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README.md

SpecGuard: Docker

We provide two separate Docker containers for the drone and the rover, each designed to install the necessary dependencies and autopilot software.

Setup

The Docker containers are designed to mount the environments, drone, and rover directories. This setup allows users to create and modify configuration files or environment settings outside the containers while seamlessly using them inside the containers.

  1. NVIDIA Container Toolkit: Install Docker and NVIDIA Container Toolkit. Installation instructions are here.
  2. Environment Binaries: Ensure that the environment binaries are in the environments directory. This allows the binaries to be shared across multiple container instances.
  3. Configuration: Place all configuration files, including custom AirSim settings.json files, in the appropriate directories - drone or rover directories.

Usage

Building the container

To build for each RAV type, use the following scripts:

./build_drone.sh
./build_car.sh

To customize the Docker image, edit DroneDockerfile or RoverDockerfile. Note that the modified autopilot firmware is pulled from the modified_ardupilot and modified_px4 directories within this repository.

Running the container

To run the drone, use the following script. You can optionally specify the GPU device for running the container.

./run_drone.sh <gpu_device>
./run_rover.sh <gpu_device>

To modify the directories or files that are mounted, update the run_rover.sh or run_drone.sh scripts.

Running the Deep-RL policy

To run the Deep-RL policy set the docker flag, -d, and specify a configuration file.

cd drone
python ppo_drone.py -d -c /path/to/config

Additional steps for rover: Before running the policy, build ArduPilot in the container:

cd ardupilot/
./waf configure --board sitl
./waf build --target bin/ardurover

cd rover
python ppo_rover.py -d -c /path/to/config

Make sure the configuration file includes the correct paths to your environment binaries.

Options

  • -c/--config path/to/config the path to your configuration file.
  • -n/--name agent_name the name of the trained policy.
  • -lr/--load-recovery to load a trained policy.
  • -la/--load-attacker to load a trained anomaly agent.
  • -v/--verbose print verbose output to console for debugging.
  • -e/--evaluate num_evaluation_episodes number of evaluation episodes.

Examples

python ppo_drone.py -d -c configs/NH/nh_gps.yaml
python ppo_rover.py -d -c configs/nh_gps.yaml

Output

The docker console will print the results:

ATTACKS: 100 | RECOVERIES: 94  | COLLISIONS: 0
  • ATTACKS: number of attacks launched
  • RECOVERIES: successful recoveries
  • COLLISIONS: collisions with obstacle or ground

The videos of the simulation runs with and without SpecGuard are available here https://tinyurl.com/specguard