Package Summary

Tags No category tags.
Version 1.4.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/open-rmf/rmf_ros2.git
VCS Type git
VCS Version galactic
Last Updated 2021-09-02
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

Python bindings for the rmf_fleet_adapter

Additional Links

No additional links.

Maintainers

  • methylDragon
  • Marco A. Gutiérrez

Authors

No additional authors.

rmf_fleet_adapter_python

Python bindings for rmf_fleet_adapter

Introduction

The rmf_fleet_adapter package defines C++ classes that can interact with rmf_core. Specifically an Adapter and a MockAdapter class. This package provides bindings for both and most of their dependencies.

The main difference between rmf_fleet_adapter and rmf_mock_adapter is the fact that rmf_fleet_adapter objects can interact with the ROS2 graph.

The MockAdapter and Adapter both do this. But the difference is that MockAdapter can be instantiated with no issues, whereas Adapter will block to wait for a scheduler node from rmf_core to make its presence known.

Installation

  • Place the pybind_ament and rmf_fleet_adapter_python packages in your ROS2 workspace, and run colcon build as per normal.

Running Examples

Integration test script for rmf loop and delivery task:

ros2 run rmf_fleet_adapter_python test_adapter.py

You may then probe the effects on the ROS2 graph by subscribing to the following topics with ros2 topic echo <TOPIC_NAME>: - : /dispenser_requests, /dispenser_results, ingestor_requests, ingestor_results, /task_summaries

Traffic Light Example

This will showcase an example of having 2 "traffic light" robots using RMF.

# First Terminal
ros2 run rmf_fleet_adapter_python schedule_blockade_nodes

# Second Terminal
ros2 run rmf_fleet_adapter_python traffic_light

For more details, please read this README.

Notes

  • The py api bindings are mainly experimental. Use with caution.
  • Current CI and docs gen are using rolling release of RMF
  • More bindings, tests, and examples are welcome!

Using the Bindings

Ensure that you have built this package and sourced its environment. Then it is as simple as importing the module containing the Python bindings!

import rmf_adapter as adpt

# You may then check the available bindings
print(dir(adpt))

Description

Fleet adapters allow for interactions between rmf_core and robot fleets.

High level pathing or task assignments can be issued to robots from rmf_core, and a fleet adapter will take those assignments and handle it in an implementation specific manner to direct individual robots to execute certain actions while monitoring and updating rmf_core on their high-level states via rmf_core robot update handles.

For more information it is helpful to look at the docs for rmf_core

Usage

The bound APIs are almost identical to the rmf_core ones as well, so if the written documentation for these packages, and the Python usage examples in the scripts or tests are insufficient, you can also look into the relevant C++ implementations as a last resort.

Alternatively, you may also explore any interfaces with:

# To list any available members or methods
dir(adpt.<WHATEVER_CLASS_YOU_WANT_TO_INSPECT>)

# To see function signatures
help(adpt.<WHATEVER_CLASS_YOU_WANT_TO_INSPECT>)

Binding Details

This package contains Python bindings to C++ code that is found in rmf_core and its associated packages. It was implemented using Pybind11.

Do note that as a result of that, if one needs to inspect the source, there are no Python implementations of any of the bound code, and it would be advisable to look directly into the appropriate rmf_fleet_adapter package, that contains the C++ code.. Additionally, C++ objects will get instantiated (generally) as references that can be used or called using the appropriate bound methods on the Python side.

Some of these bindings allow you to override virtual methods that would normally be called in the C++ side with Python code that you write in your Python scripts!

Features

There are enough functionalities bound to implement your own fleet adapters to communicate with the rest of the rmf_core systems in the ROS2 graph. Allowing you to:

  • Adapter/MockAdapter: Communicate with rmf_core by adding fleets or requesting deliveries
  • RobotCommandHandle: Specify custom robot command handles that can take commands from the rmf_core packages and implement or execute the following robot behaviors:
    • Docking
    • Path assignment
    • Starting
    • Stopping
    • RobotUpdateHandle: Update robot statuses
  • Executor: Implement new event handlers for:
    • Docking
    • Door operation
    • Lift door operation
    • Lift moving
  • Graph: Specify map geometries as they relate to waypoints and lanes
  • VehicleTraits: Specify robot vehicle traits, including:
    • Footprint
    • Kinematic limits

Additionally, when you pair this with rclpy, you can interact with the other ROS2 message interfaces provided by rmf_core, and essentially write your own fleet adapter in Python!


Creating your own RobotCommandHandle

Introduction

A RobotCommandHandle allows you to define routines that should be executed when rmf_core issues a command to the robot the RobotCommandHandle is responsible for to:

  • dock(): Dock
  • follow_new_path(): Follow a new path
  • stop() Stop

Python bindings have been written for the rmf_fleet_adapter::RobotCommandHandle abstract class that allows you to implement it in Python and have it communicate with the C++ code, as well as other Python bound rmf_mock_adapter classes and methods.

That is, you can write Python code as overrides to C++ RobotCommandHandle methods and have rmf_core execute it in the C++ side! This allows you to effectively write robot routines in Python that are callable from rmf_core!

All the standard functionalities for Python classes are open to you. You may define new attributes or members as you wish.

Caveats

  • The class methods follow_new_path, dock, and stop methods must be implemented if you want to use them, since the underlying C++ definitions are pure virtual method which requires an override
    • These methods will be called in from the C++ side by rmf_core! rmf_core will call the methods with the relevant arguments, so it is absolutely essential that you ensure that the same number of arguments are exposed in any of the core methods that you define.
  • You must not declare an __init__ method, as that will override the binding
    • If you still need to, look at the Using __init__ section

Also:

  • You may pass instances of your implemented RobotCommandHandles into bound C++ methods that take in std::shared_ptr<rmf_mock_adapter::RobotCommandHandle> arguments. This is because the bound class inherits from that type! It's very convenient.

