Multiresolution Mapping and Informative Path Planning for UAV-based Terrain Monitoring

Popović, Marija; Vidal-Calleja, Teresa; Hitz, Gregory; Sa, Inkyu; Siegwart, Roland; Nieto, Juan

doi:10.48550/arxiv.1703.02854

Cited by 1 publication

(1 citation statement)

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“…In response to this potential, the research community is pushing the limits with respect to the system capacity and overall intelligence. Most commonly, aerial robotics in particular are utilized as information gathering platforms exploiting their ability to seamlessly navigate without being subject to limitations of ground or surface locomotion [1][2][3]. Despite the great progress of the research community, and although significant effort has been put in developing methods for autonomous exploration of complex and often unknown environments , little progress has been made for the specific consideration of rewards sparsity.…”

Section: Introductionmentioning

confidence: 99%

Lévy Flight Foraging Hypothesis-based Autonomous Memoryless Search Under Sparse Rewards

Papachristos,

Alexis

2018

Preprint

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Autonomous robots are commonly tasked with the problem of area exploration and search for certain targets or artifacts of interest to be tracked. Traditionally, the problem formulation considered is that of complete search and thusideally -identification of all targets of interest. An important problem however which is not often addressed is that of timeefficient memoryless search under sparse rewards that may be worth visited any number of items. In this paper we specifically address the largely understudied problem of optimizing the "time-of-arrival" or "time-of-detection" to robotically search for sparsely distributed rewards (detect targets of interest) within large-scale environments and subject to memoryless exploration. At the core of the proposed solution is the fact that a search-based Lévy walk consisting of a constant velocity search following a Lévy flight path is optimal for searching sparse and randomly distributed target regions in the lack of map memory. A set of results accompany the presentation of the method, demonstrate its properties and justify the purpose of its use towards large-scale area exploration autonomy.

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Section: Introductionmentioning

confidence: 99%