Placement of Swarm UAV for Data Collection: A Deep Reinforcement Learning Approach

Rapid technical advancements in recent times have made it possible to produce a class of affordable unmanned aerial vehicles (UAVs), which can serve a wide range of public and private users for different industrial or non-industrial requirements. It is very complex to set up a communication network among the UAVs, which is very essential for completing any task like collecting data in extreme weather conditions. For UAV placement, we have suggested a distributed approach using PPO-based reinforcement learning; here, UAVs are utilized as aerial access points to create a mesh network that connects to ground nodes positioned in a designated area and can collect data from ground nodes to predict weather conditions. Our objective is to fulfill each ground node’s data rate requirements while deploying the fewest number of UAVs possible to cover it. The convex hull formation of swarm uavs satisfy our objective, which arranges the n uavs on a convex hull’s vertex. In this study, we present a deep reinforcement learning-based method for building swarm uavs with convex hull patterns in the euclidean plane. The existing research on convex hull pattern generation uses heuristic and combinatorial optimization techniques and evaluates performance in terms of time and space consumed. Convex hull patterns increase the mutual visibility of swarm UAVs, which helps in the cooperation of UAVs. In our research for the first time, convex hull pattern formation around a given target point is accomplished using PPO-based Deep Reinforcement Learning (DRL).

I. Introduction

Unmanned Aerial Vehicles (UAVs), also known as drones, refer to aircraft with no human pilot on board. These drones are either programmed and fully autonomous or remotely controlled from another location, like a ground or space station. With rapid advancement in the electronics and sensor technology sectors, small-size UAVs are becoming more popular in many public and civilian applications, such as in search and rescue operations [54], aerial surveillance [55], tracking targets [56], agriculture field monitoring [57], network extension [58], and recreational activities. The use of swarms of small UAVs has many benefits compared to a single and large UAV [59]. Swarms of UAVs can automatically reconfigure themselves in case of node failure or communication link break-up and can easily accomplish the designated task, which is not possible in the case of a single UAV system. Swarm of UAVs present many advantages, an important aspect to be considered when designing an application using multiple UAVs is establishing the communication network, which poses many challenging issues, as described in [60], [61]. Based on the requirement of the applications UAVs may be semi-stationary and hovering over the area of operations or move around at high speed by changing their relative positions and if relative position changes then swarm drone topology changes also occurred, which may lead to network partitioning and poor link quality.