Implementation of Cloud-Based Drone Navigation for Swarm Robot Coordination

Abstract

Smart agriculture 4.0 has recently been implemented in Indonesia to enhance agricultural productivity through the use of advance technology. Unmanned Autonomous Vehicle (UAVs) is one of the technologies that have been utilized in the agricultural sector to improve production quality and quantity. Although some advanced technology has been used, currently there are some challenges that remain to be solved to implement multi-UAV in the real environment. Some of these challenges include battery limitations in UAV and the long duration to queue at the charging station. To address this issue, a previous study has proposed Cloud Based Drone Navigation (CBDN) algorithm that can be employed to optimize multi-UAV coordination by selecting the best flight path for the UAV to reach a charging station. Such an approach has resulted in reducing the waiting time of UAVs to be charged. However, the algorithm has not considered swarm robot parameters. This study aims to analyze the use of CBDN algorithm with parameters derived from swarm robots. The performance of the CBDN algorithm will then be evaluated and compared to the Shortest Flight Time (SFT) and Individual Reservation Navigation System (IRN) algorithms as two benchmark algorithms, in terms of the total travel time. By considering real swarm robot parameters, the CBDN algorithm has resulted in an average total travel time of 17.44% less than the average total travel time of SFT and 17.25% less than the average total travel time of IRN.