The demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols. © 2017 IEEE.

Field coverage and weed mapping by UAV swarms / Albani, D.; Nardi, D.; Trianni, V.. - (2017), pp. 4319-4325. (Intervento presentato al convegno 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 tenutosi a Vancouver; Canada) [10.1109/IROS.2017.8206296].

Field coverage and weed mapping by UAV swarms

Albani D.
;
Nardi D.;
2017

Abstract

The demands from precision agriculture (PA) for high-quality information at the individual plant level require to re-think the approaches exploited to date for remote sensing as performed by unmanned aerial vehicles (UAVs). A swarm of collaborating UAVs may prove more efficient and economically viable compared to other solutions. To identify the merits and limitations of a swarm intelligence approach to remote sensing, we propose here a decentralised multi-agent system for a field coverage and weed mapping problem, which is efficient, intrinsically robust and scalable to different group sizes. The proposed solution is based on a reinforced random walk with inhibition of return, where the information available from other agents (UAVs) is exploited to bias the individual motion pattern. Experiments are performed to demonstrate the efficiency and scalability of the proposed approach under a variety of experimental conditions, accounting also for limited communication range and different routing protocols. © 2017 IEEE.
2017
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017
Robots; Robotics; Robot swarms
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Field coverage and weed mapping by UAV swarms / Albani, D.; Nardi, D.; Trianni, V.. - (2017), pp. 4319-4325. (Intervento presentato al convegno 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017 tenutosi a Vancouver; Canada) [10.1109/IROS.2017.8206296].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1321721
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