Nuclear Environments Inspection with Micro Aerial Vehicles: Algorithms and Experiments

Thakur, Dinesh; Loianno, Giuseppe; Liu, Wenxin; Kumar, Vijay

doi:10.1007/978-3-030-33950-0_17

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…Autonomous robotic systems are becoming more resilient and capable of performing monitoring tasks in degraded and hazardous environments Chung et al (2018). Applications include volcano monitoring Wood et al (2020), subterranean exploration Dang et al (2020), mapping mines Weber (1995), and nuclear facilities Thakur et al (2020), surveying penguin colonies Shah et al (2020), and disaster relief operations Sun et al (2020), to name just a few. The underlying requirement across all these examples is one we share; to take the human out of harm's way, putting the risk on the expendable robotic hardware.…”

Section: Environmental Sensing By Uavsmentioning

confidence: 99%

Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs

Ericksen

Fricke

Nowicki

et al. 2022

Front. Control Eng.

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We present methods for autonomous collaborative surveying of volcanic CO2 emissions using aerial robots. CO2 is a useful predictor of volcanic eruptions and an influential greenhouse gas. However, current CO2 mapping methods are hazardous and inefficient, as a result, only a small fraction of CO2 emitting volcanoes have been surveyed. We develop algorithms and a platform to measure volcanic CO2 emissions. The Dragonfly Unpiloted Aerial Vehicle (UAV) platform is capable of long-duration CO2 collection flights in harsh environments. We implement two survey algorithms on teams of Dragonfly robots and demonstrate that they effectively map gas emissions and locate the highest gas concentrations. Our experiments culminate in a successful field test of collaborative rasterization and gradient descent algorithms in a challenging real-world environment at the edge of the Valles Caldera supervolcano. Both algorithms treat multiple flocking UAVs as a distributed flexible instrument. Simultaneous sensing in multiple UAVs gives scientists greater confidence in estimates of gas concentrations and the locations of sources of those emissions. These methods are also applicable to a range of other airborne concentration mapping tasks, such as pipeline leak detection and contaminant localization.

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Section: Environmental Sensing By Uavsmentioning

confidence: 99%

Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs

Ericksen

Fricke

Nowicki

et al. 2022

Front. Control Eng.

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“…The individual MAV control and estimation is based on our previous work [23,24]. The system combines the Inertial Measurement Unit (IMU) data and the VGA downward camera image data in an Unscented Kalman Filter (UKF) framework in order to localize the vehicle in the environment.…”

Section: System Architecturementioning

confidence: 99%

Swarm of Inexpensive Heterogeneous Micro Aerial Vehicles

Thakur

Tao

et al. 2021

Experimental Robotics

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“…It is relevant to applications that are too hazardous or costly for humans to operate in e.g. disaster response [1], search and rescue operations [2], [3]. One particular case that has gained attention from recent research is underground exploration, [4], [5], [6] which arose from the DARPA Subterranean Challenge.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Topological Exploration Planning in Large-Scale 3D Environments

Yang¹,

Lee²,

Keller³

et al. 2021

Preprint

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Currently, state-of-the-art exploration methods maintain high-resolution map representations in order to optimize exploration goals in each step that maximizes information gain. However, during exploring, those "optimal" selections could quickly become obsolete due to the influx of new information, especially in large-scale environments, and result in high-frequency re-planning that hinders the overall exploration efficiency. In this paper, we propose a graph-based topological planning framework, building a sparse topological map in three-dimensional (3D) space to guide exploration steps with high-level intents so as to render consistent exploration maneuvers. Specifically, this work presents a novel method to estimate 3D space's geometry with convex polyhedrons. Then, the geometry information is utilized to group space into distinctive regions. And those regions are added as nodes into the topological map, directing the exploration process. We compared our method with the state-of-the-art in simulated environments. The proposed method achieves higher space coverage and outperforms exploration efficiency by more than 40% during experiments. Finally, a field experiment was conducted to further evaluate the applicability of our method to empower efficient and robust exploration in real-world environments.

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Nuclear Environments Inspection with Micro Aerial Vehicles: Algorithms and Experiments

Cited by 12 publications

References 15 publications

Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs

Aerial Survey Robotics in Extreme Environments: Mapping Volcanic CO2 Emissions With Flocking UAVs

Swarm of Inexpensive Heterogeneous Micro Aerial Vehicles

Graph-Based Topological Exploration Planning in Large-Scale 3D Environments

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