Autonomous Delivery Drone or Vehicle Fleet Managerial System

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 17 | 03 | Page :
By

Palavi Chaudhari,

Harshda Raghuawanshi,

  1. U.G Student, Department of Computer Engineering, Trinity College of Engineering and Research, Maharashtra, India
  2. Assistant Professor, Department of Computer Engineering, Trinity College of Engineering and Research, Maharashtra, India

Abstract

This review presents a clear conceptual framework for an autonomous delivery drone / vehicle fleet managerial system, emphasizing Artificial Intelligence (AI), Machine Learning (ML), Electrical and Electronics Engineering, and operational safety. The proposed architecture receives delivery requests, checks payload and route feasibility, assigns a suitable drone or ground vehicle, predicts travel time and energy demand, monitors missions through telemetry, and re-plans when traffic, weather, battery state, or vehicle availability changes. AI and optimization techniques can support demand forecasting, estimated-time-of-arrival prediction, task assignment, route selection, anomaly detection, and preventive maintenance. Meanwhile, deterministic safety rules remain responsible for enforcing essential limits, including geofencing, minimum battery reserves, and emergency stop conditions. The paper also explains the sensing, localization, communication, battery-management, motor-control, and edge-computing hardware that connects fleet-management software to physical vehicles. Furthermore, it discusses the importance of coordinating vehicle availability, monitoring mission progress, and responding to unexpected operational conditions to maintain reliable delivery services. A phased deployment approach is recommended, beginning with simulation and supervised pilot testing before progressing toward larger-scale autonomous operations. The review also highlights the need to consider cybersecurity, communication reliability, energy consumption, regulatory requirements, and human oversight throughout system development and deployment. By integrating intelligent decision- making with dependable electronic hardware and clearly defined safety procedures, the proposed framework aims to support efficient and reliable fleet operations. Overall, an effective fleet manager should balance delivery speed with energy efficiency, reliability, cybersecurity, regulatory constraints, and human supervision rather than treating autonomy as a purely algorithmic problem.

Keywords: Autonomous Delivery, Fleet Management, Delivery Drone, Artificial Intelligence, Battery Management, Last-Mile Logistics

How to cite this article: Palavi Chaudhari, Harshda Raghuawanshi. Autonomous Delivery Drone or Vehicle Fleet Managerial System. Journal of Control & Instrumentation. 2026; 17(03):-.
How to cite this URL: Palavi Chaudhari, Harshda Raghuawanshi. Autonomous Delivery Drone or Vehicle Fleet Managerial System. Journal of Control & Instrumentation. 2026; 17(03):-. Available from: https://journals.stmjournals.com/joci/article=2026/view=261127

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Ahead of Print Subscription Review Article
Volume 17
03
Received 12/10/2026
Accepted 15/10/2026
Published 21/10/2026
Publication Time 9 Days

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