Very Minimal Template

import rmf_adapter as adpt

class RobotCommandHandle(adpt.RobotCommandHandle):
    # If you want to declare class attributes you do it here or in an init
    new_member = "Rawr"

    # The argument names do not need to be the same
    # But they are declared here to match the C++ interface for consistency
    def follow_new_path(self,
                        waypoints: str,
                        path_finished_callback: Callable) -> None:
        # Your implementation goes here.
        # You may replace the following lines!
        print(self.new_member)  # We use the instance variable here!
        path_finished_callback()

    def dock(self,
             dock_name: str,
             docking_finished_callback: Callable) -> None:
        # Implementation here too!
        print(dock_name)
        docking_finished_callback()

# Then you may simply instantiate it like any other Python class!
command_handler = RobotCommandHandle()

## Testing Instance Attributes
print(command_handler.new_member)  # Directly
command_handler.follow_new_path("", lambda: None)  # Via class method

command_handler.newer_member = "Rer"  # A new one!
print(command_handler.newer_member)

## Testing Class Methods
# And the methods can get called from the Python side
command_handler.dock(
    "", lambda: print("Dock callback works!")
) 

# But also to a C++ method that takes in a std::shared_ptr argument!
adpt.test_shared_ptr(command_handler,
                     "wow",
                     lambda: print("wow"))
# With default args!
adpt.test_shared_ptr(command_handler,
                     docking_finish_callback=lambda: print("wow"))

Using __init__

If, however, you still want to define an __init__ magic method, ensure that you explicitly call the required bound C++ constructor.

class RobotCommandHandleInit(adpt.RobotCommandHandle):
    def __init__(self, new_member="rawr"):
        adpt.RobotCommandHandle.__init__(self)
        self.new_member = new_member

    # The argument names do not need to be the same
    # But they are declared here to match the C++ interface for consistency
    def follow_new_path(self,
                        waypoints: str,
                        path_finished_callback: Callable) -> None:
        # Your implementation goes here.
        # You may replace the following lines!
        print(self.new_member)  # We use the instance variable here!
        path_finished_callback()

    def dock(self,
             dock_name: str,
             docking_finished_callback: Callable) -> None:
        # Implementation here too!
        print(dock_name)
        docking_finished_callback()

Creating Your Own Event Executor

An event executor simply executes some routing upon receipt of an event.

In this case, the graph.lane.Executor binding class allows you to override the various execution routines for the following event types (defined and used by rmf_core on the C++ side):

  • Dock
  • DoorOpen
  • DoorClose
  • LiftDoorOpen
  • LiftDoorClose
  • LiftMove

The corresponding bound event objects in Python can be found defined in graph.lane. You can print information about them out using dir and help as usual.

import rmf_adapter.graph as graph

class EventListener(graph.lane.Executor):
    def __init__(self):
        graph.lane.Executor.__init__(self)

    def dock_execute(self, dock):
        return

    def door_open_execute(self, door_open):
        return

    def door_close_execute(self, door_close):
        return

    def lift_door_open_execute(self, lift_door_open):
        return

    def lift_door_close_execute(self, lift_door_close):
        return

    def lift_move_execute(self, lift_move):
        return


Using Executors

Simply pass it into the execute() method for a given Entry object, which you can find attached to either the entry or exit members of a given graph Lane object.

The appropriate event execute method from the executor will be called, with the event being passed in, allowing you to access the relevant event attributes.

executor = self.EventListener()

# Where lane_obj is a lane from graph.get_lane()
lane_obj.entry.event.execute(executor)

So, for example, if the event in the lane_obj's entry node was docking event, and our executor had this dock_execute defined:

def dock_execute(self, dock):
    self.dock_to_wp[dock.dock_name] = self.wp
    print("DOCK EVENT EXECUTED FOR DOCK:", dock.dock_name)

Then it will get called! (Notice how the passed in dock's dock_name attribute is accessible.)

Interacting with rmf_core

Check out the test_adapter.py script in scripts for an integration example with rmf_core! These steps are much easier understood in context.

However, if you have already done that, relevant points are to:

  1. Init rclpy if needed (rclpy.init()), and init rclcpp (adpt.init_rclcpp())
  2. Define your own ChildRobotCommandHandle subclass of adpt.RobotCommandHandle
    • Making sure to define any of the core methods you intend for the rmf_core to be able to call
    • Ensure that the method argument signatures are the same as the template though!
    • If you want the ability to update rmf_core on the robot's status, also be sure to expose a class member for a bound RobotUpdateHandle instance, that can be used to notify rmf_core of any updates to robot position (update_position()), interruptions (interrupted()), or additional delays (add_delay())
  3. Optionally define custom graph.lane.Executorexecutor objects to handle events
  4. Define your map's graph with graph.Graph()
  5. Adding map waypoints: add_waypoint()
    • You can also specify if the waypoints are holding_points (set_holding_point()) or passthrough_points (set_passthrough_point())
  6. And map lanes: add_lane(), add_bidir_lane(), `add_dock_lane()
  7. Add keys to the graph waypoints, which gives the waypoints names that are relevant when requests are sent via the MockAdapter/Adapter to rmf_core
  8. Define your robot
    • By defining its profile: traits.Profile()
    • And instantiating its traits: traits.VehicleTraits()
  9. Create an adapter: adpt.Adapter() or adpt.MockAdapter()
  10. Attach a new fleet: add_fleet()
  11. Define a callback function for task acceptance: accept_task_requests()
  12. Set task planner parameters: set_task_planner_params()
  13. Create a list of Plan Starts: [plan.Start()]
    • You will need to pass in a C++ starting time object, which you can obtain using the now() method from your instantiated Adapter/MockAdapter
  14. And add your robots to your fleet: add_robot()
  15. Then simply request your deliveries!: request_delivery()
  16. Then spin your rclpy nodes if you have any!: spin(), spin_once()

T​hen you're done! :tada:

Running tests

Unit tests have been written for the individual components.

You may invoke pytest directly in the appropriate directory.

# Ensure environment is sourced and you are in the right directory
$ source <workspace_dir>/install/setup.bash
$ cd <rmf_fleet_adapter_python_dir>/tests

# Invoke the tests
$ pytest -v

Or use colcon test with console output redirection.

# Ensure environment is sourced and you are in the right directory
$ source <workspace_dir>/install/setup.bash
$ cd <workspace_dir>

# Invoke the tests
$ colcon test --packages-select rmf_fleet_adapter_python --event-handlers console_direct+

Gotchas

Pointer Indirection Gotchas

  • clone_ptr indirection does not seem to work correctly currently! Be very careful!
  • The only way surefire way to do pointer indirection is to do it via the objects that manage them. Unfortunately there isn't much of a workaround given that most of the pointers point to abstract classes.
  • For most of the other pointers, you must make them using the various factory functions. Do not instantiate them directly since you will not be able to configure their internal members, even if those members are public.
    • No explicit bound methods exist for them as they are meant to be pointers to implementations that might vary widely.

Update Handles Should NOT be Directly Instantiated (Segfault risk!)

  • The RobotUpdateHandle and FleetUpdateHandle classes must only be instantiated via their factory methods! (Their init members have been disabled as a result.)
    • FleetUpdateHandle should be instantiated via TestScenario (via add_fleet)
    • RobotUpdateHandle should be instantiated via FleetUpdateHandle (via add_robot)
    • Doing otherwise will cause their members to contain null values which will lead to segmentation faults!!!

Different Kinds of Waypoints

  • The graph and plan submodules have their own internal Waypoint classes with different, but related interfaces!

Two Kinds of ROS2 Nodes

Seeing how we are binding C++ objects and methods from rmf_core, we inevitably will end up binding an rclcpp Node object. (This will normally come from the Adapter or MockAdapter node members.)

However, if we wanted to, for instance, use rclpy and define our own pub-sub functionalities in our Python script, we'll have an rclpy Node object that we can play around with as well.

