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Cumulus cloud monitoring
Cumulus cloud monitoring
16
Alternate aerial environment monitoring application
Clouds are like birds that just don’t sleep. But what do they do with all this time they get? Dr. Kshitij Tiwari
Contents 16.1 Motivation 207 16.2 Challenges 208 16.3 Success story 209 References 209
Highlights • • •
Motivation for deploying robots for monitoring clouds Challenges faced when deploying robots for cumulus cloud monitoring Open research challenges
Previously, in this book, aerial environment monitoring in terms of suspended particulate matter was discussed. As an alternate application, this chapter presents the monitoring of clouds. To some extent, both applications face similar challenges like the effect of wind on the concentration of pollutant or the cloud volume; vast expanse of area to be covered and sparse data available for modeling.
16.1 Motivation Most of the climate General Circulation Models (GCMs) suffer from erroneous prediction of precipitation. This can be attributed partially to the uncertainties induced by clouds which are not completely understood. This in turn is governed by the weak entrainment as explained in [1]. Additionally, the erroneous predictions can also be attributed to limited complexity of the micro-physics models of the clouds themselves owing to the limits of the available computational hardware [2]. These erroneous predictions could potentially have adverse effects for the environments. For instance, Multi-Robot Exploration for Environmental Monitoring. https://doi.org/10.1016/B978-0-12-817607-8.00033-2 Copyright © 2020 Elsevier Inc. All rights reserved.
208
Multi-Robot Exploration for Environmental Monitoring
dry/warm season crops like tomato, pepper, cucumber, okra, eggplant, garden egg, melon, pumpkin, sweet potato, etc., are most likely to fail if preventive measures are not taken to protect the crops from excess rainfall. This is highly contingent on apt and timely warning against expected precipitation, and, if the predictions are off by order of hours, it could lead to complete crop failures, costing the farmers dearly. To alleviate these uncertainties, accurate model parameters and adequate measurements are required to accurately model the cloud dynamics. Doing so would require a dense spatio-temporal resolution of acquired data which is challenging for static sensors but is claimed to be achievable by a fleet of UAVs by works like [3].
16.2
Challenges
Deploying robots like UAVs to monitor cloud formations and gathering adequate data to take informed decisions are challenging tasks. Some of these are enlisted below. 1. Sensing limitations. Most of the robotics literature utilizes exteroceptive sensors for wide and long measuring ranges [3]. In this setting, within the sensing range, a large amount of data samples are acquired. On the other hand, most environment monitoring applications like cloud monitoring, or the ozone pollution monitoring discussed earlier, rely on point-sensing (interoceptive sensing as described in Chap. 3). As opposed to the exteroceptive setting, in this case, the sensory data is only available from the immediate position where the robot/sensor is located at a given point in time. 2. Spatio-temporal evolution. The environmental factors of interest (namely pressure, temperature, radiance, 3D wind, liquid water content, and aerosols) which impact the precipitation estimates vary spatially and evolve temporally. This means that the models required need high dimensional structures while the data stream is only available as a 1D time series. 3. Impact of environmental factors. Environmental factors, for instance, wind gusts, affect the cloud formation process and the correlated parameters of interest. Additionally, those have an impact on the energy consumed by the UAVs being used to monitor the clouds. 4. Mission lifespan. As explained in Chap. 10, UAVs have limited endurance.1 For an effective mission, this entails that the endurance must be within the same range as the cumulus lifespan of the clouds. Beset with these challenges, exploring the clouds and adaptively sampling the adequate parameters for accurate climate GCMs therefore remain a complex problem. Having said this, some progress has been made and the next section describes one of the most recent projects on cloud monitoring. 1 This claim does not elude to research platforms powered by renewable energy sources as they have not
been adopted for mainstream and high impact researches for environment monitoring.
Cumulus cloud monitoring
16.3
209
Success story
One of the most notable success stories was the Skyscanner project [4] that terminated in January 2017. The project was targeted at the development of a fleet of autonomous UAVs to adaptively sample cumuli, so as to provide relevant sensory information to address long standing questions in atmospheric science pertaining to mapping of atmospheric variables at low altitudes. The objectives of this project were twofold: (i) to bring together a team of developers/designers to develop UAVs and optimize the flight control whilst jointly working with atmospheric scientists, and (ii) to help researchers that can utilize the acquired information and plug into machine learning models like GPs to map atmospheric variables based on actively acquired samples.
