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

2025-06-18

AI Summary

To address trajectory optimization for drone swarms in post-disaster complex environments, this paper proposes an autonomous cooperative planning method integrating Henry Gas Solubility Optimization (HGO) with a Cluster Optimization Scheme (COS), jointly minimizing transportation cost and computational overhead while ensuring shortest path length and flight time. This work pioneers the application of HGO to drone trajectory optimization and enhances population diversity and local search capability via COS, significantly improving convergence speed and robustness. Extensive simulations across four scenarios—typical, constrained, entangled, and highly complex—demonstrate superior performance over mainstream algorithms including PSO, GWO, CSA, and BMO: in typical environments, transportation cost is reduced by 39.3% and computational time by 16.8%. The results validate the method’s efficacy and practicality for emergency communication and rescue missions.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Constraint Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Optimizing UAV trajectory in disaster zones for rescue efficiency
• Reducing transportation cost and time using Henry Gas Optimization
• Comparing HGO with PSO, GWO, CSA, BMO in various environments

Innovation

• Uses Henry Gas Optimization for UAV pathfinding
• Reduces transportation cost and computational time
• Outperforms PSO, GWO, CSA, and BMO algorithms