Route planning for active classification with UAVs

Vivaldini, Kelen Cristiane Teixeira; Guizilini, Vitor; Oliveira, Matheus Della Croce; Martinelli, Thiago H.; Wolf, Denis F.; Ramos, Fábio

doi:10.1109/icra.2016.7487412

Cited by 14 publications

(14 citation statements)

References 11 publications

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“…A large and growing body of literature exists for autonomous information gathering problems. Recently, this field has attracted considerable interest in the context of robotics-based environmental monitoring (Dunbabin and Marques 2012) for a wide variety of applications, including aerial surveillance (Colomina and Molina 2014;Vivaldini et al 2016), aquatic monitoring (Hitz et al 2014(Hitz et al , 2017, and infrastructure inspection (Ezequiel et al 2014;Bircher et al 2016). This section overviews recent work based on two main research streams: (1) methods for environmental modelling; and (2) algorithms for informative planning, or efficient data acquisition.…”

Section: Related Workmentioning

confidence: 99%

“…In the past several decades, rapid technological advances have unlocked their potential as a flexible, cost-efficient tool enabling monitoring at unprecedented levels of resolution and autonomy. In many emerging aerial (Ezequiel et al 2014;Vivaldini et al 2016;Colomina and Molina 2014) and aquatic (Hitz et al 2014(Hitz et al , 2017Girdhar and Dudek 2015) applications, these devices are replacing traditional data acquisition campaigns based on static sensors, manual sampling, or conventional manned platforms, which can be unreliable, costly, and even dangerous (Dunbabin and Marques 2012;Manfreda et al 2018).…”

Section: Introductionmentioning

confidence: 99%

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An informative path planning framework for UAV-based terrain monitoring

Popović

Vidal-Calleja

Hitz³

et al. 2020

Auton Robot

128

103

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Unmanned aerial vehicles represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path planning framework for monitoring scenarios using an aerial robot, focusing on problems in which the value of sensor information is unevenly distributed in a target area and unknown a priori. The approach is capable of learning and focusing on regions of interest via adaptation to map either discrete or continuous variables on the terrain using variable-resolution data received from probabilistic sensors. During a mission, the terrain maps built online are used to plan information-rich trajectories in continuous 3-D space by optimizing initial solutions obtained by a coarse grid search. Extensive simulations show that our approach is more efficient than existing methods. We also demonstrate its real-time application on a photorealistic mapping scenario using a publicly available dataset and a proof of concept for an agricultural monitoring task.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An informative path planning framework for UAV-based terrain monitoring

Popović

Vidal-Calleja

Hitz³

et al. 2020

Auton Robot

128

103

View full text Add to dashboard Cite

show abstract

“…A part ir dessa informação e, com o int uit o de reduzir as perdas causadas pelo espalhament o das doenças nas áreas plantadas com eucalipt os, Vivaldini et al (2016) propôs um framework para monit orament o cont ínuo das doenças at ravés de VANTs .…”

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“…Ent ão são aplicados algoritmos de planejament o de t rajet ória para minimizar a dist ância percorrida enquanto assegura-se uma boa cobert ura da área (VIVALDINI et al, 2016).…”

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