Authors:
Assia Belbachir
and
Juan-Antonio Escareno
Affiliation:
Polytechnic Institute of Advanced Sciences and IPSA, France
Keyword(s):
Autonomous Decision, High-level Planning, Control, UAVs.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Engineering Applications
;
Formal Methods
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
Simulation and Modeling
;
Symbolic Systems
Abstract:
This paper addresses the problem of forest-fire localization using unmanned aerial vehicles (UAVs). Due to the fast deployment of UAVs, it is practical to use them. In forest fires, usually the area to explore is unknown. Thus, existing studies use an automatic or semi-automatic exploration strategy following a zig-zag sweep pattern or expanding spiral search pattern. However, such an approach is not optimal in terms of exploration time since the mission execution and achievement in an unknown environment requires autonomous vehicle decision and control. This paper presents an enhanced approach for the fire localization mission via a decisional strategy considering a probabilistic model that uses the temperature 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 exploration step. We show in this paper that our planning and control method
ology for forest-fire localization is efficient. Simulation results are carried out to evaluate the feasibility of the generated paths by the proposed methodology.
(More)