Optimal Route Planning of Unmanned Aerial Vehicle for Data Collection from Sensor Nodes.

Automated surveillance of agricultural land is increasingly using wireless, communicating underground sensors to record local variables such as temperature, hydrometry or the presence of certain chemical elements. These electronic detection devices are buried at various depths to protect them from destruction by motorised machinery or animals. This results in a wide range of signal emission characteristics and short communication ranges. The originality of the project lies in the fact that the task of collecting data is carried out by a drone, whose mission is to cover the areas where transmissions with the sensors are possible. Determining the order of collecting data between these zones must take into account the distances covered and the total energy consumed by the drone.
This theme is naturally related to combinatorial optimisation, where many variations of the classic travelling salesman problem have been investigated. Uncertainties over the position of the sensor nodes have led us to study the close enough traveling salesman problem, taking into account the obstacles (trees, pillars, etc.) that the drone must avoid Uncertainties over the position of the sensor nodes have led us to study the close enough traveling salesman problem, taking into account the obstacles (trees, pillars, etc.) that the drone must avoid during its rounds. 

The implementation of the various algorithmic solutions will be evaluated in the context of data collection carried out in experimental agricultural areas managed by INRAE (Montoldre site, 03). 

The Codic project is supported by the International Research Center "Innovation Transportation and Production Systems" of the I-SITE CAP 20-25. It is based on collaboration between the LIMOS laboratory, INRAE's TSCF research unit and the Computer Science and the Mathematical Research Institute of the Universidad de La Laguna (IMAULL) in Tenerife (Spain).