The two node types are NOT interchangeable! Keep a VERY strong handle on which nodes are which, and treat them with respect.

The reason why the rclcpp Node has been exposed for you to use, is for an interface to access rmf_core's MockAdapter/Adapter time, as it pertains to the rclcpp ROS2 clock, which is helpfully converted into a Python manipulatable datetime object.

Time Shenanigans

There are three kinds of time that you should be concerned with, owing to different types of time used on the C++ side.

However, confusing things can happen due to how pybind11 provides conversions between those C++ types and Python datetime types. Let me explain...

The three kinds of time that the C++ side uses are:

  • Steady clock timepoint: std::chrono::steady_clock::time_point
    • This gets converted into a datetime.timedelta, where the timedelta represents the time elapsed since the start of the steady_clock's time (that is, since its epoch)
  • System clock timepoint: std::chrono::time_point
    • This gets converted into a datetime.datetime, simple.
  • Duration: std::chrono::Duration
    • This gets converted into a datetime.timedelta, which represents the quantity of time that the Duration represents.

Caught the confusion? Three types of C++ time are being converted into TWO types of Python time, with no way to distinguish between the datetime.timedelta objects that represent Duration objects as opposed to std::chrono::steady_clock::time_point objects.

The conversions back and forth work fine. But you must be very aware of which timedelta objects refer to which.

In an effort to make it simpler, the bindings automatically convert steady_clock::time_point objects to system_clock time_point objects. So that what is meant by the datetime.timedelta objects is clearer.

Caveats

There is also an additional caveat though. The steady_clock::time_point values are relative to the ROS2 clock. That is, it uses the ROS2 epoch.

So when we convert them to system_clock time_point objects, just note that that relative time be used to construct the datetime.datetime object.

So, for example, if the steady_clock time_point was relative to a ROS2 clock with epoch start on 1 Jan 2020 00:00:00, and represented 1 second, and it gets converted automatically by the bindings, the returned datetime.datetime object will actually represent... 1 Jan 1970 00:00:01! Because system clocks are relative to the UNIX epoch!

All that is important is the relative time values. If you keep that as a constant, and pass the datetime objects back into bound methods that use them, there are no additional implications on the correct functioning of the C++ calls that those bound methods will invoke.

CHANGELOG

Changelog for package rmf_fleet_adapter_python

1.3.0 (2021-06-07)

  • Modifications to support refactor of rmf_task (#51 <https://github.com/open-rmf/rmf_ros2/issues/51>_)
  • Fix symlink-install compilation (#32 <https://github.com/open-rmf/rmf_ros2/issues/32>_)
  • Updated package.xml (#26 <https://github.com/open-rmf/rmf_ros2/issues/26>_)
  • Fix/rmf task ros2 cleanup (#21 <https://github.com/open-rmf/rmf_ros2/issues/21>_)
  • Feature/python binding planner (#11 <https://github.com/open-rmf/rmf_ros2/issues/11>_)
  • Adding reference_internal tag to function bindings that return raw pointers (#6 <https://github.com/open-rmf/rmf_ros2/issues/6>_)
  • Feature/add unstable participant api (#11 <https://github.com/open-rmf/rmf_ros2/issues/11>_)
  • Feature/add simple docs (#9 <https://github.com/open-rmf/rmf_ros2/issues/9>_)
  • Support apis for task dispatcher (#10 <https://github.com/open-rmf/rmf_ros2/issues/10>_)
  • differentiate functions to prevent overloading (#8 <https://github.com/open-rmf/rmf_ros2/issues/8>_)
  • support ez traffic light (#7 <https://github.com/open-rmf/rmf_ros2/issues/7>_)
  • Update/release 1.1 (#6 <https://github.com/open-rmf/rmf_ros2/issues/6>_)
  • Implement binding for Duration optional
  • Make integration test even stricter
  • Add reference capture for posterity
  • Add clarifying printouts and fix multi-timer bug
  • Integrate compute_plan_starts into integration test
  • Implement type tests
  • Bind optional constructors and delivery msg interfaces
  • Bind compute_plan_starts
  • Add update_position overload
  • Implement Python Bindings for rmf_fleet_adapter (#1 <https://github.com/open-rmf/rmf_ros2/issues/1>_)
  • Contributors: Aaron Chong, Charayaphan Nakorn Boon Han, Geoffrey Biggs, Grey, Marco A. Gutiérrez, Yadu, methylDragon, youliang

Wiki Tutorials

See ROS Wiki Tutorials for more details.

Source Tutorials

Not currently indexed.

Package Dependencies

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

No launch files found

Messages

No message files found.

Services

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Plugins

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Recent questions tagged rmf_fleet_adapter_python at answers.ros.org

Package Summary

Tags No category tags.
Version 1.4.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/open-rmf/rmf_ros2.git
VCS Type git
VCS Version foxy
Last Updated 2021-09-02
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

Python bindings for the rmf_fleet_adapter

Additional Links

No additional links.

Maintainers

  • methylDragon
  • Marco A. Gutiérrez

Authors

No additional authors.

rmf_fleet_adapter_python

Python bindings for rmf_fleet_adapter

Introduction

The rmf_fleet_adapter package defines C++ classes that can interact with rmf_core. Specifically an Adapter and a MockAdapter class. This package provides bindings for both and most of their dependencies.

The main difference between rmf_fleet_adapter and rmf_mock_adapter is the fact that rmf_fleet_adapter objects can interact with the ROS2 graph.

The MockAdapter and Adapter both do this. But the difference is that MockAdapter can be instantiated with no issues, whereas Adapter will block to wait for a scheduler node from rmf_core to make its presence known.

Installation

  • Place the pybind_ament and rmf_fleet_adapter_python packages in your ROS2 workspace, and run colcon build as per normal.

Running Examples

Integration test script for rmf loop and delivery task:

ros2 run rmf_fleet_adapter_python test_adapter.py

You may then probe the effects on the ROS2 graph by subscribing to the following topics with ros2 topic echo <TOPIC_NAME>: - : /dispenser_requests, /dispenser_results, ingestor_requests, ingestor_results, /task_summaries

Traffic Light Example

This will showcase an example of having 2 "traffic light" robots using RMF.

# First Terminal
ros2 run rmf_fleet_adapter_python schedule_blockade_nodes

# Second Terminal
ros2 run rmf_fleet_adapter_python traffic_light

For more details, please read this README.

Notes

  • The py api bindings are mainly experimental. Use with caution.
  • Current CI and docs gen are using rolling release of RMF
  • More bindings, tests, and examples are welcome!

Using the Bindings

Ensure that you have built this package and sourced its environment. Then it is as simple as importing the module containing the Python bindings!

import rmf_adapter as adpt

# You may then check the available bindings
print(dir(adpt))

Description

Fleet adapters allow for interactions between rmf_core and robot fleets.

