# Low-cost, efficient agricultural robotics solutions

Florian Gaurier, Dimia Iberraken, Jean-Christophe Roux, Roland Lenain

► To cite this version:

Florian Gaurier, Dimia Iberraken, Jean-Christophe Roux, Roland Lenain. Low-cost, efficient agricultural robotics solutions. WORLD FIRA 2023 - Forum international de la robotique agricole en action, Feb 2023, Toulouse, France., 2023. [https://hal.science/hal-04873679](https://hal.science/hal-04873679)

HAL Id: hal-04873679

https://hal.science/hal-04873679v1

Submitted on 8 Jan 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Low-cost, efficient agricultural robotics solutions

## Autonomous structure tracking, spraying and traversability analysis based on a 2D laser scanner

Florian Gaurier, Dimia Iberraken, Jean-Christophe Roux and Roland Lenain
University Clermont Auvergne, INRAE, UR 1471 TSCF, Aubière, France.

### Context

**Agricultural robotics** is a promising area of research that has the potential to increase crop yields, reduce labor costs, and improve sustainability. However, the development and implementation of agricultural robotics has been hindered by the **high cost and complexity** of these systems. Researchers are exploring ways to create low-cost, efficient agricultural robots. In line with this objective and in an effort to adopt an **ecological approach**, we sought in this paper to investigate all the potential applications of a **2D laser scanner** for various agricultural tasks coupled with **odometry** (providing relative position of the robot). These two sources of data enable structure following, agricultural task (spraying), 3D mapping (Point cloud) and traversability analysis for secure behavior of the robot.

**Keywords:** Agricultural Robotics, Laser scanner-based navigation, 4 Wheeled-Steered mobile robot, Row tracking, Spraying, Traversability.

<figure><img src="image_2.png" /></figure>

## Structure tracking strategy

### Structure detection based on pattern recognition

**Objective** Use an autonomous mobile robot to follow a path established by the vegetation structure

<figure><img src="image_3.png" /></figure>

**Principle**

* A row pattern (in red) is matched inside LiDAR signals, generating successive detection points of the followed row.

* A trajectory is formed with these points and is used to compute inputs for the robot control: angular & lateral deviations.

### Motion control based on Backstepping

* A two axle steering mobile robot is used to control the relative orientation and position of the robot.

* Control law will converge to a user specified lateral deviation.

<figure><img src="image_4.png" /><figcaption>Fig.1: Extended kinematic of the mobile robot(left) and experimental setup (right)</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig.2: Tracking errors and steering angles of associated experimental setup</figcaption></figure>

## Spraying strategy

### Based on density measurement of a 3D map of environment

**Objective**

Automate the activation of the spraying device according to the plant cover.

**Principle**

1. LiDAR data and estimated local position of the robot are used to build a 3D map of the environment.

<figure><img src="image_6.png" /></figure>

2. Four Regions of Interest (ROIs) based on mechanical specifications of the spraying device are generated and used to independently activate nozzle based on data density inside ROI.

3. Spraying precision is improved by anticipating vegetation to come using measured speed of the robot and nozzle activation delay.

<figure><img src="image_7.png" /><figcaption>Fig.3: Autonomous spraying at different times (above) and corresponding spraying ramp status within perceived environment (below)</figcaption></figure>

## Traversability analysis

**Objective** Retrieve and classify objects to insure a safe displacement according to the notion of obstacles

<figure><img src="image_8.png" /></figure>

### Ground Removal - Grid based strategy

**Step 1 - Grid Generation:** 3D map is matched on a 2D grid of cells.

**Step 2 - First criterion** Cells containing data above a **threshold condition** (i.e. wheel diameter) are considered as non-ground cells. Others are considered as ground cells.

**Step 3 - Second criterion** Cell data are reclassified based on a size condition of a hole between data inside the cell w.r.t Z-axis. It allows to account for large canopy.

<figure><img src="image_9.png" /><figcaption>Fig.4: Ground removal principle</figcaption></figure>

**Step 4 - Third criterion** Same objects overfilling multiple cells are handled by computing a condition continuity between neighboring cells data. It allows to account for bent trunk.

### Segmentation and classification of objects

<figure><img src="image_10.png" /><figcaption>Fig.5: Classification principle</figcaption></figure>

**Initialisation**

- Histograms are computed on Y-axis data.

- The position of nearest row is retrieved with respect to the lateral axis of the robot.

- The expected foliage thickness and inter-row distance are assumed to be known

**Three steps of classification**

1. Histogram Based Clustering (HBC) classifies roughly rows using the expected foliage thickness and inter-row distance.

2. Conditional Euclidean Clustering (CEC) grows previously retrieved rows.

3. Fast CEC (FCEC) classifies potential obstacles on remaining non-row data.

<figure><img src="image_11.png" /><figcaption>Fig.6: Simulated vine environment</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig.7: Classification result on simulated vine.
Ground in brown, obstacle in grey, retrieved rows in other colors.</figcaption></figure>

### Obstacle avoidance - Feedback on motion control

1. A intersection between each obstacle and robot bounding box is computed on Y-axis (robot lateral axis) and Z-axis.

2. If an intersection is found, a stop signal is send to robot.

3. The overall process works in reality and simulated vine environment.

<figure><img src="image_13.png" /><figcaption>Fig.8: Speed measurement & command</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig.9: Corresponding perceived environment with bounding boxes</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig.10: Robot stopping in front of obstacle</figcaption></figure>

## Perspectives and future work

Future works will deepen the perceptive abilities of the robot to adapt its behavior to the perceived environment in order to ensure high safety and precision during the agricultural task. These works will be at the center of the recently financed ANR project called "AgriVia" created between INNODURA TB and INRAE.