of papers appeared in the last years [1], [2], [10], [19]. Therein the authors cast the search problem as a Baysian framework accounting for faulty sensors.…”

Section: Related Workmentioning

confidence: 99%

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Searching for multiple targets using Probabilistic Quadtrees

Carpin

Burch²,

Chung³

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We consider the problem of searching for an unknown number of static targets inside an assigned area. The search problem is tackled using Probabilisitic Quadtrees (PQ), a data structure we recently introduced. Probabilistic quadtrees allow for a variable resolution representation and naturally induce a search problem where the searcher needs to choose not only where to sense, but also the sensing resolution. Through a Bayesian approach accommodating faulty sensors returning both false positives and missed detections, a posterior distribution about the location of the targets is propagated during the search effort. In this paper we extend our previous findings by considering the problem of searching for an unknown number of targets. Moreover, we substitute our formerly used heuristic with an approach based on information gain and expected costs. Finally, we provide some convergence results showing that in the worst case our model provides the same results as uniform grids, thus guaranteeing that the representation we propose gracefully degrades towards a known model. Extensive simulation results substantiate the properties of the method we propose, and we also show that our variable resolution method outperforms traditional methods based on uniform resolution grids.

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Section: Related Workmentioning

confidence: 99%

“…3 we set Pr[Z t nt ] = 1 and Pr[Z t nt |X n = 1] = 1, so that the posterior about node n is not altered after sensing a different node 2 . Note that this is consistent with the independence assumption we introduced, but is different from the Type1 PQ structure where all nodes were correlated with each other.…”

Section: Bayesian Updatesmentioning

confidence: 99%

Searching for multiple targets using Probabilistic Quadtrees

Carpin

Burch²,

Chung³

2011

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…And most of them is associated with the first kind of search such as lane base search, random search, grid based search [16]. Their purpose is to reduce the uncertainty about the target existence probability in its current cell and to avoid covering the same search area.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Framework for Probabilistic Search with Multi-UAVs

Qin¹,

Li²

2017

The 5th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2017)

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Abstract. This paper focuses on the task of searching a stationary target using a team of multiple unmanned aerial vehicles (UAV) with limited communication ranges and sensing capabilities. It is based on a probabilistic representation model of the search environment, in which each UAV keeps a probability map on the presence of the target for each cell. The UAVs make observations and update the map for multiple rounds according to the Bayesian rule. In this process, each UAV can exchange data with its neighbors who are in its communication range. The decision of finding the target can be made once its probability of presence in any cell reaches a threshold. In this work, we design an efficient framework to decide the movement of each UAV at each time step, and propose a novel data fusion strategy among UAVs. The simulation result shows that our framework achieves improvements on both the success rate and the time taken to complete the searching task. We also explore the impact of different parameters, like the sensor model, moving mobility model of each UAV, and the data fusion strategies associated with the moving patterns. The simulation results show that the correct rate on target identification can vary greatly under different parameter settings, indicating the importance of the selection of UAV searching components such as the sensor model and the mobility model.

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“…The goal is to support fielded missions in the spirit of Murphy's work ͑Burke & Murphy, 2004;Casper & Murphy, 2003͒, but to focus on different hardware and operator interface designs in an effort to complement and extend existing designs. Higher levels of autonomy, which can help reduce the number of humans required to perform a task, include pathgeneration and path-following algorithms, and lower levels include attitude and altitude stabilization ͑Quigley, Goodrich & Beard, 2004͒. In the WiSAR domain, literature related to aerial search is particularly relevant ͑Koopman, 1980; Bourgault, Furukawa & Durrant-Whyte, 2003͒. Recent work includes the evaluation of three heuristic algorithms for searching an environment characterized by 1 This assumes a nominal 3 km/h walking pace for the average missing person.…”

Section: ͒mentioning

confidence: 99%

Supporting wilderness search and rescue using a camera‐equipped mini UAV

Goodrich

Morse

Gerhardt

et al. 2007

Journal of Field Robotics

307

163

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Wilderness Search and Rescue ͑WiSAR͒ entails searching over large regions in often rugged remote areas. Because of the large regions and potentially limited mobility of ground searchers, WiSAR is an ideal application for using small ͑human-packable͒ unmanned aerial vehicles ͑UAVs͒ to provide aerial imagery of the search region. This paper presents a brief analysis of the WiSAR problem with emphasis on practical aspects of visual-based aerial search. As part of this analysis, we present and analyze a generalized contour search algorithm, and relate this search to existing coverage searches. Extending beyond laboratory analysis, lessons from field trials with search and rescue personnel indicated the immediate need to improve two aspects of UAV-enabled search: How video information is presented to searchers and how UAV technology is integrated into existing WiSAR teams. In response to the first need, three computer vision algorithms for improving video display presentation are compared; results indicate that constructing temporally localized image mosaics is more useful than stabilizing video imagery. In response to the second need, a goal-directed task analysis of the WiSAR domain was conducted and combined with field observations to identify operational paradigms and field tactics for coordinating the UAV operator, the payload operator, the mission manager, and ground searchers.

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Coordinated decentralized search for a lost target in a bayesian world

Cited by 126 publications

References 4 publications

Searching for multiple targets using Probabilistic Quadtrees

Searching for multiple targets using Probabilistic Quadtrees

An Efficient Framework for Probabilistic Search with Multi-UAVs

Supporting wilderness search and rescue using a camera‐equipped mini UAV

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