High level pathing or task assignments can be issued to robots from rmf_core, and a fleet adapter will take those assignments and handle it in an implementation specific manner to direct individual robots to execute certain actions while monitoring and updating rmf_core on their high-level states via rmf_core robot update handles.

For more information it is helpful to look at the docs for rmf_core

Usage

The bound APIs are almost identical to the rmf_core ones as well, so if the written documentation for these packages, and the Python usage examples in the scripts or tests are insufficient, you can also look into the relevant C++ implementations as a last resort.

Alternatively, you may also explore any interfaces with:

# To list any available members or methods
dir(adpt.<WHATEVER_CLASS_YOU_WANT_TO_INSPECT>)

# To see function signatures
help(adpt.<WHATEVER_CLASS_YOU_WANT_TO_INSPECT>)

Binding Details

This package contains Python bindings to C++ code that is found in rmf_core and its associated packages. It was implemented using Pybind11.

Do note that as a result of that, if one needs to inspect the source, there are no Python implementations of any of the bound code, and it would be advisable to look directly into the appropriate rmf_fleet_adapter package, that contains the C++ code.. Additionally, C++ objects will get instantiated (generally) as references that can be used or called using the appropriate bound methods on the Python side.

Some of these bindings allow you to override virtual methods that would normally be called in the C++ side with Python code that you write in your Python scripts!

Features

There are enough functionalities bound to implement your own fleet adapters to communicate with the rest of the rmf_core systems in the ROS2 graph. Allowing you to:

  • Adapter/MockAdapter: Communicate with rmf_core by adding fleets or requesting deliveries
  • RobotCommandHandle: Specify custom robot command handles that can take commands from the rmf_core packages and implement or execute the following robot behaviors:
    • Docking
    • Path assignment
    • Starting
    • Stopping
    • RobotUpdateHandle: Update robot statuses
  • Executor: Implement new event handlers for:
    • Docking
    • Door operation
    • Lift door operation
    • Lift moving
  • Graph: Specify map geometries as they relate to waypoints and lanes
  • VehicleTraits: Specify robot vehicle traits, including:
    • Footprint
    • Kinematic limits

Additionally, when you pair this with rclpy, you can interact with the other ROS2 message interfaces provided by rmf_core, and essentially write your own fleet adapter in Python!


Creating your own RobotCommandHandle

Introduction

A RobotCommandHandle allows you to define routines that should be executed when rmf_core issues a command to the robot the RobotCommandHandle is responsible for to:

  • dock(): Dock
  • follow_new_path(): Follow a new path
  • stop() Stop

Python bindings have been written for the rmf_fleet_adapter::RobotCommandHandle abstract class that allows you to implement it in Python and have it communicate with the C++ code, as well as other Python bound rmf_mock_adapter classes and methods.

That is, you can write Python code as overrides to C++ RobotCommandHandle methods and have rmf_core execute it in the C++ side! This allows you to effectively write robot routines in Python that are callable from rmf_core!

All the standard functionalities for Python classes are open to you. You may define new attributes or members as you wish.

Caveats

  • The class methods follow_new_path, dock, and stop methods must be implemented if you want to use them, since the underlying C++ definitions are pure virtual method which requires an override
    • These methods will be called in from the C++ side by rmf_core! rmf_core will call the methods with the relevant arguments, so it is absolutely essential that you ensure that the same number of arguments are exposed in any of the core methods that you define.
  • You must not declare an __init__ method, as that will override the binding
    • If you still need to, look at the Using __init__ section

Also:

  • You may pass instances of your implemented RobotCommandHandles into bound C++ methods that take in std::shared_ptr<rmf_mock_adapter::RobotCommandHandle> arguments. This is because the bound class inherits from that type! It's very convenient.

Very Minimal Template

import rmf_adapter as adpt

class RobotCommandHandle(adpt.RobotCommandHandle):
    # If you want to declare class attributes you do it here or in an init
    new_member = "Rawr"

    # The argument names do not need to be the same
    # But they are declared here to match the C++ interface for consistency
    def follow_new_path(self,
                        waypoints: str,
                        path_finished_callback: Callable) -> None:
        # Your implementation goes here.
        # You may replace the following lines!
        print(self.new_member)  # We use the instance variable here!
        path_finished_callback()

    def dock(self,
             dock_name: str,
             docking_finished_callback: Callable) -> None:
        # Implementation here too!
        print(dock_name)
        docking_finished_callback()

# Then you may simply instantiate it like any other Python class!
command_handler = RobotCommandHandle()

## Testing Instance Attributes
print(command_handler.new_member)  # Directly
command_handler.follow_new_path("", lambda: None)  # Via class method

command_handler.newer_member = "Rer"  # A new one!
print(command_handler.newer_member)

## Testing Class Methods
# And the methods can get called from the Python side
command_handler.dock(
    "", lambda: print("Dock callback works!")
) 

# But also to a C++ method that takes in a std::shared_ptr argument!
adpt.test_shared_ptr(command_handler,
                     "wow",
                     lambda: print("wow"))
# With default args!
adpt.test_shared_ptr(command_handler,
                     docking_finish_callback=lambda: print("wow"))

Using __init__

If, however, you still want to define an __init__ magic method, ensure that you explicitly call the required bound C++ constructor.

class RobotCommandHandleInit(adpt.RobotCommandHandle):
    def __init__(self, new_member="rawr"):
        adpt.RobotCommandHandle.__init__(self)
        self.new_member = new_member

    # The argument names do not need to be the same
    # But they are declared here to match the C++ interface for consistency
    def follow_new_path(self,
                        waypoints: str,
                        path_finished_callback: Callable) -> None:
        # Your implementation goes here.
        # You may replace the following lines!
        print(self.new_member)  # We use the instance variable here!
        path_finished_callback()

    def dock(self,
             dock_name: str,
             docking_finished_callback: Callable) -> None:
        # Implementation here too!
        print(dock_name)
        docking_finished_callback()

Creating Your Own Event Executor

An event executor simply executes some routing upon receipt of an event.

In this case, the graph.lane.Executor binding class allows you to override the various execution routines for the following event types (defined and used by rmf_core on the C++ side):

  • Dock
  • DoorOpen
  • DoorClose
  • LiftDoorOpen
  • LiftDoorClose
  • LiftMove

The corresponding bound event objects in Python can be found defined in graph.lane. You can print information about them out using dir and help as usual.

import rmf_adapter.graph as graph

class EventListener(graph.lane.Executor):
    def __init__(self):
        graph.lane.Executor.__init__(self)

    def dock_execute(self, dock):
        return

    def door_open_execute(self, door_open):
        return

    def door_close_execute(self, door_close):
        return

    def lift_door_open_execute(self, lift_door_open):
        return

    def lift_door_close_execute(self, lift_door_close):
        return

    def lift_move_execute(self, lift_move):
        return


Using Executors

Simply pass it into the execute() method for a given Entry object, which you can find attached to either the entry or exit members of a given graph Lane object.

