Development of an Autonomous Unmanned Ground Vehicle for Artistic Landscaping

As cities strive to become more sustainable and livable in the age of smart urban development, there is a tendency toward urban landscaping concepts that combine ecological benefits and esthetic appeal. Within this context, artistic landscaping, the deliberate spatial arrangement of plant species to create visual compositions, has emerged as a valuable aspect of modern urban green infrastructure. While cutting-edge Unmanned Ground Vehicle (UGV) development has primarily focused on large-scale precision agriculture, its potential for artistic and small-scale urban landscaping remains unexplored. Furthermore, integrating Internet of Things (IoT) technology into UGVs for autonomous seeding presents an interesting research point. Addressing these challenges, this paper introduces a compact design of an IoT-enabled UGV specifically for artistic landscaping applications. The system includes an effective full seeding mechanism with dedicated modules for soil digging, sowing, water spraying, and backfilling. These operational modules are coordinated using a microcontroller-based control system to ensure reliability and repeatability. Additionally, in this study, a web-based interface has been developed to support both autonomous and manual operation modes, allowing users to customize path planning for geometric seeding patterns as well as real-time monitoring. A fully functional prototype was built and tested under controlled conditions to confirm the core modules’ effectiveness. This development provides a practical solution for supporting the realization of smart and sustainable cities.

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Publication Details

Published
2025-11-07
DOI
https://doi.org/10.3390/ecsa-12-26509
Primary Topic
Smart Agriculture and AI
Type
article
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article

Development of an Autonomous Unmanned Ground Vehicle for Artistic Landscaping

Anton Royanto Ahmad, Rowida Meligy, Nariman E. Elbaly, Arafa S. Sobh et al.
Smart Agriculture and AI
article

Development of an Autonomous Unmanned Ground Vehicle for Artistic Landscaping

Anton Royanto Ahmad, Rowida Meligy, Nariman E. Elbaly, Arafa S. Sobh, Sherif A. Elatraby
article en

Abstract

As cities strive to become more sustainable and livable in the age of smart urban development, there is a tendency toward urban landscaping concepts that combine ecological benefits and esthetic appeal. Within this context, artistic landscaping, the deliberate spatial arrangement of plant species to create visual compositions, has emerged as a valuable aspect of modern urban green infrastructure. While cutting-edge Unmanned Ground Vehicle (UGV) development has primarily focused on large-scale precision agriculture, its potential for artistic and small-scale urban landscaping remains unexplored. Furthermore, integrating Internet of Things (IoT) technology into UGVs for autonomous seeding presents an interesting research point. Addressing these challenges, this paper introduces a compact design of an IoT-enabled UGV specifically for artistic landscaping applications. The system includes an effective full seeding mechanism with dedicated modules for soil digging, sowing, water spraying, and backfilling. These operational modules are coordinated using a microcontroller-based control system to ensure reliability and repeatability. Additionally, in this study, a web-based interface has been developed to support both autonomous and manual operation modes, allowing users to customize path planning for geometric seeding patterns as well as real-time monitoring. A fully functional prototype was built and tested under controlled conditions to confirm the core modules’ effectiveness. This development provides a practical solution for supporting the realization of smart and sustainable cities.

King Fahd University of Petroleum and Minerals (SA), Imam Mohammad ibn Saud Islamic University (SA), Nile University (EG), Helwan University (EG)
Openalex Percentile: Top 72%
Smart Agriculture and AI
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Development of an Autonomous Unmanned Ground Vehicle for Artistic Landscaping — Anton Royanto Ahmad, Rowida Meligy, et al. · (2025) | TGRS Research Map | TGRS