TY - GEN
T1 - Preliminary results on UAV-based forest fire localization based on decisional navigation
AU - Belbachir, A.
AU - Escareno, J.
AU - Rubio, E.
AU - Sossa, H.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/3/24
Y1 - 2016/3/24
N2 - Efficient localization of forest-fires based Unmanned Aerial Vehicles (UAVs) represents valuable assessment. Due to the fast deployment of UAVs, it is practical to use them. For forest fire detection purposes, usually the area to explore is unknown, thus existing strategies use an automatic coverage exploration strategy. However, such approach is not efficient in terms of exploration time since the mission execution and achievement in an unknown environment that needs a strong vehicle decision and control. Based on this observation, we improved the localization mission by a decision-based strategy resulting from a probabilistic model based on the temperature in order to estimate the distance towards the forest-fire. The UAV optimizes its trajectory according to the state of the forest-fire knowledge by using a map to represent its knowledge and updates it at each step of its exploration. We show in this paper that our planning and control methodology for forest-fire localization is efficient. Simulation results are carried out to evaluate the proposed methodology and approves our claim.
AB - Efficient localization of forest-fires based Unmanned Aerial Vehicles (UAVs) represents valuable assessment. Due to the fast deployment of UAVs, it is practical to use them. For forest fire detection purposes, usually the area to explore is unknown, thus existing strategies use an automatic coverage exploration strategy. However, such approach is not efficient in terms of exploration time since the mission execution and achievement in an unknown environment that needs a strong vehicle decision and control. Based on this observation, we improved the localization mission by a decision-based strategy resulting from a probabilistic model based on the temperature in order to estimate the distance towards the forest-fire. The UAV optimizes its trajectory according to the state of the forest-fire knowledge by using a map to represent its knowledge and updates it at each step of its exploration. We show in this paper that our planning and control methodology for forest-fire localization is efficient. Simulation results are carried out to evaluate the proposed methodology and approves our claim.
UR - http://www.scopus.com/inward/record.url?scp=84965141910&partnerID=8YFLogxK
U2 - 10.1109/RED-UAS.2015.7441030
DO - 10.1109/RED-UAS.2015.7441030
M3 - Contribución a la conferencia
AN - SCOPUS:84965141910
T3 - 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems, RED-UAS 2015
SP - 377
EP - 382
BT - 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems, RED-UAS 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems, RED-UAS 2015
Y2 - 23 November 2015 through 25 November 2015
ER -