The appropriate event execute method from the executor will be called, with the event being passed in, allowing you to access the relevant event attributes.

executor = self.EventListener()

# Where lane_obj is a lane from graph.get_lane()
lane_obj.entry.event.execute(executor)

So, for example, if the event in the lane_obj's entry node was docking event, and our executor had this dock_execute defined:

def dock_execute(self, dock):
    self.dock_to_wp[dock.dock_name] = self.wp
    print("DOCK EVENT EXECUTED FOR DOCK:", dock.dock_name)

Then it will get called! (Notice how the passed in dock's dock_name attribute is accessible.)

Interacting with rmf_core

Check out the test_adapter.py script in scripts for an integration example with rmf_core! These steps are much easier understood in context.

However, if you have already done that, relevant points are to:

  1. Init rclpy if needed (rclpy.init()), and init rclcpp (adpt.init_rclcpp())
  2. Define your own ChildRobotCommandHandle subclass of adpt.RobotCommandHandle
    • Making sure to define any of the core methods you intend for the rmf_core to be able to call
    • Ensure that the method argument signatures are the same as the template though!
    • If you want the ability to update rmf_core on the robot's status, also be sure to expose a class member for a bound RobotUpdateHandle instance, that can be used to notify rmf_core of any updates to robot position (update_position()), interruptions (interrupted()), or additional delays (add_delay())
  3. Optionally define custom graph.lane.Executorexecutor objects to handle events
  4. Define your map's graph with graph.Graph()
  5. Adding map waypoints: add_waypoint()
    • You can also specify if the waypoints are holding_points (set_holding_point()) or passthrough_points (set_passthrough_point())
  6. And map lanes: add_lane(), add_bidir_lane(), `add_dock_lane()
  7. Add keys to the graph waypoints, which gives the waypoints names that are relevant when requests are sent via the MockAdapter/Adapter to rmf_core
  8. Define your robot
    • By defining its profile: traits.Profile()
    • And instantiating its traits: traits.VehicleTraits()
  9. Create an adapter: adpt.Adapter() or adpt.MockAdapter()
  10. Attach a new fleet: add_fleet()
  11. Define a callback function for task acceptance: accept_task_requests()
  12. Set task planner parameters: set_task_planner_params()
  13. Create a list of Plan Starts: [plan.Start()]
    • You will need to pass in a C++ starting time object, which you can obtain using the now() method from your instantiated Adapter/MockAdapter
  14. And add your robots to your fleet: add_robot()
  15. Then simply request your deliveries!: request_delivery()
  16. Then spin your rclpy nodes if you have any!: spin(), spin_once()

T​hen you're done! :tada:

Running tests

Unit tests have been written for the individual components.

You may invoke pytest directly in the appropriate directory.

# Ensure environment is sourced and you are in the right directory
$ source <workspace_dir>/install/setup.bash
$ cd <rmf_fleet_adapter_python_dir>/tests

# Invoke the tests
$ pytest -v

Or use colcon test with console output redirection.

# Ensure environment is sourced and you are in the right directory
$ source <workspace_dir>/install/setup.bash
$ cd <workspace_dir>

# Invoke the tests
$ colcon test --packages-select rmf_fleet_adapter_python --event-handlers console_direct+

Gotchas

Pointer Indirection Gotchas

  • clone_ptr indirection does not seem to work correctly currently! Be very careful!
  • The only way surefire way to do pointer indirection is to do it via the objects that manage them. Unfortunately there isn't much of a workaround given that most of the pointers point to abstract classes.
  • For most of the other pointers, you must make them using the various factory functions. Do not instantiate them directly since you will not be able to configure their internal members, even if those members are public.
    • No explicit bound methods exist for them as they are meant to be pointers to implementations that might vary widely.

Update Handles Should NOT be Directly Instantiated (Segfault risk!)

  • The RobotUpdateHandle and FleetUpdateHandle classes must only be instantiated via their factory methods! (Their init members have been disabled as a result.)
    • FleetUpdateHandle should be instantiated via TestScenario (via add_fleet)
    • RobotUpdateHandle should be instantiated via FleetUpdateHandle (via add_robot)
    • Doing otherwise will cause their members to contain null values which will lead to segmentation faults!!!

Different Kinds of Waypoints

  • The graph and plan submodules have their own internal Waypoint classes with different, but related interfaces!

Two Kinds of ROS2 Nodes

Seeing how we are binding C++ objects and methods from rmf_core, we inevitably will end up binding an rclcpp Node object. (This will normally come from the Adapter or MockAdapter node members.)

However, if we wanted to, for instance, use rclpy and define our own pub-sub functionalities in our Python script, we'll have an rclpy Node object that we can play around with as well.

The two node types are NOT interchangeable! Keep a VERY strong handle on which nodes are which, and treat them with respect.

The reason why the rclcpp Node has been exposed for you to use, is for an interface to access rmf_core's MockAdapter/Adapter time, as it pertains to the rclcpp ROS2 clock, which is helpfully converted into a Python manipulatable datetime object.

Time Shenanigans

There are three kinds of time that you should be concerned with, owing to different types of time used on the C++ side.

However, confusing things can happen due to how pybind11 provides conversions between those C++ types and Python datetime types. Let me explain...

The three kinds of time that the C++ side uses are:

  • Steady clock timepoint: std::chrono::steady_clock::time_point
    • This gets converted into a datetime.timedelta, where the timedelta represents the time elapsed since the start of the steady_clock's time (that is, since its epoch)
  • System clock timepoint: std::chrono::time_point
    • This gets converted into a datetime.datetime, simple.
  • Duration: std::chrono::Duration
    • This gets converted into a datetime.timedelta, which represents the quantity of time that the Duration represents.

Caught the confusion? Three types of C++ time are being converted into TWO types of Python time, with no way to distinguish between the datetime.timedelta objects that represent Duration objects as opposed to std::chrono::steady_clock::time_point objects.

The conversions back and forth work fine. But you must be very aware of which timedelta objects refer to which.

In an effort to make it simpler, the bindings automatically convert steady_clock::time_point objects to system_clock time_point objects. So that what is meant by the datetime.timedelta objects is clearer.

Caveats

There is also an additional caveat though. The steady_clock::time_point values are relative to the ROS2 clock. That is, it uses the ROS2 epoch.

So when we convert them to system_clock time_point objects, just note that that relative time be used to construct the datetime.datetime object.

So, for example, if the steady_clock time_point was relative to a ROS2 clock with epoch start on 1 Jan 2020 00:00:00, and represented 1 second, and it gets converted automatically by the bindings, the returned datetime.datetime object will actually represent... 1 Jan 1970 00:00:01! Because system clocks are relative to the UNIX epoch!

All that is important is the relative time values. If you keep that as a constant, and pass the datetime objects back into bound methods that use them, there are no additional implications on the correct functioning of the C++ calls that those bound methods will invoke.

