This study presents a new combinable multi-legged modular robot that mimics the structures of ants to expand the physical capabilities of the legged robot. To do this, the robot design is focused on exploring a fusion of two robotic platforms, modular and multi-legged, in which both the body frame and the legged structure are designed to be a rectangular prism and a 3-DoF sprawling-type articulated leg structure, respectively. By imitating ants’ claws, the hook-link structure of the robot as the coupling mechanism is proposed. This study includes the platform’s development, and the experimental work on the locomotion in both single and combined modes is carried out. The result of this study proves that mimicking ants’ structure in the proposed robots successfully enhances the capability of the conventional legged robot. It is feasible to use in a multi-robot system to realize ants’ super-organized behavior.
To generate stable walking of a quadruped, the complexity of the configuration of the robot involves a significant amount of optimization that decreases to its time efficiency. To address this issue, a machine learning method was used to build a simplified control policy using joint models for the supervised training of quadruped robots. This study considered 12 joints for a four-legged robot, and each joint value was determined based on the conventional method of walking simulation and prepossessed, equaling 2508 sets of data. For data training, the multilayer perceptron model was used, and the optimized number of epochs used to train the model was 5000. The trained models were implemented in robot walking simulations, and they improved performance with an average distance error of 0.0719 m and a computational time as low as 91.98 s.
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