MIGHTY: Hermite Spline-based Efficient Trajectory Planning

If you like this project, please consider starring ⭐ the repo!

Submitted to the IEEE Robotics and Automation Letters (RA-L)

Trajectory	Forest

Dynamic Obstacles	Long Flight

Fast Flight 1	Fast Flight 2

Dynamic Env 1	Dynamic Env 2

Paper

MIGHTY: Hermite Spline-based Efficient Trajectory Planning is available at https://arxiv.org/abs/2511.10822!

@article{kondo2025mighty,
      title={MIGHTY: Hermite Spline-based Efficient Trajectory Planning},
      author={Kota Kondo and Yuwei Wu and Vijay Kumar and Jonathan P. How},
      year={2025},
      eprint={2511.10822},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2511.10822},
}

Video

The full video is available at https://youtu.be/Pvb-VPUdLvg.

Interactive Demo

For an interactive demo of MIGHTY, please switch to the interactive_demo branch:

git checkout interactive_demo

and follow the setup instructions in the README of that branch.

Note: When forking this repository, unselect "Copy the main branch only" to include the interactive_demo branch.

---

Installation

MIGHTY has been tested on Ubuntu 22.04 with ROS 2 Humble.

Docker Installation (Recommended)

1. Install Docker

Follow the official Docker installation guide for Ubuntu.

2. Clone the Repository

mkdir -p ~/code/ws/src
cd ~/code/ws/src
git clone https://github.com/mit-acl/mighty.git
cd mighty/docker

3. Build the Docker Image

make build

# Or build without cache (useful when dependencies change)
make build-no-cache

4. Run Simulations

# Multi-agent simulation (default: 10 agents)
make run

# Multi-agent with custom number of agents
make run-multiagent NUM_AGENTS=5

# Single-agent Gazebo simulation
make run-gazebo

# Gazebo with custom goal
make run-gazebo GOAL_X=100 GOAL_Y=50 GOAL_Z=3

# Gazebo with different environment (default: hard_forest)
make run-gazebo ENV=easy_forest

# Interactive shell (for debugging)
make shell

Docker Make Targets Reference

Target	Description	Options
make build	Build the Docker image	-
make build-no-cache	Build without cache (forces fresh build)	-
make run	Run multiagent simulation (default: 10 agents)	-
make run-multiagent	Run multiagent with custom agent count	NUM_AGENTS=N (default: 10)
make run-gazebo	Run single-agent Gazebo simulation	GOAL_X, GOAL_Y, GOAL_Z (default: 305, 0, 3), ENV (default: hard_forest)
make shell	Open interactive shell for debugging	-

Useful Docker Commands

• Remove all caches:

  docker builder prune

• Remove all containers:

  docker rm $(docker ps -a -q)

• Remove all images:

  docker rmi $(docker images -q)

---

Native Installation

1. Clone the Repository

mkdir -p ~/code
cd ~/code
git clone https://github.com/mit-acl/mighty.git mighty_ws/src/mighty
cd mighty_ws/src/mighty

2. Run the Setup Script

./setup.sh

This automated script will:

• ✅ Install ROS 2 Humble (if not already installed)
• ✅ Install all system dependencies
• ✅ Import all repositories from mighty.repos at tested commits
• ✅ Build DecompROS2, Livox-SDK2, and livox_ros_driver2
• ✅ Build MIGHTY and all ROS dependencies
• ✅ Configure your ~/.bashrc for future use

Notes:

• You'll be prompted for sudo password once at the start
• Safe to re-run if something fails (skips already-installed components)
• After completion, run source ~/.bashrc to use MIGHTY immediately

3. Run Simulations

Use the unified simulation launcher script run_sim.py:

cd ~/code/mighty_ws

# Multi-agent simulation (default: 10 agents)
python3 src/mighty/scripts/run_sim.py --mode multiagent --setup-bash ~/code/mighty_ws/install/setup.bash

# Multi-agent with custom number of agents
python3 src/mighty/scripts/run_sim.py --mode multiagent -s ~/code/mighty_ws/install/setup.bash --num-agents 5

# Single-agent Gazebo simulation
python3 src/mighty/scripts/run_sim.py --mode gazebo -s ~/code/mighty_ws/install/setup.bash

# Gazebo with custom goal position
python3 src/mighty/scripts/run_sim.py --mode gazebo -s ~/code/mighty_ws/install/setup.bash --goal 100 50 3

# Gazebo with different environment
python3 src/mighty/scripts/run_sim.py --mode gazebo -s ~/code/mighty_ws/install/setup.bash --env easy_forest

# Gazebo with GUI enabled
python3 src/mighty/scripts/run_sim.py --mode gazebo -s ~/code/mighty_ws/install/setup.bash --gazebo-gui

All Simulation Options

--mode, -m          Required. 'multiagent' or 'gazebo'
--setup-bash, -s    Required. Path to setup.bash
--goal, -g          Goal position X Y Z for gazebo mode (default: 305 0 3)
--start, -p         Start position X Y Z for gazebo mode (default: 0 0 3)
--start-yaw         Start yaw in radians (default: 1.57)
--num-agents, -n    Number of agents for multiagent mode (default: 10)
--radius, -r        Circle radius for multiagent formation (default: 10)
--env, -e           Gazebo environment (default: hard_forest)
--ros-domain-id     ROS_DOMAIN_ID (default: 7)
--no-rviz           Disable RViz
--gazebo-gui        Enable Gazebo GUI
--dry-run           Print generated YAML without launching

---

Additional Information

Dependencies

All dependencies are version-controlled in mighty.repos:

• ROS 2 packages: acl-mapping, dynus_interfaces, gazebo_ros_pkgs, livox_laser_simulation_ros2, realsense_gazebo_plugin, uav_simulator
• DecompROS2: Decomposition utilities (requires decomp_util to build first)
• Livox-SDK2: Livox LiDAR SDK (non-ROS binary)
• livox_ros_driver2: Livox ROS 2 driver (uses custom build.sh)

Paper Benchmarking

The simple, complex, jerk weight sweep, and reference position/velocity benchmarking is available at:

https://github.com/kotakondo/GCOPTER

Bag Recording

python3 src/mighty/scripts/bag_record.py --bag_number 3

Goal Command Example

ros2 topic pub /NX01/term_goal geometry_msgs/msg/PoseStamped "{header: {stamp: {sec: 0, nanosec: 0}, frame_id: 'map'}, pose: {position: {x: 305.0, y: 0.0, z: 3.0}, orientation: {x: 0.0, y: 0.0, z: 0.0, w: 1.0}}}" --once