CHANGELOG

Changelog for package rmf_fleet_adapter_python

1.3.0 (2021-06-07)

  • Modifications to support refactor of rmf_task (#51 <https://github.com/open-rmf/rmf_ros2/issues/51>_)
  • Fix symlink-install compilation (#32 <https://github.com/open-rmf/rmf_ros2/issues/32>_)
  • Updated package.xml (#26 <https://github.com/open-rmf/rmf_ros2/issues/26>_)
  • Fix/rmf task ros2 cleanup (#21 <https://github.com/open-rmf/rmf_ros2/issues/21>_)
  • Feature/python binding planner (#11 <https://github.com/open-rmf/rmf_ros2/issues/11>_)
  • Adding reference_internal tag to function bindings that return raw pointers (#6 <https://github.com/open-rmf/rmf_ros2/issues/6>_)
  • Feature/add unstable participant api (#11 <https://github.com/open-rmf/rmf_ros2/issues/11>_)
  • Feature/add simple docs (#9 <https://github.com/open-rmf/rmf_ros2/issues/9>_)
  • Support apis for task dispatcher (#10 <https://github.com/open-rmf/rmf_ros2/issues/10>_)
  • differentiate functions to prevent overloading (#8 <https://github.com/open-rmf/rmf_ros2/issues/8>_)
  • support ez traffic light (#7 <https://github.com/open-rmf/rmf_ros2/issues/7>_)
  • Update/release 1.1 (#6 <https://github.com/open-rmf/rmf_ros2/issues/6>_)
  • Implement binding for Duration optional
  • Make integration test even stricter
  • Add reference capture for posterity
  • Add clarifying printouts and fix multi-timer bug
  • Integrate compute_plan_starts into integration test
  • Implement type tests
  • Bind optional constructors and delivery msg interfaces
  • Bind compute_plan_starts
  • Add update_position overload
  • Implement Python Bindings for rmf_fleet_adapter (#1 <https://github.com/open-rmf/rmf_ros2/issues/1>_)
  • Contributors: Aaron Chong, Charayaphan Nakorn Boon Han, Geoffrey Biggs, Grey, Marco A. Gutiérrez, Yadu, methylDragon, youliang

Wiki Tutorials

See ROS Wiki Tutorials for more details.

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Recent questions tagged rmf_fleet_adapter_python at answers.ros.org

Package Summary

Tags No category tags.
Version 1.4.0
License Apache License 2.0
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/open-rmf/rmf_ros2.git
VCS Type git
VCS Version main
Last Updated 2021-09-02
Dev Status DEVELOPED
CI status No Continuous Integration
Released RELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

Python bindings for the rmf_fleet_adapter

Additional Links

No additional links.

Maintainers

  • methylDragon
  • Marco A. Gutiérrez

Authors

No additional authors.

rmf_fleet_adapter_python

Python bindings for rmf_fleet_adapter

Introduction

The rmf_fleet_adapter package defines C++ classes that can interact with rmf_core. Specifically an Adapter and a MockAdapter class. This package provides bindings for both and most of their dependencies.

The main difference between rmf_fleet_adapter and rmf_mock_adapter is the fact that rmf_fleet_adapter objects can interact with the ROS2 graph.

The MockAdapter and Adapter both do this. But the difference is that MockAdapter can be instantiated with no issues, whereas Adapter will block to wait for a scheduler node from rmf_core to make its presence known.

Installation

  • Place the pybind_ament and rmf_fleet_adapter_python packages in your ROS2 workspace, and run colcon build as per normal.

Running Examples

Integration test script for rmf loop and delivery task:

ros2 run rmf_fleet_adapter_python test_adapter.py

You may then probe the effects on the ROS2 graph by subscribing to the following topics with ros2 topic echo <TOPIC_NAME>: - : /dispenser_requests, /dispenser_results, ingestor_requests, ingestor_results, /task_summaries

Traffic Light Example

This will showcase an example of having 2 "traffic light" robots using RMF.

# First Terminal
ros2 run rmf_fleet_adapter_python schedule_blockade_nodes

# Second Terminal
ros2 run rmf_fleet_adapter_python traffic_light

For more details, please read this README.

Notes

  • The py api bindings are mainly experimental. Use with caution.
  • Current CI and docs gen are using rolling release of RMF
  • More bindings, tests, and examples are welcome!

Using the Bindings

Ensure that you have built this package and sourced its environment. Then it is as simple as importing the module containing the Python bindings!

import rmf_adapter as adpt

# You may then check the available bindings
print(dir(adpt))

Description

Fleet adapters allow for interactions between rmf_core and robot fleets.

High level pathing or task assignments can be issued to robots from rmf_core, and a fleet adapter will take those assignments and handle it in an implementation specific manner to direct individual robots to execute certain actions while monitoring and updating rmf_core on their high-level states via rmf_core robot update handles.

For more information it is helpful to look at the docs for rmf_core

Usage

The bound APIs are almost identical to the rmf_core ones as well, so if the written documentation for these packages, and the Python usage examples in the scripts or tests are insufficient, you can also look into the relevant C++ implementations as a last resort.

Alternatively, you may also explore any interfaces with:

# To list any available members or methods
dir(adpt.<WHATEVER_CLASS_YOU_WANT_TO_INSPECT>)

# To see function signatures
help(adpt.<WHATEVER_CLASS_YOU_WANT_TO_INSPECT>)

Binding Details

This package contains Python bindings to C++ code that is found in rmf_core and its associated packages. It was implemented using Pybind11.

Do note that as a result of that, if one needs to inspect the source, there are no Python implementations of any of the bound code, and it would be advisable to look directly into the appropriate rmf_fleet_adapter package, that contains the C++ code.. Additionally, C++ objects will get instantiated (generally) as references that can be used or called using the appropriate bound methods on the Python side.

Some of these bindings allow you to override virtual methods that would normally be called in the C++ side with Python code that you write in your Python scripts!

Features

There are enough functionalities bound to implement your own fleet adapters to communicate with the rest of the rmf_core systems in the ROS2 graph. Allowing you to:

  • Adapter/MockAdapter: Communicate with rmf_core by adding fleets or requesting deliveries
  • RobotCommandHandle: Specify custom robot command handles that can take commands from the rmf_core packages and implement or execute the following robot behaviors:
    • Docking
    • Path assignment
    • Starting
    • Stopping
    • RobotUpdateHandle: Update robot statuses
  • Executor: Implement new event handlers for:
    • Docking
    • Door operation
    • Lift door operation
    • Lift moving
  • Graph: Specify map geometries as they relate to waypoints and lanes
  • VehicleTraits: Specify robot vehicle traits, including:
    • Footprint
    • Kinematic limits

Additionally, when you pair this with rclpy, you can interact with the other ROS2 message interfaces provided by rmf_core, and essentially write your own fleet adapter in Python!


