UAV-borne LiDAR revolutionizing groundwater level mapping.

In hydrogeological research, the systematic and periodic measurement of the piezometric level is fundamental to assess aquifer storage, identify recharge and discharge areas, define flow directions and to infer the balance between inputs and withdrawals. Furthermore, knowledge of this variable and its fluctuations is essential for the efficient management and protection of groundwater resources. In this work, a novel methodology is proposed for the remote acquisition of piezometric information from traditional large-diameter wells, using drone-borne LiDAR observations. The workflow developed consists of different stages, from flight planning and parameter setting, to point cloud generation, data processing and validation and its statistical treatment to extract piezometric information. This methodology has been applied in a small coastal aquifer with numerous wells that have served as monitoring points. The UAV-LiDAR has enabled the straightforward obtention of measurements of the piezometric level with very high vertical accuracies (RMSE of 5 cm) with minimum and maximum residuals of -8.7 and 7.9 cm respectively. Likewise, the method has shown vertical accuracies 3 times better than those inferred from the official DTM of best resolution available in Spain, which is usually used in hydrogeological works. Since the technique provides absolute values of the piezometric level, it eliminates the need for laborious levelling work prior to hydrogeological campaigns. This method has proved to be an effective alternative/complementary technique to traditional measurements of the piezometric level, allowing to monitor extensive or inaccessible areas over short periods of time and to potentially reduce gaps in hydrogeological databases.

A Distributed Algorithm for Real-Time Multi-Drone Collision-Free Trajectory Replanning.

In this paper, we present a distributed algorithm to generate collision-free trajectories for a group of quadrotors flying through a common workspace. In the setup adopted, each vehicle replans its trajectory, in a receding horizon manner, by solving a small-scale optimization problem that only involves its own individual variables. We adopt the Voronoi partitioning of space to derive local constraints that guarantee collision avoidance with all neighbors for a certain time horizon. The obtained set of collision avoidance constraints explicitly takes into account the vehicle's orientation to avoid infeasiblity issues caused by ignoring the quadrotor's rotational motion. Moreover, the resulting constraints can be expressed as Bézier curves, and thus can be evaluated efficiently, without discretization, to ensure that collision avoidance requirements are satisfied at any time instant, even for an extended planning horizon. The proposed approach is validated through extensive simulations with up to 100 drones. The results show that the proposed method has a higher success rate at finding collision-free trajectories for large groups of drones compared to other Voronoi diagram-based methods.