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Updated: Jun 28, 2025

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

Improved Hybrid Model for Obstacle Detection and Avoidance in Robot Operating System Framework (Rapidly Exploring

Ndidiamaka Adiuku¹, Nicolas P Avdelidis¹, Gilbert Tang²

¹Integrated Vehicle Health Management Centre (IVHM), School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire MK43 0AL, UK.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This summary is machine-generated.

This study enhances mobile robot navigation using machine learning and robotics. The NAV-YOLO system integrates YOLOv7 for obstacle detection and RRT for path planning, improving safety and efficiency in dynamic environments.

Keywords:

autonomous navigation deep learning mobile robot object detection obstacle avoidance vision

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Area of Science:

Robotics
Machine Learning
Computer Vision

Background:

Real-world robot navigation faces challenges in dynamic and unpredictable environments.
Existing hybrid methods with ROS navigation stacks struggle with real-time performance in changing conditions.
Precision in obstacle detection and avoidance control is crucial for safe and efficient robot operation.

Purpose of the Study:

To present a novel solution for enhancing mobile robot navigation in complex, dynamic environments.
To improve the real-time performance and safety of robot navigation systems.
To leverage advanced object detection and path-planning algorithms for superior navigation capabilities.

Main Methods:

Integration of a pre-trained YOLOv7 object detection model for accurate obstacle identification.
Combination with a rapidly exploring random tree (RRT)-integrated Robot Operating System (ROS) navigation stack.
Utilizing the dynamic windows approach (DWA) for efficient path planning and obstacle avoidance.

Main Results:

The NAV-YOLO system demonstrated high-level obstacle avoidance capabilities in simulations and real-world experiments.
Improved navigation performance in complex and dynamically changing settings.
Enhanced safety and efficiency of mobile robot operations, particularly noted in aviation environments.

Conclusions:

The proposed approach effectively addresses the challenges of mobile robot navigation in dynamic environments.
The integration of YOLOv7 and RRT-based ROS navigation significantly enhances robot safety and efficiency.
This solution offers a promising advancement for industrial mobile robot applications.