Maneuverable Autonomy of a Six-legged Walking Robot: Design and Implementation using Deep Neural Networks and Hexapod Locomotion

Abstract

Automatically real-time synthesizing behaviors for a six-legged walking robot pose several exciting challenges, which can be categorized into mechanics design, control software, and the combination of both. Due to the complexity of control-ling and automation, numerous studies choose to gear their attention to a specific aspect of the whole challenge by either proposing valid and low-power assumption of mechanical parts or implementing software solutions upon sensorial capabilities and camera. Therefore, a complete solution associating both mechanical moving parts, hardware components, and software encouraging generalization should be adequately addressed. The architecture proposed in this article orchestrates (i) interlocutor face detection and recognition utilizing ensemble learning and convolutional neural networks, (ii) maneuverable automation of six-legged robot via hexapod locomotion, and (iii) deployment on a Raspberry Pi, that has not been previously reported in the literature. Not satisfying there, the authors even develop one step further by enabling real-time operation. We believe that our contributions ignite multi-research disciplines ranging from IoT, computer vision, machine learning, and robot autonomy.