Ai-Driven Satellite-Assisted Route Optimization for Enhanced Mother-Daughter Drone Coordination in Efficient Parcel Delivery

Abstract

The fast-paced development in artifcial intelligence (AI), satellite communication, and autonomous aerial vehicles are transforming contemporary logistics and delivery systems. Conventional drone-based delivery systems which include single drone per trip have a number of challenges, such as restricted range, high energy consumption, and limited payload capacity. To cater these challenges, this study investigates an AI-based satellite-assisted mother daughter drone coordination system for the optimize of lastmile delivery operations. In this envisioned system, a giant mother drone serves as a carrier, sending out several smaller daughter drones to effectively deliver light packages to different locations and mother drone for heavy packages. The system uses AI based hybrid algorithms involving Mixed-Integer Linear Programming (MILP) and Genetic Algorithm (GA) AI, for route optimization real-time weather data to adaptively modify flight routes to decrease travel distance, reduce delivery time, reduce energy expenditure, and enhance delivery effciency. This study utilizes simulation-based approach using Python. The important metrics include delivery time, total distance, energy consumption, scalability, and feasibility. The possible outcome of this study is the creation of an AI-based Mother daughter drone delivery system that is much more effcient, adaptable, and sustainable than traditional single-drone delivery systems. The results of this study have broad implications for e-commerce logistics, medical supply chain distribution, emergency response, and smart city infrastructure, and thus represent a pioneering contribution to the future of autonomous aerial transport