Figure 16.1 Mako aircraft used for the Skyscanner project. Image taken from [3].
As an outcome of this project, the researchers presented an information acquisition mechanism for a fleet of energy constrained mako aircraft with a wing span of 1.288 m and weight of 0.9 kg as shown in Fig. 16.1. The approach is claimed to generate energy optimal flight patterns that account for wind updrafts while gathering observations. However, a vast majority of the results were shown in simulations. The prediction errors achieved by GPs were significantly low, making them amicable for reliable predictions in cumulus cloud formations, but deploying such a fleet in reality still needs significant work. In the authors’ own words from [3], a distributed architecture is “far-fetched” and it was recommended to make attempts to deploy a centralized fleet. A major bottleneck is the computational resources needed to embed GP regression framework on-board off-the-shelf UAVs. This was also the reason why during the span of this project, the author(s)’ have also been able to validate only partial framework in real-world. Additionally, factors like collision avoidance, receding horizon path planning, sampling rate, etc., have been left out for further works.
References [1] A.D. Del Genio, J. Wu, The role of entrainment in the diurnal cycle of continental convection, Journal of Climate 23 (10) (2010) 2722–2738. [2] B. Stevens, S. Bony, What are climate models missing?, Science 340 (6136) (2013) 1053–1054.
210
Multi-Robot Exploration for Environmental Monitoring
[3] C. Reymann, A. Renzaglia, F. Lamraoui, M. Bronz, S. Lacroix, Adaptive sampling of cumulus clouds with UAVs, Autonomous Robots 42 (2) (2018) 491–512. [4] S. Lacroix, G. Roberts, E. Benard, M. Bronz, F. Burnet, E. Bouhoubeiny, J.P. Condomines, C. Doll, G. Hattenberger, F. Lamraoui, et al., Fleets of enduring drones to probe atmospheric phenomena with clouds, in: EGU General Assembly Conference Abstracts, vol. 18, 2016.
16
Alternate aerial environment monitoring application
Clouds are like birds that just don’t sleep. But what do they do with all this time they get? Dr. Kshitij Tiwari
Contents 16.1 Motivation 207 16.2 Challenges 208 16.3 Success story 209 References 209
Highlights • • •
Motivation for deploying robots for monitoring clouds Challenges faced when deploying robots for cumulus cloud monitoring Open research challenges
Previously, in this book, aerial environment monitoring in terms of suspended particulate matter was discussed. As an alternate application, this chapter presents the monitoring of clouds. To some extent, both applications face similar challenges like the effect of wind on the concentration of pollutant or the cloud volume; vast expanse of area to be covered and sparse data available for modeling.
16.1 Motivation Most of the climate General Circulation Models (GCMs) suffer from erroneous prediction of precipitation. This can be attributed partially to the uncertainties induced by clouds which are not completely understood. This in turn is governed by the weak entrainment as explained in [1]. Additionally, the erroneous predictions can also be attributed to limited complexity of the micro-physics models of the clouds themselves owing to the limits of the available computational hardware [2]. These erroneous predictions could potentially have adverse effects for the environments. For instance, Multi-Robot Exploration for Environmental Monitoring. https://doi.org/10.1016/B978-0-12-817607-8.00033-2 Copyright © 2020 Elsevier Inc. All rights reserved.
208
Multi-Robot Exploration for Environmental Monitoring
dry/warm season crops like tomato, pepper, cucumber, okra, eggplant, garden egg, melon, pumpkin, sweet potato, etc., are most likely to fail if preventive measures are not taken to protect the crops from excess rainfall. This is highly contingent on apt and timely warning against expected precipitation, and, if the predictions are off by order of hours, it could lead to complete crop failures, costing the farmers dearly. To alleviate these uncertainties, accurate model parameters and adequate measurements are required to accurately model the cloud dynamics. Doing so would require a dense spatio-temporal resolution of acquired data which is challenging for static sensors but is claimed to be achievable by a fleet of UAVs by works like [3].