Creating your own RobotCommandHandle

Introduction

A RobotCommandHandle allows you to define routines that should be executed when rmf_core issues a command to the robot the RobotCommandHandle is responsible for to:

  • dock(): Dock
  • follow_new_path(): Follow a new path
  • stop() Stop

Python bindings have been written for the rmf_fleet_adapter::RobotCommandHandle abstract class that allows you to implement it in Python and have it communicate with the C++ code, as well as other Python bound rmf_mock_adapter classes and methods.

That is, you can write Python code as overrides to C++ RobotCommandHandle methods and have rmf_core execute it in the C++ side! This allows you to effectively write robot routines in Python that are callable from rmf_core!

All the standard functionalities for Python classes are open to you. You may define new attributes or members as you wish.

Caveats

  • The class methods follow_new_path, dock, and stop methods must be implemented if you want to use them, since the underlying C++ definitions are pure virtual method which requires an override
    • These methods will be called in from the C++ side by rmf_core! rmf_core will call the methods with the relevant arguments, so it is absolutely essential that you ensure that the same number of arguments are exposed in any of the core methods that you define.
  • You must not declare an __init__ method, as that will override the binding
    • If you still need to, look at the Using __init__ section

Also:

  • You may pass instances of your implemented RobotCommandHandles into bound C++ methods that take in std::shared_ptr<rmf_mock_adapter::RobotCommandHandle> arguments. This is because the bound class inherits from that type! It's very convenient.

Very Minimal Template

import rmf_adapter as adpt

class RobotCommandHandle(adpt.RobotCommandHandle):
    # If you want to declare class attributes you do it here or in an init
    new_member = "Rawr"

    # The argument names do not need to be the same
    # But they are declared here to match the C++ interface for consistency
    def follow_new_path(self,
                        waypoints: str,
                        path_finished_callback: Callable) -> None:
        # Your implementation goes here.
        # You may replace the following lines!
        print(self.new_member)  # We use the instance variable here!
        path_finished_callback()

    def dock(self,
             dock_name: str,
             docking_finished_callback: Callable) -> None:
        # Implementation here too!
        print(dock_name)
        docking_finished_callback()

# Then you may simply instantiate it like any other Python class!
command_handler = RobotCommandHandle()

## Testing Instance Attributes
print(command_handler.new_member)  # Directly
command_handler.follow_new_path("", lambda: None)  # Via class method

command_handler.newer_member = "Rer"  # A new one!
print(command_handler.newer_member)

## Testing Class Methods
# And the methods can get called from the Python side
command_handler.dock(
    "", lambda: print("Dock callback works!")
) 

# But also to a C++ method that takes in a std::shared_ptr argument!
adpt.test_shared_ptr(command_handler,
                     "wow",
                     lambda: print("wow"))
# With default args!
adpt.test_shared_ptr(command_handler,
                     docking_finish_callback=lambda: print("wow"))

Using __init__

If, however, you still want to define an __init__ magic method, ensure that you explicitly call the required bound C++ constructor.

class RobotCommandHandleInit(adpt.RobotCommandHandle):
    def __init__(self, new_member="rawr"):
        adpt.RobotCommandHandle.__init__(self)
        self.new_member = new_member

    # The argument names do not need to be the same
    # But they are declared here to match the C++ interface for consistency
    def follow_new_path(self,
                        waypoints: str,
                        path_finished_callback: Callable) -> None:
        # Your implementation goes here.
        # You may replace the following lines!
        print(self.new_member)  # We use the instance variable here!
        path_finished_callback()

    def dock(self,
             dock_name: str,
             docking_finished_callback: Callable) -> None:
        # Implementation here too!
        print(dock_name)
        docking_finished_callback()

Creating Your Own Event Executor

An event executor simply executes some routing upon receipt of an event.

In this case, the graph.lane.Executor binding class allows you to override the various execution routines for the following event types (defined and used by rmf_core on the C++ side):

  • Dock
  • DoorOpen
  • DoorClose
  • LiftDoorOpen
  • LiftDoorClose
  • LiftMove

The corresponding bound event objects in Python can be found defined in graph.lane. You can print information about them out using dir and help as usual.

import rmf_adapter.graph as graph

class EventListener(graph.lane.Executor):
    def __init__(self):
        graph.lane.Executor.__init__(self)

    def dock_execute(self, dock):
        return

    def door_open_execute(self, door_open):
        return

    def door_close_execute(self, door_close):
        return

    def lift_door_open_execute(self, lift_door_open):
        return

    def lift_door_close_execute(self, lift_door_close):
        return

    def lift_move_execute(self, lift_move):
        return


Using Executors

Simply pass it into the execute() method for a given Entry object, which you can find attached to either the entry or exit members of a given graph Lane object.

The appropriate event execute method from the executor will be called, with the event being passed in, allowing you to access the relevant event attributes.

executor = self.EventListener()

# Where lane_obj is a lane from graph.get_lane()
lane_obj.entry.event.execute(executor)

So, for example, if the event in the lane_obj's entry node was docking event, and our executor had this dock_execute defined:

def dock_execute(self, dock):
    self.dock_to_wp[dock.dock_name] = self.wp
    print("DOCK EVENT EXECUTED FOR DOCK:", dock.dock_name)

Then it will get called! (Notice how the passed in dock's dock_name attribute is accessible.)

Interacting with rmf_core

Check out the test_adapter.py script in scripts for an integration example with rmf_core! These steps are much easier understood in context.

However, if you have already done that, relevant points are to:

  1. Init rclpy if needed (rclpy.init()), and init rclcpp (adpt.init_rclcpp())
  2. Define your own ChildRobotCommandHandle subclass of adpt.RobotCommandHandle
    • Making sure to define any of the core methods you intend for the rmf_core to be able to call
    • Ensure that the method argument signatures are the same as the template though!
    • If you want the ability to update rmf_core on the robot's status, also be sure to expose a class member for a bound RobotUpdateHandle instance, that can be used to notify rmf_core of any updates to robot position (update_position()), interruptions (interrupted()), or additional delays (add_delay())
  3. Optionally define custom graph.lane.Executorexecutor objects to handle events
  4. Define your map's graph with graph.Graph()
  5. Adding map waypoints: add_waypoint()
    • You can also specify if the waypoints are holding_points (set_holding_point()) or passthrough_points (set_passthrough_point())
  6. And map lanes: add_lane(), add_bidir_lane(), `add_dock_lane()
  7. Add keys to the graph waypoints, which gives the waypoints names that are relevant when requests are sent via the MockAdapter/Adapter to rmf_core
  8. Define your robot
    • By defining its profile: traits.Profile()
    • And instantiating its traits: traits.VehicleTraits()
  9. Create an adapter: adpt.Adapter() or adpt.MockAdapter()
  10. Attach a new fleet: add_fleet()
  11. Define a callback function for task acceptance: accept_task_requests()
  12. Set task planner parameters: set_task_planner_params()
  13. Create a list of Plan Starts: [plan.Start()]
    • You will need to pass in a C++ starting time object, which you can obtain using the now() method from your instantiated Adapter/MockAdapter
  14. And add your robots to your fleet: add_robot()
  15. Then simply request your deliveries!: request_delivery()
  16. Then spin your rclpy nodes if you have any!: spin(), spin_once()

T​hen you're done! :tada:

Running tests

Unit tests have been written for the individual components.

