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This study presents the construction of a Vietnamese voice recognition module and inverse kinematics control of a redundant manipulator by using artificial intelligence algorithms. The first deep learning model is built to recognize and convert voice information into input signals of the inverse kinematics problem of a 6-degrees-of-freedom robotic manipulator. The inverse kinematics problem is solved based on the construction and training. The second deep learning model is built using the data determined from the mathematical model of the system’s geometrical structure, the limits of joint variables, and the workspace. The deep learning models are built in the PYTHON language. The efficient operation of the built deep learning networks demonstrates the reliability of the artificial intelligence algorithms and the applicability of the Vietnamese voice recognition module for various tasks.

This paper presents a modular architecture for behavior-based mobile robot control systems using fuzzy logic. The behaviors are functionally classified into two separated modules concerning collision-free motion control and goaloriented path planning using topological maps. The applied fuzzy logic approach allows flexible and robust behaviors and behavior design in complex environments. The performance of the system is tested and demonstrated successfully through experiments in a real office environment using a qualitative bio-inspired optical flow perception.

This paper introduces a design process for developing a humanoid mobile robot, namely VieBot. Stemming from the design process, it is easy to adjust the design and extent robot behaviors to meet customer needs. Our key solution is to split the design into three modules related to robot behavior, kinematic computation and motion control. Base on the results from testbed module under comprehensive testing, the design is gradually improved to satisfy sophisticated requirement of assisting disabled people.