Optimal Search for a Lost Target in a Bayesian World

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

doi:10.1007/10991459_21

Cited by 135 publications

(143 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%

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%

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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“…Advances in computer technology provided the possibility of developing more feasible tools, Search and Localization Tactical decision aid (SALT) was a prototype air-antisubmarine search planner system for real-time use aboard aircraft (Stone, 1989). The problem of searching for a lost target at sea by a single autonomous sensor platform (UAV) is discussed by (Bourgault et al, 2003a). In this paper the target may be static or mobile but not evading.…”

Section: Related Workmentioning

confidence: 99%

UAV Path Planning in Search Operations

Kamrani¹,

Ayani²

2009

Aerial Vehicles

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“…The use of automated task planning for SaT missions has received little attention so far, while probabilistic approaches based on Recursive Bayesian Estimation (RBE) have been explored in more depth. Efficient solutions to SaT have been proposed under restrictive simplifying assumptions such as the search area being small (one/two square km), the temporal horizon being short (a few minutes) and the target's motion model being simple (e.g., targets being stationary or in Markovian motion) (Stone 1975;Bourgault et al 2006;Furukawa et al 2006;Lavis and Furukawa 2008;Lin and Goodrich 2014). Although this purely probabilistic approach is successful for small-scale and simple SaT problems, it fails in the face of all the constraints that characterise realworld SaT operations because it becomes computationally too expensive.…”

Section: Introductionmentioning

confidence: 99%

Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

2015

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It is particularly challenging to devise techniques for underpinning the behaviour of autonomous vehicles in surveillance missions as these vehicles operate in uncertain and unpredictable environments where they must cope with little stability and tight deadlines in spite of their restricted resources. State-of-the-art techniques typically use probabilistic algorithms that suffer a high computational cost in complex real-world scenarios. To overcome these limitations, we propose a hybrid approach that combines the probabilistic reasoning based on the target motion model offered by Monte Carlo simulation with long-term strategic capabilities provided by automated task planning. We demonstrate our approach by focusing on one particular surveillance mission, search-and-tracking, and by using two different vehicles, a fixed-wing UAV deployed in simulation and the "Parrot AR.Drone2.0" quadcopter deployed in a physical environment. Our experimental results show that our unique way of integrating probabilistic and deterministic reasoning pays off when we tackle realistic missions. Electronic supplementary materialThe online version of this article

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Optimal Search for a Lost Target in a Bayesian World

Cited by 135 publications

References 4 publications

Searching for multiple targets using Probabilistic Quadtrees

Searching for multiple targets using Probabilistic Quadtrees

UAV Path Planning in Search Operations

Combining temporal planning with probabilistic reasoning for autonomous surveillance missions

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