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ATRON: Autonomous Trash Retrieval for Oceanic Neatness

Please visit our website and paper for more information about this project.

[Website] [Paper]

This repo contains instructions for how to use the Isaac SIM simulation for the Atron USV autonomous debris collection vehicle.

Installation

mkdir -p ros2_ws/src
cd src
git clone https://github.com/RISC-NYUAD/ATRON
cd ../
rosdep install --from-path src --ignore-src -r -y
colcon build --symlink-install && source install/setup.bash
echo "source ~/ros2_ws/install/setup.bash" >> ~/.bashrc

Usage

  1. Run the simulation in Isaac Sim using the file in
isaac/atron_simulation.usd

isaac_sim.png

  1. Run the autonomous routine using:
ros2 launch triton_bringup simulation.launch.xml
  1. In another terminal, run
ros2 launch triton_navigation op_solver_bridge.launch.py

This will launch the planning algorithms, as create a link to the orienteering solver.

simulation.png

  1. Run the following in another terminal to run the Orienteering -> OMPL -> Pure pursuit navigation pipeline.
cd ~/ros2_ws/src/Triton/triton_navigation/config
ros2 service call /export_oplib std_srvs/srv/Trigger
./../../dependencies/op_solver_ros/op-solver/build/src/op-solver opt --op-exact 0 problem.oplib
ros2 service call /create_waypoints std_srvs/srv/Trigger
ros2 service call /generate_path std_srvs/srv/Trigger
ros2 service call /navigate std_srvs/srv/Empty

High-Fidelity Simulation

We also provide a high-fidelity simulation which includes improved visual and physical details. image

Buoyancy is simulated via force calculations on a height wavefunction. Movemement is implemented via thrust forces controlled via PID to execute the desired velocity. buoyancy

See the releases tab for the atron_highfidelity for the simulation file. The simulation can physically simulate sea states from 0 to 4 on the Beaufort Scale image

We also implement a correction step for the debris and obstacle localization process. Below are videos showing the before and after for the correction step.

Without the correction in a buoyancy environment (cylinders are for the obstacle class, spheres for the debris class): no_correction_short

With the correction: with_correction_short

The photorealistic environment also allows the training of object detection models, as seen below image

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