Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain

Carbone, Carlos; Albani, Dario; Magistri, Federico; Ognibene, Dimitri; Stachniss, Cyrill; Kootstra, Gert; Nardi, Daniele; Trianni, Vito

doi:10.1007/978-3-030-92790-5_24

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…Finally, we aim to use the data gathered from the rover to create a map of the electrostatic environment with an additional focus on electrostatic anomalies or 'hotspots'. This could involve the rover exploring areas based on information gain [23], rather than following a pre-determined path.…”

Section: Rover Trialsmentioning

confidence: 99%

Exploring the Use of Terrestrial Robots for Atmospheric Electricity Measurement

Zijlstra,

Aplin,

Hunt

2024

J. Phys.: Conf. Ser.

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Measuring the electric field is a central goal in electrostatics research, such as the study of electrostatics in atmospheric processes. It serves as a key indicator for various atmospheric phenomena, including the presence of lightning, dust or charged clouds. Traditionally, electric field measurements have been conducted from static platforms, with limited mobile measurements from airborne platforms such as balloons or, more recently, Unmanned Aerial Vehicles (UAVs). Here, we explore the potential of terrestrial robots to measure the electric field with some level of autonomy, such as during supervised navigation between user-defined waypoints. We mount a field mill on a four-wheeled rover and use a Global Navigation Satellite System (GNSS) to track the position of its measurements during an outdoor survey. The robot has a depth camera for 3D terrain mapping to contextualise local field measurements. We present plans for future research, including the use of semi-autonomous identification and exploration of electrostatic ‘hotspots’; and deployment of multiple robots (e.g., six) in a ‘sparse swarm’ configuration. We consider opportunities to employ such a robot system in environmental science or space research, for instance on Mars or the moon, where an understanding of electrostatic processes could be significant for future space missions.

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Section: Rover Trialsmentioning

confidence: 99%

Exploring the Use of Terrestrial Robots for Atmospheric Electricity Measurement

Zijlstra,

Aplin,

Hunt

2024

J. Phys.: Conf. Ser.

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“…Unmanned Aerial Vehicles (UAVs) [3] or drones equipped with onboard cameras and sensors have already demonstrated that autonomous flights are possible in real environments. This sparked great interest in a plethora of application scenarios, with crop monitoring [4], surveillance [5], medical and food delivery [6], and search and rescue in disaster areas [7] representing only some of the relevant applications of UAVs.…”

Section: Introductionmentioning

confidence: 99%

Simulation-based Test Case Generation for Unmanned Aerial Vehicles in the Neighborhood of Real Flights

Khatiri

Panichella

Tonella

2023

2023 IEEE Conference on Software Testing, Verification and Validation (ICST)

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Unmanned aerial vehicles (UAVs), also known as drones, are acquiring increasing autonomy. With their commercial adoption, the problem of testing their functional and non-functional, and in particular their safety requirements has become a critical concern. Simulation-based testing represents a fundamental practice, but the testing scenarios considered in software-in-the-loop testing may not be representative of the actual scenarios experienced in the field.In this paper, we propose SURREAL (teSting Uavs in the neighboRhood of REAl fLights), a novel search-based approach that analyses logs of real UAV flights and automatically generates simulation-based tests in the neighborhood of such real flights, thereby improving the realism and representativeness of the simulation-based tests. This is done in two steps: first, SURREAL faithfully replicates the given UAV flight in the simulation environment, generating a simulation-based test that mirrors a pre-logged real-world behavior. Then, it smoothly manipulates the replicated flight conditions to discover slightly modified flight scenarios that are challenging or trigger misbehaviors of the UAV under test in simulation. In our experiments, we were able to replicate a real flight accurately in the simulation environment and to expose unstable and potentially unsafe behavior in the neighborhood of a flight, which even led to crashes.

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“…By modifying plans online, such strategies enable adapting the flight path according to the mission aim to maximize the value of collected data. This can be used for improving the geometric estimation [22] or for semantic estimation tasks [35,5]. Several previous studies in UAV-based informative planning either consider planning at a fixed altitude [37,31], i.e.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation

Stache¹,

Westheider²,

Magistri³

et al. 2022

Preprint

Self Cite

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Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems. In many applications, the usage of unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. A key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. This is, for example, relevant for monitoring agricultural fields. This paper addresses the problem of adaptive path planning for accurate semantic segmentation of using UAVs. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum image resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on different domains using real-world data, proving the efficacy and generability of our solution.

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Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain

Cited by 9 publications

References 23 publications

Exploring the Use of Terrestrial Robots for Atmospheric Electricity Measurement

Exploring the Use of Terrestrial Robots for Atmospheric Electricity Measurement

Simulation-based Test Case Generation for Unmanned Aerial Vehicles in the Neighborhood of Real Flights

Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation

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