A Tree-based Next-best-trajectory Method for 3D UAV Exploration
The ERRT framework is a combined Exploration-Planning algorithm for the exploration of unknown and unstructured 3D environments - in this version set up for rotorcraft UAVs. In the framework we leverage RRT tree-expansion for rapid path generation to multiple sampled candidate goals, combined with iterative path improvement modules and a NMPC to generate dynamic paths. Candidate RRT branches are then evaluated for distance, model-based actuation, and 3D LiDAR sensor based information gain along the candidate branches - not just at the end-goals or frontiers. The fundamental goal of ERRT is to find the momentary local "Next-best-trajectory" for continued and efficient exploration of unknown environments.
The framework heavily relies on the UFOmap occupancy mapper library and requires an UFOmap topic as input, together with the robot localization/odometry. As output the user can select to use a continously updated pose reference or the full trajectory to track.
The public version of the framework provides a pre configured Dockerfile here with all dependencies already configured including but not limited to: the UFOmap, the RotorS UAV simulator and trajectory tracker, and the NMPC optimizer. Instructions for installing docker and building the Dockerfile are provided below, and further down are detailed instruction on running the framework inside the errt docker container.
The docker container based simulation is pre-configured to load the DARPA SubT Cave World consisting of wide interconnected tunnels and caves, and the ERRT and UFOmap tuning is specified for this environment - and as such optimal performance in other environments might require parameter tuning. The provided version relies also on a full-tracjectory tracking controller provided by RotorS, for the UAV to track the generated and selected path.
The ERRT repository only requires docker. If you do not have docker installed, please follow the docker installation instructions. If you already have docker installed, you can skip this step.
# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl gnupg
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg
# Add the repository to Apt sources:
echo \
"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \
$(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update
sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin python3-pip
pip install gdown
The repository contains a Dockerfile that allows the user to build a docker image containing packages for exploration, planning, control and simulation environment.
Clone the ERRT project
git clone https://github.com/LTU-RAI/ExplorationRRT.git
Navigate to the ERRT directory and download cave models for gazebo. (This speeds up the world loading process in Gazebo)
cd ExplorationRRT/docker
gdown --id 1TDbXF9He_LXYY57Xo3tOxvEVYH3kPtS8
gdown --id 1y7BDt0tjK9Ml7MlTxUwOTG8ZygO_dpI0
unzip models.zip
unzip ignition_models.zip Build the docker image with following command. The build process might take some time when building for first time.
sudo docker build --build-arg USERNAME=$(whoami) -t errt_test .
This section will detail some of the critical launch parameters of interest for the user - focusing on baseline configuration params, and those that can have a large impact on using ERRT different environments. The relevant launch files launch/errt.launch, config/errt.yaml, and launch/server.launch (for UFOmap) has more details for every configuration parameter.
The Following ROS topic configurations can be found in the launch/errt.launch file.
remap from="odometry_in_" to="/hummingbird/ground_truth/odometry" - The robot odometry topic
remap from="ufomap_in_" to="ufomap_mapping_server_node/map" - The UFOmap topic. Maps at different depths can also be used.
remap from="reference_out_" to="/hummingbird/reference" - Momentary pose references along the trajectory. These are updated by the condition of the robot position being closer to the current reference than path_update_dist.
remap from="path_out_" to="/hummingbird/command/trajectory" - Path topic as a MultiDOFJointTrajectory message - Set up with the message type to be synced with the RotorS trajectory tracking controller.
There are also many visualization topics included in the ERRT program such as the tree_expansion (whole tree), candidate_goals, predicted_info_gain (predicted exploration), candidate_branches (all paths), and selected_trajectory. These are set up in the rviz/errt.rviz.
This section will detail a number of tuning parameters in config/errt.yaml and launch/server.launch (UFOmap). The related launch files detail all possible tuning and configuration parameters.
| Parameter | Description |
|---|---|
| resolution | Found in server.launch. The resolution of the UFOmap. |
| planning_depth | The depth of the Octree in UFOmap practically means merging voxels into larger ones. This significantly speeds up various volumetric occupancy checks. ERRT is configured to use a small resolution of ~0.05-0.15m but performing computationally demanding actions such as information gain calculations at a set depth in the Octree. |
| info_gain_depth | Similar to planning_depth, used for information gain calculations. |
| robot_size | Approximate size-radius of the robot, used in collision checks. |
| v_local | Side length of the bounding box for local sampling space in RRT. |
| number_of_nodes | Size of the RRT, determining when to stop tree expansion. |
| number_of_goals | Number of candidate goals and trajectories to investigate. Affects computation time. |
| sensor_range | Range of the LiDAR model for computing predicted information gain. Should be set lower than max_range in server.launch. |
| sensor_vertical_fov | Vertical cutoff angle for the LiDAR model, matching the onboard LiDAR on the robot. Example: Ouster 32-beam 45° FoV -> sensor_vertical_fov = 0.393 |
| info_calc_dist | Distance between nodes in candidate branches where information gain calculations are performed. Smaller values increase computation time. |
| k_dist | Gain related to the distance cost along candidate branches. |
| k_info | Gain related to the information gain along candidate branches. |
| k_u | Gain related to the actuation cost along candidate branches. |
| start_from_waypoint | true/false indicates if the UAV should travel to a specified initial coordinate before ERRT takes over navigation, set by subsequent x,y,z-coordinates. |
Run the docker container with NVIDIA flags. In the ERRT directory:
./start_errt_gpu.shIf you do not have NVIDIA GPU :
./start_errt_no_gpu.shOnce you are inside the docker container, please run the following command to start the ERRT tmux session. This session will launch the ERRT sub-modules and a Rviz window to visualize the drone exploring the cave environment.
tmuxinator errtIf you find the E-RRT framework useful in your research, please consider citing the E-RRT article.
@ARTICLE{10582913,
author={Lindqvist, Björn and Patel, Akash and Löfgren, Kalle and Nikolakopoulos, George},
journal={IEEE Transactions on Robotics},
title={A Tree-based Next-best-trajectory Method for 3D UAV Exploration},
year={2024},
volume={},
number={},
pages={1-18},
keywords={Robots;Robot sensing systems;Costs;Collision avoidance;Autonomous aerial vehicles;Trajectory;Three-dimensional displays;Tree-based Exploration;RRT;Subterranean Exploration;Field Robotics;Unmanned Aerial Vehicles},
doi={10.1109/TRO.2024.3422052}}
}