Self-awareness approach for complete coverage metrology using autonomous systems

Unmanned aerial vehicles (UAV) are autonomous systems with embedded intelligence. Despite the numerous advantages they bring, multiple problems have been arisen in the past, like collision detection and avoidance of multiple aerial aircrafts. The recent advances of on-board sensors and computation devices have allowed complete ground-breaking improvements, in the present and forthcoming navigation of multiple UAVs. These are now fostering the implantation of the next generation of autonomous aerial aircrafts, with a growing potential in important sectors like precision agriculture. Industries like, Drone hopper, are developing new advanced aerial platforms that will provide deep support to automation in the agriculture fields where it is necessary to optimize multiple trajectories of UAVs to accomplish tasks, such as, fumigation, irrigation, and strengthening plant growth. Precise measurements and UAV’s trajectory planning are necessary task for the complete coverage optimization problem. In this paper, we analyze and simulate a fully distributed solution with a self-awareness approach. Our solution has been tested in simulation (code, video) and it will be applied in a near future to real operations in outdoor environments using efficient and robust platforms provided by the Drone Hopper company.