ISAR - Integration and Supervisory control of Autonomous Robots - is a tool for integrating robot applications into operator systems. Through the ISAR API you can send commands to a robot to do missions and collect results from the missions.
Steps:
- Install
- Integrate a robot
- Run the ISAR server
- Run a robot mission
For local development, please fork the repository. Then, clone and install in the repository root folder:
git clone https://github.com/equinor/isar
cd isar
pip install -e .[dev]
For zsh
you might have to type ".[dev]"
Verify that you can run the tests:
pytest .
To connect the state machine to a robot in a separate repository, it is required that the separate repository implements the robot interface. A mocked robot can be found in this repository. Install the repo, i.e:
pip install git+https://@github.com/equinor/isar-robot.git@main
Then, ensure the ISAR_ROBOT_PACKAGE
variable in settings.env
is set to the name of the package you installed. isar_robot
is set by default. See the section
for configuration for overwriting configuration.
If you have the robot repository locally, you can simply install through
pip install -e /path/to/robot/repo/
A simulator based on the open source robot Turtlebot3 has been implemented for use with ISAR and may be
found here. Follow the installation instructions for the simulator and
install isar-turtlebot
in the same manner as given in the robot integration section. Overwrite
the following configuration variables:
ISAR_ROBOT_PACKAGE = isar_turtlebot
ISAR_DEFAULT_MAP = turtleworld
To run ISAR:
python main.py
Note, running the full system requires that an implementation of a robot has been installed. See this section for installing a mocked robot or a Turtlebot3 simulator.
Once the application has been started the swagger site may be accessed at
http://localhost:3000/docs
Execute the /schedule/start-mission
endpoint with mission_id=1
to run a mission.
In this folder there are predefined default missions, for example the mission
corresponding to mission_id=1
. A new mission may be added by adding a new json-file with a mission description. Note,
the mission IDs must be unique.
ISAR may be started with an instance of the isar-robot package by
docker-compose up --build
Provided that the simulator from isar-turtlebot is running ISAR may be started with the turtlebot by
docker-compose -f docker-compose-turtlebot.yml up --build
The system consists of many configuration variables which may alter the functionality. As an example, it is possible to change mission planners or add multiple storage handlers as described in the mission planner and storage sections.
There are two methods of specifying configuration.
-
Override the default value by setting an environment variable.
Every configuration variable is defined in settings.py, and they may all be overwritten by specifying the variables in the environment instead. Note that the configuration variable must be prefixed with
ISAR_
when specified in the environment. So for theMISSION_PLANNER
configuration variable:export ISAR_MISSION_PLANNER=echo
This means ISAR will use the
echo
mission planner module. -
Adding environment variables through settings.env.
By adding environment variables with the prefix
ISAR_
to the settings.env file the configuration variables will be overwritten by the values in this file.
After following the steps in Development, you can run the tests:
pytest .
To create an interface test in your robot repository, use the function interface_test
from robot_interface
. The
argument should be an interface object from your robot specific implementation.
See isar-robot for example.
Integration tests can be found here and have been created
with a simulator in mind. The integration tests will not run as part of pytest .
or as part of the CI/CD pipeline. To
run the integration tests please follow the instructions in this section for
setting up the isar-turtlebot
implementation with simulator and run the following command once the simulation has been
launched.
pytest tests/integration
Note that these tests will run towards the actual simulation (you may monitor it through Gazebo and RVIZ) and it will take a long time.
To build the project documentation, run the following commands:
cd docs
make docs
The documentation can now be viewed at docs/build/html/index.html
.
We welcome all kinds of contributions, including code, bug reports, issues, feature requests, and documentation. The preferred way of submitting a contribution is to either make an issue on GitHub or by forking the project on GitHub and making a pull requests.
The system consists of two main components.
- State machine
- FastAPI
The state machine handles interaction with the robots API and monitors the execution of missions. It also enables interacting with the robot before, during and after missions.
The state machine is based on the transitions package for Python. The main states are:
- Idle: The robot is in an idle state and ready to perform new missions.
- InitiateStep: The state machine has received a mission and initiates a step for the robot.
- Monitor: The robot has received a mission from the state machine, and the state machine is monitoring the current mission.
- Finalize: The state machine has finished the mission, received a request to abort, or an event has occurred which requires the mission to be canceled. Once finished the state machine will return to idle.
The FastAPI establishes an interface to the state machine for the user. As the API and state machine are separate threads, they communicate through python queues. FastAPI runs on an ASGI-server, specifically uvicorn. The FastAPI-framework is split into routers where the endpoint operations are defined.
The mission planner that is currently in use is defined by the ISAR_MISSION_PLANNER
configuration variable. This can
be changed by overriding the configuration through an environment variable. The available options are
ISAR_MISSION_PLANNER = local
ISAR_MISSION_PLANNER = echo
By default, the local
planner is used.
You can create your own mission planner by implementing the mission planner interface and adding your planner to the selection here. Note that you must add your module as an option in the dictionary.
The storage modules that are used is defined by the ISAR_STORAGE
configuration variable. This can be changed by
overriding the configuration through an environment variable. It accepts a json encoded list and will use each element
in the list to retrieve the corresponding handler. The current options are
ISAR_STORAGE = '["local", "blob", "slimm"]'
Note that the blob
and slimm
options require special configuration to authenticate to these endpoints.
You can create your own storage module by implementing the storage interface and adding your storage module to the selection here. Note that you must add your module as an option in the dictionary.
The API has an option to include user authentication. This can be enabled by setting the environment variable
ISAR_AUTHENTICATION_ENABLED = true
By default, the local
storage module is used and API authentication is disabled. If using Azure Blob Storage a set of
environment variables must be available which gives access to an app registration that may use the storage account.
Enabling API authentication also requires the same environment variables. The required variables are
AZURE_CLIENT_ID
AZURE_TENANT_ID
AZURE_CLIENT_SECRET
ISAR is able to publish parts of its internal state to topics on an MQTT broker whenever they change. This is by default turned off but may be activated by setting the environment variable
ISAR_MQTT_ENABLED = true
The connection to the broker will be determined by the following configuration values which may all be overwritten through the environment.
ISAR_MQTT_USERNAME
ISAR_MQTT_HOST
ISAR_MQTT_PORT
In addition, the ISAR_MQTT_PASSWORD
environment variable should be available for connection to the broker. If username
and password is not specified both will default to empty strings.