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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Path Following for Autonomous Mobile Robots with Deep Reinforcement Learning.

Yu Cao¹, Kan Ni¹, Takahiro Kawaguchi¹

¹Program of Intelligence and Control, Cluster of Electronics and Mechanical Engineering, School of Science and Technology, Gunma University, 1-5-1 Tenjin-cho, Kiryu 376-8515, Japan.

Sensors (Basel, Switzerland)

|January 23, 2024

Summary

This summary is machine-generated.

This study enhances autonomous mobile robot path following by combining pure pursuit steering with deep reinforcement learning for adaptive velocity control. The novel approach improves path convergence and velocity adjustments for robots with nonholonomic constraints.

Keywords:

autonomous mobile robot deep reinforcement learning path following soft actor-critic velocity control

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Area of Science:

Robotics and Autonomous Systems
Artificial Intelligence
Control Theory

Background:

Autonomous mobile robots are essential in various applications, with path following being a core capability.
Current path-following methods often lack generality due to limited velocity control or reliance on path-specific speeds.

Purpose of the Study:

To develop a more general and robust path-following method for nonholonomic mobile robots.
To integrate traditional algorithms with deep reinforcement learning for enhanced autonomous navigation.

Main Methods:

A novel approach combining the pure pursuit algorithm for steering control with the soft actor-critic deep reinforcement learning algorithm for velocity control.
Training the velocity control strategy in environments with randomly generated paths.
Validation through simulation and experimental testing.

Main Results:

Demonstrated significant improvements in path convergence compared to existing methods.
Achieved adaptive velocity adjustments for paths with varying curvatures.
Validated the approach's effectiveness on a nonholonomic mobile robot.

Conclusions:

The integrated pure pursuit and deep reinforcement learning approach enhances path-following robustness for nonholonomic robots.
This method offers improved path convergence and adaptive velocity control, increasing generality.
The approach shows potential for broader application to other nonholonomic systems.