Automatic Navigation and Voice Cloning Technology Deployment on a Humanoid Robot

TL;DR

This paper presents the development and implementation of an automatic navigation system for a humanoid robot using SLAM, path planning, and obstacle avoidance, along with a voice cloning application for enhanced service capabilities.

Contribution

It introduces a control algorithm for humanoid robot navigation in real environments and integrates voice cloning and chatbot functionalities for service applications.

Findings

Successful implementation of navigation algorithms on Cruzr robot

Comparison showing MPC outperforms DWA in navigation tasks

Voice cloning and chatbot functionalities are effectively deployed on the robot

Abstract

Mobile robots have shown immense potential and are expected to be widely used in the service industry. The importance of automatic navigation and voice cloning cannot be overstated as they enable functional robots to provide high-quality services. The objective of this work is to develop a control algorithm for the automatic navigation of a humanoid mobile robot called Cruzr, which is a service robot manufactured by Ubtech. Initially, a virtual environment is constructed in the simulation software Gazebo using Simultaneous Localization And Mapping (SLAM), and global path planning is carried out by means of local path tracking. The two-wheel differential chassis kinematics model is employed to ensure autonomous dynamic obstacle avoidance for the robot chassis. Furthermore, the mapping and trajectory generation algorithms developed in the simulation environment are successfully implemented on the real robot Cruzr. The performance of automatic navigation is compared between the Dynamic Window Approach (DWA) and Model Predictive Control (MPC) algorithms. Additionally, a mobile application for voice cloning is created based on a Hidden Markov Model, and the proposed Chatbot is also tested and deployed on Cruzr.