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

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Published on: October 14, 2017

Autonomous Navigation by Mobile Robot with Sensor Fusion Based on Deep Reinforcement Learning.

Yang Ou^1,2, Yiyi Cai^1,2,3, Youming Sun^1,2

¹School of Computer and Electronic Information, Guangxi University, Nanning 530004, China.

Sensors (Basel, Switzerland)

|June 27, 2024

Summary

This study introduces a novel deep reinforcement learning approach for mobile robot navigation in unknown environments. The method enhances exploration and path planning, outperforming traditional algorithms.

Keywords:

autonomous navigation deep reinforcement learning mobile robots sensor data fusion

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Conventional mobile robot navigation struggles in unknown environments due to reliance on predefined maps and rules.
Deep reinforcement learning (DRL) offers a promising alternative for complex navigation tasks.

Purpose of the Study:

To develop and evaluate a self-exploration and navigation strategy for mobile robots in unfamiliar environments using DRL.
To address the limitations of traditional path-planning algorithms in dynamic and unmapped spaces.

Main Methods:

Fused sensor data (lidar, camera, odometer) and target coordinates to define the robot's state.
Employed a deep neural network to process fused inputs and generate motion control strategies.
Integrated a novel heuristic function for local planning, synthesizing map information and global objectives.

Main Results:

The DRL-based approach demonstrated effective self-exploration and navigation capabilities.
The proposed method showed superior performance compared to existing navigation techniques in complex, unknown environments.
Successful progressive guidance of the robot towards its global target point was achieved.

Conclusions:

The developed DRL framework provides a robust solution for mobile robot navigation in unmapped and intricate settings.
This approach overcomes the limitations of map-dependent navigation systems.
The findings highlight the potential of DRL for autonomous robot navigation in real-world applications.