Dora-rs 机器人框架学习教程（1）—— Dora-rs安装

本文介绍: Dora-rs[1] 是一个基于 Rust 实现的化机器人框架，其具有极高的实时性能。Dora-rs使用Rust语言做数据流的传输和调度管理，可以大大减少了数据的重复拷贝和传输。它提供了Rust语言和Python语言之间的无缝集成，减少了跨语言的性能代价。Dora-rs通过YAML脚本配置节点、节点之间的数据流。：Dora 目前提供 Rust 、Cpp 、Python三种语言。：Dora-rs 性能是ROS2 Python API 的 17 倍！是 ROS2 Rust API 的 10 倍！

Dora-rs[1] 是一个基于 Rust 实现的化机器人框架，其具有极高的实时性能。Dora-rs使用Rust语言做数据流的传输和调度管理，可以大大减少了数据的重复拷贝和传输。它提供了Rust语言和Python语言之间的无缝集成，减少了跨语言的性能代价。Dora-rs通过YAML脚本配置节点、节点之间的数据流。

多语言支持：Dora 目前提供 Rust 、Cpp 、Python三种语言。

性能：Dora-rs 性能是ROS2 Python API 的 17 倍！是 ROS2 Rust API 的 10 倍！与 ROS2 C/Cpp API 共享内存快 0.06 ms。(图片来源于github[1]) （对Python有很强的优化能力，对C++性能提升不大）

请添加图片描述 )

dora_安装文档（官方提供三种安装方法，推荐使用二进制安装）：https://dora.carsmos.ai/docs/guides/Installation/installing/

dora_rs版本为 V0.3.0
注意python版本必须要3.11.5

export DORA_VERSION=v0.3.0 # Check for the latest release
export ARCHITECTURE=$(uname -m)
wget https://github.com/dora-rs/dora/releases/download/${DORA_VERSION}/dora-${DORA_VERSION}-${ARCHITECTURE}-Linux.zip
unzip dora-${DORA_VERSION}-${ARCHITECTURE}-Linux.zip
pip install dora-rs==${DORA_VERSION} ## For Python API
PATH=$PATH:$(pwd)
dora --help

sudo ./install_dora.sh

apt autoremove rustc
apt install rustc

sudo apt autoremove rustc
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
export PATH=~/.cargo/bin:$PATH
sudo apt  install cargo

dora new abc_project --lang python
cd abc_project

├── dataflow.yml
├── node_1
│   └── node_1.py
├── op_1
│   └── op_1.py
└── op_2
    └── op_2.py

nodes:
  - id: op_1
    operator:
      python: op_1/op_1.py
      inputs:
        tick: dora/timer/millis/100
      outputs:
        - some-output
  - id: op_2
    operator:
      python: op_2/op_2.py
      inputs:
        tick: dora/timer/secs/2
      outputs:
        - some-output

  - id: custom-node_1
    custom:
      source: python3
      args: ./node_1/node_1.py
      inputs:
        tick: dora/timer/secs/1
        input-1: op_1/some-output
        input-2: op_2/some-output

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

from dora import Node

node = Node()

event = node.next()
if event["type"] == "INPUT":
    print(
        f"""Node received:
    id: {event["id"]},
    value: {event["value"]},
    metadata: {event["metadata"]}"""
    )

from typing import Callable, Optional

from dora import DoraStatus


class Operator:
    """
    Template docstring
    """

    def __init__(self):
        """Called on initialisation"""
        pass

    def on_event(
        self,
        dora_event: dict,
        send_output: Callable[[str, bytes, Optional[dict]], None],
    ) -> DoraStatus:
        if dora_event["type"] == "INPUT":
            return self.on_input(dora_event, send_output)
        return DoraStatus.CONTINUE

    def on_input(
        self,
        dora_input: dict,
        send_output: Callable[[str, bytes, Optional[dict]], None],
    ):
        """

        Args:
            dora_input (dict): Input dict containing an `id`, `data` and `metadata`.
            send_output Callable[[str, bytes | pa.Array, Optional[dict]], None]:
                Function for sending output to the dataflow:
                - First argument is the `output_id`
                - Second argument is the data as either bytes or `pa.Array`
                - Third argument is dora metadata dict
                e.g.: `send_output("bbox", pa.array([100], type=pa.uint8()), dora_event["metadata"])`

        Returns:
            DoraStatus:
                CONTINUE means that the operator will
                    keep listening for further inputs.
                STOP means that the operator stop listening for inputs.

        """
        print(f"Received input {dora_input['id']}, with data: {dora_input['value']}")

        return DoraStatus.CONTINUE

    def __del__(self):
        """Called before being deleted"""
        pass

from typing import Callable, Optional

from dora import DoraStatus


class Operator:
    """
    Template docstring
    """

    def __init__(self):
        """Called on initialisation"""
        pass

    def on_event(
        self,
        dora_event: dict,
        send_output: Callable[[str, bytes, Optional[dict]], None],
    ) -> DoraStatus:
        if dora_event["type"] == "INPUT":
            return self.on_input(dora_event, send_output)
        return DoraStatus.CONTINUE

    def on_input(
        self,
        dora_input: dict,
        send_output: Callable[[str, bytes, Optional[dict]], None],
    ):
        """

        Args:
            dora_input (dict): Input dict containing an `id`, `data` and `metadata`.
            send_output Callable[[str, bytes | pa.Array, Optional[dict]], None]:
                Function for sending output to the dataflow:
                - First argument is the `output_id`
                - Second argument is the data as either bytes or `pa.Array`
                - Third argument is dora metadata dict
                e.g.: `send_output("bbox", pa.array([100], type=pa.uint8()), dora_event["metadata"])`

        Returns:
            DoraStatus:
                CONTINUE means that the operator will
                    keep listening for further inputs.
                STOP means that the operator stop listening for inputs.

        """
        print(f"Received input {dora_input['id']}, with data: {dora_input['value']}")

        return DoraStatus.CONTINUE

    def __del__(self):
        """Called before being deleted"""
        pass

dora start dataflow.yml --name first-dataflow

dora stop --name first-dataflow

dora logs first-dataflow op_1
dora logs first-dataflow op_2
dora logs first-dataflow custom-node_1

pip install dora-rs==0.3.0 --force

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dora rs rust

1、dora简介

2、dora安装

2.1 二进制安装

3、运行测试程序

3.1 first project

3.2 编写节点

3.3 启动程序

3.4 查看节点输出

4 注意事项

参考资料

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