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Delivery Mobile Robot

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Agus SUKOCO

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Limited delivery alternatives make grocery and other company and office stock management systems inefficient. Security officers handle high volumes and recipients go far to retrieve items left at security stations. Inefficiencies slow workflows and reduce productivity. Manual interdepartmental deliveries delay and divert staff. Data-driven autonomous delivery robots with HMC solve the challenge with strong information processing. LiDAR and GNSS let these outdoor robots navigate and recognize impediments in real time (Ahmad, et al., 2023; Hossain, 2023; Madani & Ndiaye, 2022). Emergency interventions and route optimization are possible using HMC prediction models and decision-making systems.


Designing an autonomous delivery robot

Autonomous Delivery

An autonomous stock management delivery robot with enhanced HMC features is the goal of this project. Project uses data-driven technologies and AI-based decision-making to improve logistics, sustainability, and user productivity.

A reliable prototype under varied external conditions is the goal. Modern logistics systems like the Smart System-based Delivery Robot distribute things securely, efficiently, and sustainably throughout campuses and offices. Smart System 4-Layer was used to build this robot:

  1. Instrumentation—LiDAR, GNSS, and cameras for real-time environmental monitoring.
  2. IT architecture for route planning, status monitoring, and logistics reporting employing integrated data processing.
  3. AI uses machine learning for autonomous decision-making, risk detection, and environmental adaptation.
  4. Gamification—Enhancing user engagements like package tracking, interactive notifications, and feedback with an informative and entertaining interface.

Administration and System Control

  1. Bots automatically arrange their routes using environmental data and delivery destinations. Algorithms plan the fastest and safest route.
  2. Perception: The robot's perception and planning system uses road, obstruction, and pedestrian data. LiDAR, cameras, GNSS, and ultrasonic sensors survey the environment, detect obstacles, and locate the robot.
  3. To implement the plan, the control system controls speed, direction, and obstacle avoidance.

Support Systems

  1. Load Locker: Prevents delivery falls and theft.
  2. The Power System powers all components and stabilizes the system.

Actuators

Control system commands activate steering, acceleration, and braking actuators.

End-user interfaces

Users will send and receive via robot. A mobile app will notify of delivery status and robot location. It addresses logistical issues and shows smart technology and automation in daily life.


Bibliographies

Ahmad, A., et al. 2023. “A Review on Autonomous Delivery Robots.” In 2023 2nd International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS), Abuja, Nigeria, 1-6. https://ieeexplore.ieee.org/document/10429843

Hossain, M. 2023. “Autonomous Delivery Robots: A Literature Review.” IEEE Engineering Management Review 51 (4): 77-89. https://ieeexplore.ieee.org/abstract/document/10218729

Madani, B., and M. Ndiaye. 2022. “Hybrid Truck-Drone Delivery Systems: A Systematic Literature Review.” IEEE Access 10: 92854-92878. https://ieeexplore.ieee.org/document/9869811

Lee, J., G. Park, I. Cho, K. Kang, D. Pyo, S. Cho, M. Cho, and W. Chung. 2022. “Ods-bot: Mobile Robot Navigation for Outdoor Delivery Services.” IEEE Access 10: 107250–107258. https://ieeexplore.ieee.org/document/9913438

Nishida, K., and T. Nishi. 2022. “Dynamic Optimization of Conflict-Free Routing of Automated Guided Vehicles for Just-in-Time Delivery.” IEEE Transactions on Automation Science and Engineering 20 (3): 2099–2114. https://ieeexplore.ieee.org/document/9849477

Moshayedi, A. J., A. S. Roy, L. Liao, A. S. Khan, A. Kolahdooz, and A. Eftekhari. 2024. “Design and Development of FOODIEBOT Robot: From Simulation to Design.” IEEE Access 12. https://ieeexplore.ieee.org/document/10401885

Camisa, A., A. Testa, and G. Notarstefano. 2022. “Multi-Robot Pickup and Delivery via Distributed Resource Allocation.” IEEE Transactions on Robotics 39 (2): 1106–1118. https://ieeexplore.ieee.org/document/9954913

Gan, X., Z. Huo, and W. Li. 2023. “Dp-a*: For Path Planning of UGV and Contactless Delivery.” IEEE Transactions on Intelligent Transportation Systems 25 (1): 907–919. https://ieeexplore.ieee.org/document/10101684

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AuthorAgus SUKOCOAugust 15, 2025 at 2:49 AM

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Contents

  • Autonomous Delivery

  • Administration and System Control

  • Support Systems

  • Actuators

  • End-user interfaces

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