You may invoke pytest directly in the appropriate directory.

# Ensure environment is sourced and you are in the right directory
$ source <workspace_dir>/install/setup.bash
$ cd <rmf_fleet_adapter_python_dir>/tests

# Invoke the tests
$ pytest -v

Or use colcon test with console output redirection.

# Ensure environment is sourced and you are in the right directory
$ source <workspace_dir>/install/setup.bash
$ cd <workspace_dir>

# Invoke the tests
$ colcon test --packages-select rmf_fleet_adapter_python --event-handlers console_direct+

Gotchas

Pointer Indirection Gotchas

  • clone_ptr indirection does not seem to work correctly currently! Be very careful!
  • The only way surefire way to do pointer indirection is to do it via the objects that manage them. Unfortunately there isn't much of a workaround given that most of the pointers point to abstract classes.
  • For most of the other pointers, you must make them using the various factory functions. Do not instantiate them directly since you will not be able to configure their internal members, even if those members are public.
    • No explicit bound methods exist for them as they are meant to be pointers to implementations that might vary widely.

Update Handles Should NOT be Directly Instantiated (Segfault risk!)

  • The RobotUpdateHandle and FleetUpdateHandle classes must only be instantiated via their factory methods! (Their init members have been disabled as a result.)
    • FleetUpdateHandle should be instantiated via TestScenario (via add_fleet)
    • RobotUpdateHandle should be instantiated via FleetUpdateHandle (via add_robot)
    • Doing otherwise will cause their members to contain null values which will lead to segmentation faults!!!

Different Kinds of Waypoints

  • The graph and plan submodules have their own internal Waypoint classes with different, but related interfaces!

Two Kinds of ROS2 Nodes

Seeing how we are binding C++ objects and methods from rmf_core, we inevitably will end up binding an rclcpp Node object. (This will normally come from the Adapter or MockAdapter node members.)

However, if we wanted to, for instance, use rclpy and define our own pub-sub functionalities in our Python script, we'll have an rclpy Node object that we can play around with as well.

The two node types are NOT interchangeable! Keep a VERY strong handle on which nodes are which, and treat them with respect.

The reason why the rclcpp Node has been exposed for you to use, is for an interface to access rmf_core's MockAdapter/Adapter time, as it pertains to the rclcpp ROS2 clock, which is helpfully converted into a Python manipulatable datetime object.

Time Shenanigans

There are three kinds of time that you should be concerned with, owing to different types of time used on the C++ side.

However, confusing things can happen due to how pybind11 provides conversions between those C++ types and Python datetime types. Let me explain...

The three kinds of time that the C++ side uses are:

  • Steady clock timepoint: std::chrono::steady_clock::time_point
    • This gets converted into a datetime.timedelta, where the timedelta represents the time elapsed since the start of the steady_clock's time (that is, since its epoch)
  • System clock timepoint: std::chrono::time_point
    • This gets converted into a datetime.datetime, simple.
  • Duration: std::chrono::Duration
    • This gets converted into a datetime.timedelta, which represents the quantity of time that the Duration represents.

Caught the confusion? Three types of C++ time are being converted into TWO types of Python time, with no way to distinguish between the datetime.timedelta objects that represent Duration objects as opposed to std::chrono::steady_clock::time_point objects.

The conversions back and forth work fine. But you must be very aware of which timedelta objects refer to which.

In an effort to make it simpler, the bindings automatically convert steady_clock::time_point objects to system_clock time_point objects. So that what is meant by the datetime.timedelta objects is clearer.

Caveats

There is also an additional caveat though. The steady_clock::time_point values are relative to the ROS2 clock. That is, it uses the ROS2 epoch.

So when we convert them to system_clock time_point objects, just note that that relative time be used to construct the datetime.datetime object.

So, for example, if the steady_clock time_point was relative to a ROS2 clock with epoch start on 1 Jan 2020 00:00:00, and represented 1 second, and it gets converted automatically by the bindings, the returned datetime.datetime object will actually represent... 1 Jan 1970 00:00:01! Because system clocks are relative to the UNIX epoch!

All that is important is the relative time values. If you keep that as a constant, and pass the datetime objects back into bound methods that use them, there are no additional implications on the correct functioning of the C++ calls that those bound methods will invoke.

CHANGELOG

Changelog for package rmf_fleet_adapter_python

1.3.0 (2021-06-07)

  • Modifications to support refactor of rmf_task (#51 <https://github.com/open-rmf/rmf_ros2/issues/51>_)
  • Fix symlink-install compilation (#32 <https://github.com/open-rmf/rmf_ros2/issues/32>_)
  • Updated package.xml (#26 <https://github.com/open-rmf/rmf_ros2/issues/26>_)
  • Fix/rmf task ros2 cleanup (#21 <https://github.com/open-rmf/rmf_ros2/issues/21>_)
  • Feature/python binding planner (#11 <https://github.com/open-rmf/rmf_ros2/issues/11>_)
  • Adding reference_internal tag to function bindings that return raw pointers (#6 <https://github.com/open-rmf/rmf_ros2/issues/6>_)
  • Feature/add unstable participant api (#11 <https://github.com/open-rmf/rmf_ros2/issues/11>_)
  • Feature/add simple docs (#9 <https://github.com/open-rmf/rmf_ros2/issues/9>_)
  • Support apis for task dispatcher (#10 <https://github.com/open-rmf/rmf_ros2/issues/10>_)
  • differentiate functions to prevent overloading (#8 <https://github.com/open-rmf/rmf_ros2/issues/8>_)
  • support ez traffic light (#7 <https://github.com/open-rmf/rmf_ros2/issues/7>_)
  • Update/release 1.1 (#6 <https://github.com/open-rmf/rmf_ros2/issues/6>_)
  • Implement binding for Duration optional
  • Make integration test even stricter
  • Add reference capture for posterity
  • Add clarifying printouts and fix multi-timer bug
  • Integrate compute_plan_starts into integration test
  • Implement type tests
  • Bind optional constructors and delivery msg interfaces
  • Bind compute_plan_starts
  • Add update_position overload
  • Implement Python Bindings for rmf_fleet_adapter (#1 <https://github.com/open-rmf/rmf_ros2/issues/1>_)
  • Contributors: Aaron Chong, Charayaphan Nakorn Boon Han, Geoffrey Biggs, Grey, Marco A. Gutiérrez, Yadu, methylDragon, youliang

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