16.2
Challenges
Deploying robots like UAVs to monitor cloud formations and gathering adequate data to take informed decisions are challenging tasks. Some of these are enlisted below. 1. Sensing limitations. Most of the robotics literature utilizes exteroceptive sensors for wide and long measuring ranges [3]. In this setting, within the sensing range, a large amount of data samples are acquired. On the other hand, most environment monitoring applications like cloud monitoring, or the ozone pollution monitoring discussed earlier, rely on point-sensing (interoceptive sensing as described in Chap. 3). As opposed to the exteroceptive setting, in this case, the sensory data is only available from the immediate position where the robot/sensor is located at a given point in time. 2. Spatio-temporal evolution. The environmental factors of interest (namely pressure, temperature, radiance, 3D wind, liquid water content, and aerosols) which impact the precipitation estimates vary spatially and evolve temporally. This means that the models required need high dimensional structures while the data stream is only available as a 1D time series. 3. Impact of environmental factors. Environmental factors, for instance, wind gusts, affect the cloud formation process and the correlated parameters of interest. Additionally, those have an impact on the energy consumed by the UAVs being used to monitor the clouds. 4. Mission lifespan. As explained in Chap. 10, UAVs have limited endurance.1 For an effective mission, this entails that the endurance must be within the same range as the cumulus lifespan of the clouds. Beset with these challenges, exploring the clouds and adaptively sampling the adequate parameters for accurate climate GCMs therefore remain a complex problem. Having said this, some progress has been made and the next section describes one of the most recent projects on cloud monitoring. 1 This claim does not elude to research platforms powered by renewable energy sources as they have not
been adopted for mainstream and high impact researches for environment monitoring.
Cumulus cloud monitoring
16.3
209
Success story
One of the most notable success stories was the Skyscanner project [4] that terminated in January 2017. The project was targeted at the development of a fleet of autonomous UAVs to adaptively sample cumuli, so as to provide relevant sensory information to address long standing questions in atmospheric science pertaining to mapping of atmospheric variables at low altitudes. The objectives of this project were twofold: (i) to bring together a team of developers/designers to develop UAVs and optimize the flight control whilst jointly working with atmospheric scientists, and (ii) to help researchers that can utilize the acquired information and plug into machine learning models like GPs to map atmospheric variables based on actively acquired samples.
Figure 16.1 Mako aircraft used for the Skyscanner project. Image taken from [3].
As an outcome of this project, the researchers presented an information acquisition mechanism for a fleet of energy constrained mako aircraft with a wing span of 1.288 m and weight of 0.9 kg as shown in Fig. 16.1. The approach is claimed to generate energy optimal flight patterns that account for wind updrafts while gathering observations. However, a vast majority of the results were shown in simulations. The prediction errors achieved by GPs were significantly low, making them amicable for reliable predictions in cumulus cloud formations, but deploying such a fleet in reality still needs significant work. In the authors’ own words from [3], a distributed architecture is “far-fetched” and it was recommended to make attempts to deploy a centralized fleet. A major bottleneck is the computational resources needed to embed GP regression framework on-board off-the-shelf UAVs. This was also the reason why during the span of this project, the author(s)’ have also been able to validate only partial framework in real-world. Additionally, factors like collision avoidance, receding horizon path planning, sampling rate, etc., have been left out for further works.
References [1] A.D. Del Genio, J. Wu, The role of entrainment in the diurnal cycle of continental convection, Journal of Climate 23 (10) (2010) 2722–2738. [2] B. Stevens, S. Bony, What are climate models missing?, Science 340 (6136) (2013) 1053–1054.
210
Multi-Robot Exploration for Environmental Monitoring
[3] C. Reymann, A. Renzaglia, F. Lamraoui, M. Bronz, S. Lacroix, Adaptive sampling of cumulus clouds with UAVs, Autonomous Robots 42 (2) (2018) 491–512. [4] S. Lacroix, G. Roberts, E. Benard, M. Bronz, F. Burnet, E. Bouhoubeiny, J.P. Condomines, C. Doll, G. Hattenberger, F. Lamraoui, et al., Fleets of enduring drones to probe atmospheric phenomena with clouds, in: EGU General Assembly Conference Abstracts, vol. 18, 2016.