Monitoring crop fields to map features like weeds can be efficiently performed with unmanned aerial vehicles (UAVs) that can cover large areas in a short time due to their privileged perspective and motion speed. However, the need for high-resolution images for precise classification of features (e.g., detecting even the smallest weeds in the field) contrasts with the limited payload and flight time of current UAVs. Thus, it requires several flights to cover a large field uniformly. However, the assumption that the whole field must be observed with the same precision is unnecessary when features are heterogeneously distributed, like weeds appearing in patches over the field. In this case, an adaptive approach that focuses only on relevant areas can perform better, especially when multiple UAVs are employed simultaneously. Leveraging on a swarm-robotics approach, we propose a monitoring and mapping strategy that adaptively chooses the target areas based on the expected information gain, which measures the potential for uncertainty reduction due to further observations. The proposed strategy scales well with group size and leads to smaller mapping errors than optimal pre-planned monitoring approaches.

Carbone, C., Albani, D., Magistri, F., Ognibene, D., Stachniss, C., Kootstra, G., et al. (2022). Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain. In Distributed Autonomous Robotic Systems (pp.306-319). Springer Nature [10.1007/978-3-030-92790-5_24].

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

Ognibene D.;
2022

Abstract

Monitoring crop fields to map features like weeds can be efficiently performed with unmanned aerial vehicles (UAVs) that can cover large areas in a short time due to their privileged perspective and motion speed. However, the need for high-resolution images for precise classification of features (e.g., detecting even the smallest weeds in the field) contrasts with the limited payload and flight time of current UAVs. Thus, it requires several flights to cover a large field uniformly. However, the assumption that the whole field must be observed with the same precision is unnecessary when features are heterogeneously distributed, like weeds appearing in patches over the field. In this case, an adaptive approach that focuses only on relevant areas can perform better, especially when multiple UAVs are employed simultaneously. Leveraging on a swarm-robotics approach, we propose a monitoring and mapping strategy that adaptively chooses the target areas based on the expected information gain, which measures the potential for uncertainty reduction due to further observations. The proposed strategy scales well with group size and leads to smaller mapping errors than optimal pre-planned monitoring approaches.
Si
poster + paper
Information gain; Precision farming; Swarm robotics; UAV
English
15th International Symposium on Distributed Autonomous Robotic Systems, DARS 2021 and 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics, SWARM 2021
978-3-030-92789-9
Carbone, C., Albani, D., Magistri, F., Ognibene, D., Stachniss, C., Kootstra, G., et al. (2022). Monitoring and Mapping of Crop Fields with UAV Swarms Based on Information Gain. In Distributed Autonomous Robotic Systems (pp.306-319). Springer Nature [10.1007/978-3-030-92790-5_24].
Carbone, C; Albani, D; Magistri, F; Ognibene, D; Stachniss, C; Kootstra, G; Nardi, D; Trianni, V
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/362662
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