RGSO-UAV: Reverse Glowworm Swarm Optimization inspired UAV path-planning in a 3D dynamic environment

Three-dimensional path planning for UAVs is a very complex, NP-hard optimization problem. It is an effort to resolve the best feasible trajectory between the source and the destination while prohibiting collision in a dynamic environment. Many researchers have proposed different deterministic and non-deterministic solutions to the path-planning problem. However, it is still open to new promising methods to acquire better quality solutions with minimum time complexity. This article presents a novel methodology inspired by the Reverse Glowworm Swarm Optimization (RGSO) algorithm to resolve this problem for the UAV without colliding with any dynamic and static obstructions appearing in its path. A tuned path is generated after the initial one to optimize further and smoothen the trajectory. The proposed method has been extensively experimented with, and the outcomes show it outperforms the other algorithm in a three-dimensional dynamic environment in a path-planning strategy. The result shows that our proposed scheme reduces the value of the cost function by approximately 49% - 107% less than other existing approaches, and the execution time of this mechanism reduces by 2% - 61% compared with other existing methods. Thus, the proposed technique RGSO-UAV is an optimal and energy-efficient path-planning model in flying ad-hoc networks that enhance UAV's lifetime.