Utility-based adaptive path planning for subsea search

Yetkin, Harun; Lutz, Collin C.; Stilwell, Daniel J.

doi:10.23919/oceans.2015.7404367

Cited by 7 publications

(13 citation statements)

References 15 publications

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“…A few studies in the literature aim to evaluate the benefit of reducing the uncertainty in the environment. In our prior work [1], we show that inaccurate estimate of sensor performance can lead to inaccurate estimate of search performance. For example, when the presumed probability of detection is higher than the actual probability of detection, the probability that all objects have been found during a search mission is exaggerated, and the search mission might be terminated too early.…”

Section: B Effect Of Environment Informationmentioning

confidence: 88%

Acquiring environmental information yields better anticipated search performance

Yetkin

Lutz

Stilwell

2016

OCEANS 2016 MTS/IEEE Monterey

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We address the problem where a mobile search agent seeks to find an unknown number of stationary objects distributed in a bounded search domain, and the search mission is subject to time/distance constraint. Our work accounts for false positives, false negatives and environmental uncertainty. We consider the case that the performance of a search sensor is dependent on the environment (e.g., clutter density), and therefore sensor performance is better in some locations than in others. We specifically consider applications where environmental information can be acquired either by a separate vehicle or by the same vehicle that performs the search task. Our main contribution in this study is to formally derive a decision-theoretic cost function to compute the locations where the environmental information should be acquired. For the cases where computing the optimal locations to sample the environment is computationally expensive, we offer an approximation approach that yields provable near-optimal paths. We show that our decision-theoretic cost function outperforms the information-maximization approach, which is often employed in similar applications.

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Section: B Effect Of Environment Informationmentioning

confidence: 88%

Acquiring environmental information yields better anticipated search performance

Yetkin

Lutz

Stilwell

2016

OCEANS 2016 MTS/IEEE Monterey

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“…3 shows the search paths for both algorithms for a mission length of 50 ( Fig. 3a-b) and for a mission length 1 We note that the search area in Fig. 2.…”

Section: A Comparisons Of Exact and Approximate Solutionsmentioning

confidence: 99%

“…Both α j and D j are assumed to vary as functions of the environment type b j . We refer the reader to [2] for more details on the observation model in (1). After acquiring the measurements z and y, we apply Bayes' rule to update our belief on the number of targets and on the environmental conditions.…”

Section: Problem Formulation a Preliminariesmentioning

confidence: 99%

“…The goal is to find an unknown number of stationary targets distributed in a bounded area in finite time and/or finite distance. Our contributions in this paper build upon prior work reported in [1], [2] in which we developed decision-theoretic cost functions that can be used to evaluate candidate search paths. We assume herein, as we did in prior work, that the search sensor produces false positive measurements of targets that do not exist, and that the performance of the sensor is affected by spatial variation of the environment.…”

Section: Introductionmentioning

confidence: 99%

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Towards real-time search planning in subsea environments

McMahon

Yetkin

Wolek

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We address the challenge of computing search paths in real-time for subsea applications where the goal is to locate an unknown number of targets on the seafloor. Our approach maximizes a formal definition of search effectiveness given finite search effort. We account for false positive measurements and variation in the performance of the search sensor due to geographic variation of the seafloor. We compare near-optimal search paths that can be computed in real-time with optimal search paths for which real-time computation is infeasible. We show how sonar data acquired for locating targets at a specific location can also be used to characterize the performance of the search sonar at that location. Our approach is illustrated with numerical experiments where search paths are planned using sonar data previously acquired from Boston Harbor.

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“…If targets are known to be stationary (or static), one could divide the search space into cells on a grid and track hypotheses in each cell [ 7 , 22 , 26 ]. Or, one can track the number of targets contained within each cell [ 27 , 28 ]. Since our work assumes stationary targets, there is a grid of cells, but the approach collects multiple cells into regions for faster evaluation of coverage plans by planning at the coarser granularity of searching regions instead of cells, requiring fewer actions for a full duration coverage plan at the cost of generating suboptimal plans.…”

Section: Related Workmentioning

confidence: 99%

Multipass Target Search in Natural Environments

Kuhlman

Otte

Sofge

et al. 2017

Sensors

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Consider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue operations. Entropy can be used to quantify the generation and resolution of uncertainty. When searching for targets, maximizing mutual information of future sensor observations will minimize expected target location uncertainty by minimizing the entropy of the future estimate. Motion planning for multi-target autonomous search requires planning over an area with an imperfect sensor and may require multiple passes, which is hindered by the submodularity property of mutual information. Further, mission duration constraints must be handled accordingly, requiring consideration of the vehicle’s dynamics to generate feasible trajectories and must plan trajectories spanning the entire mission duration, something which most information gathering algorithms are incapable of doing. If unanticipated changes occur in an uncertain environment, new plans must be generated quickly. In addition, planning multipass trajectories requires evaluating path dependent rewards, requiring planning in the space of all previously selected actions, compounding the problem. We present an anytime algorithm for autonomous multipass target search in natural environments. The algorithm is capable of generating long duration dynamically feasible multipass coverage plans that maximize mutual information using a variety of techniques such as ϵ-admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time.

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Utility-based adaptive path planning for subsea search

Cited by 7 publications

References 15 publications

Acquiring environmental information yields better anticipated search performance

Acquiring environmental information yields better anticipated search performance

Towards real-time search planning in subsea environments

Multipass Target Search in Natural Environments

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