GiAnt: A Bio-Inspired Hexapod for Adaptive Terrain Navigation and Object Detection
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912594240995328 |
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| author | Bhuiyan, Aasfee Mosharraf Mehda, Md Luban Puspo, Md. Thawhid Hasan Pritom, Jubayer Amin |
| author_facet | Bhuiyan, Aasfee Mosharraf Mehda, Md Luban Puspo, Md. Thawhid Hasan Pritom, Jubayer Amin |
| contents | This paper presents the design, development and testing of GiAnt, an affordable hexapod which is inspired by the efficient motions of ants. The decision to model GiAnt after ants rather than other insects is rooted in ants' natural adaptability to a variety of terrains. This bio-inspired approach gives it a significant advantage in outdoor applications, offering terrain flexibility along with efficient energy use. It features a lightweight 3D-printed and laser cut structure weighing 1.75 kg with dimensions of 310 mm x 200 mm x 120 mm. Its legs have been designed with a simple Single Degree of Freedom (DOF) using a link and crank mechanism. It is great for conquering challenging terrains such as grass, rocks, and steep surfaces. Unlike traditional robots using four wheels for motion, its legged design gives superior adaptability to uneven and rough surfaces. GiAnt's control system is built on Arduino, allowing manual operation. An effective way of controlling the legs of GiAnt was achieved by gait analysis. It can move up to 8 cm of height easily with its advanced leg positioning system. Furthermore, equipped with machine learning and image processing technology, it can identify 81 different objects in a live monitoring system. It represents a significant step towards creating accessible hexapod robots for research, exploration, and surveying, offering unique advantages in adaptability and control simplicity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15264 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GiAnt: A Bio-Inspired Hexapod for Adaptive Terrain Navigation and Object Detection Bhuiyan, Aasfee Mosharraf Mehda, Md Luban Puspo, Md. Thawhid Hasan Pritom, Jubayer Amin Robotics This paper presents the design, development and testing of GiAnt, an affordable hexapod which is inspired by the efficient motions of ants. The decision to model GiAnt after ants rather than other insects is rooted in ants' natural adaptability to a variety of terrains. This bio-inspired approach gives it a significant advantage in outdoor applications, offering terrain flexibility along with efficient energy use. It features a lightweight 3D-printed and laser cut structure weighing 1.75 kg with dimensions of 310 mm x 200 mm x 120 mm. Its legs have been designed with a simple Single Degree of Freedom (DOF) using a link and crank mechanism. It is great for conquering challenging terrains such as grass, rocks, and steep surfaces. Unlike traditional robots using four wheels for motion, its legged design gives superior adaptability to uneven and rough surfaces. GiAnt's control system is built on Arduino, allowing manual operation. An effective way of controlling the legs of GiAnt was achieved by gait analysis. It can move up to 8 cm of height easily with its advanced leg positioning system. Furthermore, equipped with machine learning and image processing technology, it can identify 81 different objects in a live monitoring system. It represents a significant step towards creating accessible hexapod robots for research, exploration, and surveying, offering unique advantages in adaptability and control simplicity. |
| title | GiAnt: A Bio-Inspired Hexapod for Adaptive Terrain Navigation and Object Detection |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.15264 |