Theory and experimental results for the multiple aspect coverage problem

Bays, Matthew J.; Shende, Apoorva; Stilwell, Daniel J.; Redfield, Signe

doi:10.1016/j.oceaneng.2012.06.023

Cited by 14 publications

(15 citation statements)

References 13 publications

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“…Right: Proposed algorithm combining DBSCAN clustering and an ACO TSP algorithm. [1] travel savings for the UUV, potentially as high as 41% of the distance required by standard techniques. Additionally, the algorithm significantly reduces the number of turns required by an average of 39%.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Once the objects are partitioned into smaller clusters, each cluster is consecutively processed using a modified version of the Multiple Aspect Coverage (MAC) algorithm found in [1]- [2]. We will now provide a high-level overview of the algorithm as well as details of its current modifications.…”

Section: Brief Overview Of the Multiple Aspect Coverage Algorithmmentioning

confidence: 99%

“…An additional improvement was achieved by modifying the threshold probability calculations used by each aspect based on current MCM theory. We have modified the algorithm such that the output is now the set of swaths S. For additional information on the original algorithm, we refer to [1]- [2]. Using the MAC algorithm for choosing the swaths within each cluster, we next turn to planning the transit paths and ordering of the swaths in between each cluster.…”

Section: Brief Overview Of the Multiple Aspect Coverage Algorithmmentioning

confidence: 99%

“…We demonstrate that the DB-SCAN clustering algorithm chose to perform the partitioning has parameters that may be tuned using analytical techniques in order to incorporate unique characteristics of the underwater vehicle used for the inspection. Our work leverages the previously-developed algorithms to solve the multiple aspect coverage (MAC) problem for surveying multiple contacts simultaneously when the contacts are grouped closely together [1]- [2].…”

Section: Introductionmentioning

confidence: 99%

“…The multiple angles allow for multiple views of a contact in order to determine if it is indeed an object of interest, or merely a false contact. The algorithm discussed in [1]- [2], which we term the multiple-aspect coverage (MAC) algorithm, has been shown to be significantly more efficient than the standard, singlecontact pattern when the contacts occur in close proximity. However, when the contacts span a great distance, the algorithm often creates paths that are less efficient than using a single-contact pattern.…”

Section: Introductionmentioning

confidence: 99%

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Efficient reacquire and identify path planning over large areas

Sriraman

Bays

2014

2014 Oceans - St. John's

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Abstract-An important task in maritime search and inspection involves re-acquiring and identifying underwater objects by surveying the objects from multiple angles. Because of false contacts related to clutter on the sea floor, the objects are often detected in dramatically different densities in a given area. Previously developed methods to plan survey paths on groups of contacts led to efficient paths when the contacts occur in close proximity, but inefficient paths when the objects occur over large distances. We present a planning algorithm to generate an efficient path to survey objects from multiple angles that is independent of the density of the objects. The algorithm leverages the previously-developed algorithms for surveying objects from multiple directions, coupled with density-based spatial clustering of applications with noise (DBSCAN) clustering and ant colony optimization techniques.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Brief Overview Of the Multiple Aspect Coverage Algorithmmentioning

confidence: 99%

Section: Brief Overview Of the Multiple Aspect Coverage Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient reacquire and identify path planning over large areas

Sriraman

Bays

2014

2014 Oceans - St. John's

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The Orbiting Dubins Traveling Salesman Problem: planning inspection tours for a minehunting AUV

et al. 2020

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Persistent Schedule Evaluation and Adaptive Re-planning for Maritime Search Tasks

Bays,

Wettergren,

Shin

et al. 2024

J Intell Robot Syst

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Search operations performed by adaptive autonomous maritime vehicles have been a topic of considerable interest for many years. Such operations require carefully scheduled coordination of multiple vehicles performing search tasks across the region of interest. Due to the inherent uncertainty of the maritime environment, however, an initially planned search schedule may not be maintained if the vehicles have significant capability to adapt their tasks to match the environment they detect in real time. We propose a multi-vehicle adaptive algorithm for dynamic evaluation and elastic re-planning of variable-length tasks commonly found in the maritime environments. In adaptive evaluation and re-planning problems, a set of tasks are initially planned for execution by adaptive, autonomous search vehicles. Tasks are allocated to search vehicles under a pre-defined schedule based on prior knowledge and desired outcome. Because of the vehicles’ autonomy and reactivity to in situ conditions such as environment or target pose, the precise duration and actions required by each task are unknown a priori. We develop a hidden Markov model (HMM) for propagating task estimates, coupled with a quadratic-programming-based elastic re-scheduler. The result is an integrated estimate-and-schedule adaptation scheme that quickly and efficiently re-plans the vehicles’ schedules based on in situ observations. The numerical simulation results show that this novel HMM approach decreases avoidable schedule variation by over a factor of two compared to existing methods.

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Theory and experimental results for the multiple aspect coverage problem

Cited by 14 publications

References 13 publications

Efficient reacquire and identify path planning over large areas

Efficient reacquire and identify path planning over large areas

The Orbiting Dubins Traveling Salesman Problem: planning inspection tours for a minehunting AUV

Persistent Schedule Evaluation and Adaptive Re-planning for Maritime Search Tasks

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