An Integrated Route and Path Planning Strategy for Skid--Steer Mobile Robots in Assisted Harvesting Tasks with Terrain Traversability Constraints

Overview

This project introduces a combined route and path planning strategy for guiding autonomous ground vehicles during scheduled harvesting tasks. The strategy is designed to handle expansive crop rows and complex terrain conditions of agricultural fields. The proposed planning strategy integrates:

1. A global planning method based on the Traveling Salesman Problem (TSP) under the Capacitated Vehicle Routing approach. This method prioritizes harvesting positions based on criteria such as minimum path length and vehicle's load capacity.

2. A local planning strategy based on Informed Rapidly-exploring Random Trees (IRRT*). This strategy coordinates the previously scheduled harvesting points while simultaneously avoiding obstacles of low-traction terrain.

The global approach aims to generate an ordered queue of harvesting locations, maximizing the harvested product of the expected crop yield. In the second stage, the informed RRT* planner avoids obstacles around the configuration space of the robotic vehicle. It incorporates a dynamical model of the vehicle motion dynamics to meet constraints compatible with those of the planned path. Experimental results demonstrate the effectiveness of the proposed planning strategy, achieving smooth and collision-free paths, making it suitable for practical applications in precision agriculture.

Getting Started

Clone the repository using the following command:

git clone https://github.com/rickyurvina/path_planning_python_coppelia.git

Prerequisites

Ensure the following are installed before starting:

• CoppeliaSim (Version 4.5.1)
• Python (Version 3.9)
• MySql (Version 8.0.26)

Installation

1. CoppeliaSim:

   • Download CoppeliaSim from the official website.
   • Follow the installation instructions provided in the documentation.

2. Python and Dependencies:

   • Install Python > 3.9 from python.org.
   • Install the required dependencies by running the following command:

     pip3 install -r requirements.txt

3. MySql:

   • Install MySql from the official website.
   • Follow the installation instructions provided in the documentation.

CoppeliaSim Map

1. Open CoppeliaSim.
2. Open the file environment_harvesting_test.ttt from the coppelia_map folder.

Considerations

• The map must have a working plane of 1x15 meters.
• If you want to run the application online, obtaining the map from Coppelia, you must change the value of the ON_LINE variable to 1 in the config.py file.
• If you want to run the application offline, obtaining the map from a file, you must change the value of the ON_LINE variable to 0 in the config.py file.

Python Environment Configuration

1. Create a virtual environment:

   python3 -m venv venv

2. Activate the virtual environment:

   source venv/bin/activate

3. Install the required dependencies:

   pip3 install -r requirements.txt

4. Deactivate the virtual environment:

   deactivate

Database Creation

1. Update the MySql variables in the src/steps/config.py file according to your MySql configuration.
2. Use the create_database.py script from the steps folder to create the database:

   python3 src/steps/create_database.py

Solutions Folder

1. Create a folder named solutions in the root of the project. This folder will store the solutions of the TSP and RRT algorithms.
2. Move the solution_2049_07052024 folder to the solutions folder. If you generate new solutions, you can update the SOLUTION constant in the config.py file to point to the new folder.

Running the Program

Use the app.py script from the steps folder to execute the program:

python3 src/steps/app.py

Running in Parts

1. Use the step_1_get_data.py script from the steps folder to obtain the processed map from Coppelia. Ensure that the ON_LINE variable in steps/config.py is set to 1, and that the connectionPort in components/step1_get_data.py matches your Coppelia port. Load the map located in coppelia_map/environment_harvestng_test.ttt in CoppeliaSim. The simulation in Coppelia starts automatically:

   python3 src/steps/step_1_get_data.py

2. Use the step_2_tsp.py script from the steps folder to execute the TSP algorithm. You can run this file online or offline. If running online, ensure Coppelia is open with the map loaded. If running offline, set the ON_LINE variable in config.py to 0. If you want to run the TSP step offline, ensure that the solutions folder contains the app_variables1.pickle file. In config.py, you can adjust the VEHICLE_CAPACITIES and START_POINT_TSP values:

   python src/steps/step_2_tsp.py

3. Use the step_3_rrt.py script from the steps folder to execute the RRT algorithm. You can run this file online or offline. If running online, ensure the ON_LINE variable in config.py is set to 1 and that Coppelia is running with the map open. If running offline, set the ON_LINE variable in config.py to 0. This will load the solution from the solutions folder, using the SOLUTION constant value from config.py. If running offline, the path will be generated with the TSP Solution saved in the solutions folder. In the main function of step_3_rrt.py, you can uncomment the line of the different RRT methods for comparison. In config.py, you can adjust various parameters:

   python src/steps/step_3_rrt.py

Notes

This project was carried out at the Universidad Católica del Norte of Antofagasta in the Department of Systems and Computer Engineering (DISC).

If you wish to use this code in your work, please cite our paper available at this link DOI: 10.3390/agriculture14081206.