The massive deployment of soil moisture and temperature sensors inside agricultural plots will become essential in the next years to meet the challenges of the agroecological transition. In such a context, the use of buried communicating sensor nodes is particularly relevant because they are protected underground from human activities, animals and agricultural machinery. Their communication range is however limited to a few meters above the ground, that leads to diiculties in collecting their data remotely. One possible solution is to use one or several UAVs to visit them successively. Nevertheless, this approach requires being able to take into account the presence of obstacles at low light height in the path planning algorithms, and to solve the Close Enough Traveling Salesman Problem (CE-TSP) in cluttered environments. To meet this need, the objective of this paper is to present the possibility of exploiting real data from an airborne Light Detection and Ranging sensor (LiDAR) to determine the location and height of obstacles in the workspace of the UAV. Based on this data, a cost matrix associated to the trajectories between each node can be calculated, with the possibility for the UAV to ly above the obstacle areas. Next, to optimize the route of the UAV in the CE-TSP framework, this paper presents a simple approach based on a Partheno Genetic Algorithm (PGA) completed with some heuristic rules. Our method is applied in a real use case consisting of 50 sensor nodes distributed in our experimental farm. The results highlight the performances of the method proposed. They demonstrate the capabilities of our method to address the CE-TSP with obstacles, and open the way to future prospects for collecting data of buried sensor nodes using UAVs.

CCS Concepts: • Theory of computation → Approximation algorithms analysis.