GiAnt: A Bio-Inspired Hexapod for Adaptive Terrain Navigation and Object Detection

Abstract

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 deep inside ant's natural adaptability to a variety of terrains. This bio-inspired approach gives it a significant advantage in outdoor applications having 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 4-wheels for motion, its legged design gives super 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 done 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, surveying offering unique advantages in adaptability and control simplicity.