Autonomous Trajectory Optimization for UAVs in Disaster Zone Using Henry Gas Optimization Scheme

TL;DR

This paper introduces a novel Henry gas optimization-based scheme for UAV trajectory planning in disaster zones, demonstrating superior performance over existing metaheuristics across various complex scenarios.

Contribution

The paper proposes a new cluster optimization scheme using Henry gas optimization for UAV trajectory planning, outperforming existing algorithms in complex environments.

Findings

HGO algorithm reduces transportation cost by up to 39.3%.

HGO algorithm decreases computational time by 16.8%.

HGO outperforms PSO, GWO, CSA, and BMO in all tested scenarios.

Abstract

The unmanned aerial vehicles (UAVs) in a disaster-prone environment plays important role in assisting the rescue services and providing the internet connectivity with the outside world. However, in such a complex environment the selection of optimum trajectory of UAVs is of utmost importance. UAV trajectory optimization deals with finding the shortest path in the minimal possible time. In this paper, a cluster optimization scheme (COS) is proposed using the Henry gas optimization (HGO) metaheuristic algorithm to identify the shortest path having minimal transportation cost and algorithm complexity. The mathematical model is designed for COS using the HGO algorithm and compared with the state-of-the-art metaheuristic algorithms such as particle swarm optimization (PSO), grey wolf optimization (GWO), cuckoo search algorithm (CSA) and barnacles mating optimizer (BMO). In order to prove the robustness of the proposed model, four different scenarios are evaluated that includes ambient environment, constrict environment, tangled environment, and complex environment. In all the aforementioned scenarios, the HGO algorithm outperforms the existing algorithms. Particularly, in the ambient environment, the HGO algorithm achieves a 39.3% reduction in transportation cost and a 16.8% reduction in computational time as compared to the PSO algorithm. Hence, the HGO algorithm can be used for autonomous trajectory optimization of UAVs in smart cities.