Adaptive Ruminant Optimization With LoRa-Based Communication for Formation Control of Multiple UAVs

Abstract

In a dynamic environment with mountains and hazardous peaks, avoiding collisions and maintaining the desired formation is a crucial problem. This paper addresses this problem by presenting a novel formation control strategy of a cluster of UAVs in three different scenarios. The first scenario is designed to test the designed algorithm and hence contains no obstacles. The second scenario introduces some obstacles in the form of mountains to see whether the proposed algorithm can avoid the obstacles while maintaining the formation. In the last scenario, all the UAVs join together in one big cluster and have to avoid the obstacles while maintaining the formation. To design the environment for the scenarios, this study uses graph theory. To address the aforementioned scenarios, this paper offers a novel strategy by integrating a bio-inspired algorithm called the Adaptive Ruminant Optimization Algorithm (AROA) with the Long Range (LoRa) communication to achieve the formation control of multiple UAVs. Initially, AROA offers the best agents of each of the swarm. Then, the proposed method helps choose the best agent to be the leader for each of the swarm. The leader of each swarm finds the best trajectory for each swarm. LoRa-based networking technique is used for the connectivity between the UAVs. In addition, this study uses basis splines (B-splines) to smooth the planned trajectories of UAVs. Lastly, simulations demonstrate the better convergence and efficiency of the designed strategy by comparing it with classic algorithms. The simulations also show that the proposed method successfully maintains formation control in all three scenarios.