Energy-constrained motion planning for information gathering with autonomous aerial soaring

Nguyen, Joseph L.; Lawrance, Nicholas; Sukkarieh, Salah

doi:10.1109/icra.2013.6631115

Cited by 27 publications

(18 citation statements)

References 12 publications

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“…Even though this latter work does not take into account air currents for the navigation, it is worth to remark that it includes experiments with a real platform -contrary to the previous ones. Autonomous soaring has also been studied in different scenarios, as in [7], where a glider has to search for a target on the ground. The goal here is to maximize the probability of detecting the target traveling between thermals with known location.…”

Section: Introductionmentioning

confidence: 99%

Monitoring the evolution of clouds with UAVs

Renzaglia

Reymann

Lacroix

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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We study the problem of monitoring the evolution of atmospheric variables within low-altitude cumulus clouds with a fleet of Unmanned Aerial Vehicles (UAVs). To tackle this challenge, two main problems can be identified: i) creating on-line maps of the relevant variables, based on sparse local measurements; ii) designing a planning algorithm which exploits the obtained map to generate trajectories that optimize the adaptive data sampling process, minimizing the uncertainty in the map, while steering the vehicles within the air flows to generate energetic-efficient flights. Our approach is based on Gaussian Processes (GP) for the mapping, combined with a stochastic optimization scheme for the trajectories generation. The system is tested in simulations carried out using a realistic three-dimensional current field. Results for a single UAV as well as for a fleet of multiple UAVs, sharing information to cooperatively achieve the mission, are provided.

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Section: Introductionmentioning

confidence: 99%

Monitoring the evolution of clouds with UAVs

Renzaglia

Reymann

Lacroix

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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“…Recent work has begun to explore sampling-based methods [11]. Numerous applications of information-based planning involve UAVs [10,15,21,24,25,27]. In previous work we explored multi-UAV constrained search [8] and UAV planning using formal methods [30,31].…”

Section: Related Workmentioning

confidence: 99%

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Cliff

Fitch

Sukkarieh

et al. 2015

Robotics: Science and Systems XI

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112

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Abstract-The application of autonomous robots to efficiently locate small wildlife species has the potential to provide significant ecological insights not previously possible using traditional landbased survey techniques, and a basis for improved conservation policy and management. We present an approach for autonomously localizing radio-tagged wildlife using a small aerial robot. We present a novel two-point phased array antenna system that yields unambiguous bearing measurements and an associated uncertainty measure. Our estimation and informationbased planning algorithms incorporate this bearing uncertainty to choose observation points that improve confidence in the location estimate. These algorithms run online in real time and we report experimental results that show successful autonomous localization of stationary radio tags and live radio-tagged birds.

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“…This section summarises the IFS problem [1] and defines a new variant with drifting thermals. Novel elements include thermal motion prediction and unknown thermal locations.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The informative soaring (IFS) problem combines informative path planning (IPP) with autonomous aerial soaring [1]- [3]. The problem formulation is motivated by a target-search scenario conducted by a gliding unmanned aerial vehicle (UAV) which periodically replenishes its energy level using thermal soaring.…”

Section: Introductionmentioning

confidence: 99%

Informative soaring with drifting thermals

Nguyen

Lawrance

Sukkarieh

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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The informative soaring (IFS) problem involves a gliding unmanned aerial vehicle (UAV) exploiting energy from thermals to extend its information gathering capability. In this paper, we address the realistic situation of detecting new thermals drifting with the wind in the search environment. We consider complex target-search scenarios characterised by information clusters and propose a new set of algorithms designed to both explore for and exploit high-value thermals to maximise information gain. Our algorithms: 1) compute a thermal exploration map to detect useful thermals that eventually intercept clusters, 2) solve a boundary value problem for interthermal path segment (ITP) generation with moving thermals, 3) compute thermal time windows to gather information from clusters and form a cluster service schedule, and 4) use branch and bound (BnB) tree search for global planning, considering high-utility-rate ITPs to maximise information gain. Our solution is compared against a greedy method that neither considers the thermal exploration map nor cluster schedule and a full knowledge method that has access to all thermals. Numerical simulations show that on average, our solution outperforms the greedy method in one-third of 2400 Monte Carlo trials, and achieves similar performance to the full knowledge method when environmental conditions are favourable.

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Energy-constrained motion planning for information gathering with autonomous aerial soaring

Cited by 27 publications

References 12 publications

Monitoring the evolution of clouds with UAVs

Monitoring the evolution of clouds with UAVs

Online Localization of Radio-Tagged Wildlife with an Autonomous Aerial Robot System

Informative soaring with drifting thermals

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