ROS机器人操作系统完全参考指南(第三卷)

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"Robot Operating System (ROS) - The Complete Reference (Volume 3) 是一本由Anis Koubaa编辑的专业书籍，聚焦于ROS系统，包含了实际操作示例、演示和插图，旨在全面介绍ROS技术。该书属于'Studies in Computational Intelligence'系列，由Janusz Kacprzyk担任系列编辑，旨在快速且高质量地发布计算智能领域的最新进展和应用。" ROS（Robot Operating System）是机器人技术领域的一个开源操作系统，为机器人设备和软件提供了一个标准化的框架。它提供了一整套工具、库和协议，使开发人员能够轻松地构建复杂的机器人应用程序。ROS的主要目标是促进硬件抽象、低级别设备控制、消息传递、软件包管理、任务规划等多个组件之间的通信。这本书的第三卷深入探讨了ROS的各种方面，可能包括以下知识点： 1. **ROS基础知识**：涵盖ROS的核心概念，如节点(Node)、消息(Message)、服务(Service)、参数(Parameter Server)以及图形化工具如rqt和rviz的使用。 2. **ROS架构**：解释ROS的层次结构和模块化设计，以及如何通过ROS Master和ROS Graph实现节点间的发现和通信。 3. **ROS包管理**：介绍如何使用catkin工作空间创建、编译和部署ROS软件包，以及如何利用rosdep来管理依赖关系。 4. **传感器和执行器接口**：详细讲解ROS如何与各种传感器（如激光雷达、摄像头）和执行器（如电机）交互，包括传感器数据的处理和解析。 5. **导航和移动机器人**：讨论ROS的移动机器人导航堆栈（包括amcl、move_base等），以及路径规划、定位和避障策略。 6. **SLAM（Simultaneous Localization and Mapping）**：介绍如何在ROS中实现同时定位与建图，如使用Gmapping或 Hector SLAM。 7. **ROS编程**：讲解使用C++和Python进行ROS编程的基本方法，包括编写节点、订阅和发布消息、实现服务等。 8. **ROS实战示例**：书中可能包含实际机器人项目案例，展示如何将ROS应用于机器人控制系统的设计和实现。 9. **高级主题**：可能涉及ROS的并发模型、多机器人协作、ROS2新特性，以及如何将ROS系统与其他系统（如ROS-I for industrial robots或ROS-Industrial for manufacturing environments）集成。 10. **调试与测试**：介绍ROS中的调试工具，如rosbag用于记录和回放数据，以及如何编写单元测试和功能测试确保代码质量。通过阅读《ROS - The Complete Reference (Volume 3)》，读者将能深入理解ROS的各个方面，不仅学习到理论知识，还能通过实例获得实践经验，为机器人系统开发打下坚实基础。

A ROS-Based Framework for Simulation and Benchmarking… 7

Fig. 1 A patrol graph displayed on top of a metric map to be used in multi-robot patrolling tasks.

The blue dots represent the vertices of the graph that must be visited, while the red arcs represent

the edges that connect pairs of vertices

environments), and simulation testbeds for MRS are nowadays crucial to rapidly

reproduce experiments [ 84]. While ROS and Stage provide the key building blocks

to develop realistic simulations of robotics systems, there is no ready-to-use frame-

work that allows researchers to run experiments testing and validating multi-robot

coordination strategies.

Against this background we present patrolling_sim,

a ROS-based framework for

simulation and benchmarking of multi-robot patrolling algorithms, which has been

developed and used by the authors in previous works [33, 85]. The patrolling_sim

framework allows to run exhaustive tests in different scenarios and with different team

sizes in fairly realistic environments, and ultimately to execute quicker experiments

in the real world by mimicking the setting up of simulated experiments. In the next

section, we describe such a framework in more details.

http://wiki.ros.org/patrolling_sim.

A ROS-Based Framework for Simulation and Benchmarking… 9

with engineering solutions for their particular hardware setup [87]. In the past, many

robotic researchers solved some of those issues by presenting a wide variety of

frameworks to manage complexity and facilitate rapid prototyping of software for

experiments, thus resulting in the many robotic software systems currently used in

academia and industry, like YARP [88], Orocos [89], CARMEN [90] or Microsoft

Robotics Studio [91], among others. Those frameworks were designed in response

to perceived weaknesses of available middlewares, or to place emphasis on aspects

which were seen as most important in the design process. ROS is the product of

trade-offs and prioritizations made during this process [1].

