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Related Experiment Video

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Robotic Sensing and Stimuli Provision for Guided Plant Growth

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Published on: July 1, 2019

LiDAR-Only Crop Navigation for Symmetrical Robot.

Rémy Guyonneau¹, Franck Mercier¹, Gabriel Oliveira Freitas¹

¹LARIS (Laboratoire Angevin de Recherche en Ingénierie des Systèmes), SFR MATHSTIC, University of Angers, F-49000 Angers, France.

Sensors (Basel, Switzerland)

|November 26, 2022

Summary

This study introduces a new navigation system for autonomous agricultural robots using LiDAR data. The system effectively guides robots through crops, even with variations like weeds or gaps.

Keywords:

LiDAR measurements ROS/Gazebo crop navigation fuzzy controller line extraction

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Area of Science:

Robotics
Agricultural Engineering
Computer Vision

Background:

Autonomous agricultural robots require robust navigation systems for efficient field operations.
LiDAR (Light Detection and Ranging) technology offers precise environmental sensing capabilities.
Existing navigation methods may struggle with variable crop conditions.

Purpose of the Study:

To develop and evaluate a novel navigation approach for autonomous agricultural robots.
To enable robots to accurately follow crop rows using LiDAR data.
To create a system adaptable to diverse field scenarios.

Main Methods:

A two-part navigation approach combining line finding and control algorithms.
Development of line extraction algorithms from LiDAR data, inspired by the PEARL/Ruby approach.
Implementation of a fuzzy controller to generate wheel speed commands based on processed line data.
Testing and validation using a ROS (Robot Operating System) and Gazebo-based simulator.

Main Results:

The proposed navigation approach demonstrated effective performance in simulated environments.
The system successfully navigated robots through various crop configurations, including those with weeds and gaps.
The line finding and fuzzy control algorithms accurately guided the robot along crop rows.

Conclusions:

The developed LiDAR-based navigation system is a viable solution for autonomous agricultural robots.
The approach shows robustness and adaptability to different agricultural field conditions.
The open-source simulation code facilitates further research and development in agricultural robotics.