The major goals of ROS are hardware abstraction, low-level device control, imple-

mentation of commonly-used functionalities, message-passing between processes

and package management. ROS promotes code reuse with different hardware by

providing a large amount of libraries available for the community, like laser-based

2D SLAM [92], 3D point cloud based object recognition [93], among others, as well

as tools for 3D visualization (rviz), recording experiments and playing back data

ofﬂine (rosbag), and much more.

Regular updates and broad community support enable the users to obtain, build,

write, test and run ROS code, even across multiple computers, given its ability to run

distributedly in many processors. Additionally, since it is highly ﬂexible, with a sim-

ple and intuitive architecture, ROS allows reusing code from numerous other open-

source projects such as several Player robot drivers, the Stage 2D and Gazebo 3D

simulation environments, Orocos, mostly for industrial robots and machine control,

vision algorithms from the Open Source Computer Vision (OpenCV) library [94],

etc. As a result, integrating robots and sensors in ROS is highly beneﬁcial.

Due to its peer-to-peer, modular, tools-based, free and open-source nature, ROS

helps software developers in creating robotic applications in a quick and easy way.

These applications can be programmed in C++, Python, LISP or Java, making ROS a

language-independent framework. Furthermore, ROS places virtually all complexity

in libraries, only creating small executables, i.e. nodes, which expose library func-

tionalities to ROS. Nodes communicate by publishing or subscribing to messages

at a given topic.Thetopic is a message bus, typically named so that it is easy to

identify the content of the message. Hence, a node that requires a certain kind of

data, subscribes to the appropriate topic. There may be multiple concurrent publish-

ers and subscribers for a single topic, and a single node may publish and/or subscribe

to multiple topics. The idea is to decouple the production of information from its

consumption.

Beyond the easiness of using the available tools, ROS also provides seamless

integration of new sensors without the need for hardware expertise. As a result,

the overall time spent in developing software is greatly reduced due to code reuse

and hardware abstraction, when using available ROS drivers to interface with the

hardware (Fig. 3).

A ROS-Based Framework for Simulation and Benchmarking… 11

base. Stage is relatively easy to use, it is realistic for many purposes, yielding a use-

ful balance between ﬁdelity and abstraction that is different from many alternative

simulators. It runs on Linux and other Unix-like platforms, including Mac OS X,

which is convenient for most roboticists, and it supports multiple robots sharing a

world. Moreover, Stage is also free and open-source, has an active community of

users and developers worldwide, and has reached a well-known status of being a

robust simulation platform.

Stage is made available for ROS, through the stageros node from the stage_ros

package,

which wraps the Stage multi-robot simulator. Using standard ROS topics,

stager os provides odometry data from each virtual robot and scans from the corre-

sponding laser model. Additionally, a ROS node may interact with Stage by sending

velocity commands to differentially drive the virtual robot.

3.3 Installation and Initializing Experiments

At the time of writing, the patrolling simulation framework supports the latest ROS

Long-Term Support (LTS) release: ROS Kinetic Kame. Assuming one is running

Ubuntu Linux OS, the installation steps are quite simple as seen below:

1. Install ROS Kinetic Kame, following the instructions at:

http://wiki.ros.org/kinetic/Installation/Ubuntu

2. Install needed dependencies, by typing in the terminal:

$ sudo apt install ros-kinetic-move-base ros-kinetic-

amcl ros-kinetic-map-server

3. Setup your ROS catkin workspace, by typing in the terminal:

$ mkdir -p ∼/catkin_ws/src

$ cd ∼/catkin_ws

$ catkin_make

$ source devel/setup.bash

4. Add the following two lines to your bash conﬁguration ﬁle (at /home/$USER/.

bashrc):

source ∼/catkin_ws/devel/setup.bash

export ROS_WORKSPACE=∼/catkin_ws

5. Download and compile patrolling_sim:

$ roscd; cd src

$ git clone https://github.com/davidbsp/patrolling_sim

$ roscd; catkin_make

After successfully downloading and compiling t he patrolling_sim framework, one

can easily initiate and conﬁgure multi-robot patrolling experiments by running the

start_experiment.py script as seen in Fig. 4:

$ rosrun patrolling_sim start_experiment.py

http://wiki.ros.org/stage_ros.

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