rosR

Overview

This package provides an simple interface of standard ros-functionalities for the programming language R. We hope that it might be useful to make the R capabilities for statistical analyses and visualization also usable for the robotic community. In contrast to other language implementations, such as rospy, rosjava, etc. this is not a pure R implementation, it is a mixture of pure R, SWIG generated functions, and system commands. This combination was required to overcome some limitations of R, such as single threaded, lacking support for sockets and handling of raw streams. Nevertheless, this package can be used to define and use typical ROS publishers and subscribers in R, messages are automatically and online generated from the definition files, and it integrates the possibility to read and therefore analyse bag-files in R. We will explain this in more detail within the next sections.

See also rosR_demos.

Installation

We divided this section into two parts. For those who come from the ROS side, it will be probably easy to install and to use this packet (of course they will probably have to cope with the R-code). Coming from the R side with no experience about ROS, it will be hard to install and run this package.

ROS-Side

If you have already some expertise on working with ROS, then you can install rosR just like any ordinary ros-package. Three additional dependencies are required before you can compile it:

• Swig3.0 is required for interfacing C code, and only swig3.0 can generate appropriate code for R.

  $ sudo apt-get install swig3.0

• The second part is an R base installation

  $ sudo apt-get install r-base

• r-cran-rcpp includes all required C source to develop R packages

  $ sudo apt-get install r-cran-rcpp

Afterwards compile this package with catkin_make... That's all folks!

If the compilation fails, because lib lRcpp was not found, search for it:

$ locate Rcpp.so
/usr/lib/R/site-library/Rcpp/libs/Rcpp.so

enter the directory and create a symbolic link:

$ cd /lib/R/site-library/Rcpp/libs
$ sudo ln -s Rcpp.so libRcpp.so
$ cd -

and run catkin_make again ...

R-Side

Within this subsection we will describe all steps that are required to install ros-indigo under an Ubuntu 14.04 32-bit (with long time support) and then our extension for the R-programming language (especially for users with totally no ROS experience). The first steps were taken from the manual (http://www.ros.org/wiki/indigo/Installation/Ubuntu) and we guess, you already have installed Ubuntu on your PC.

Publish Subscribe

R–Publisher

Defining a publisher in R is nearly as simple as in Python. First of all you will have to load this package into your R environment, what can be done with the following command:

> source(paste(system("rospack find rosR",intern=T),"/lib/ros.R",sep=""),chdir=T)

This looks a bit complex, but have trust, this is the only complex command that is required. We did not develop a package that can be directly installed in R but more a ros package and therefore it can be somewhere on your systems, as ros packages do it normaly. Therefore the commandline program rospack is involved to find the location of your rosR installation. But thats all, now you can use all of our ros-functions in R.

As in most programs you start with the initialization of your new ros node and so we do:

> ros.Init("R_node")

And the new node appears... Let us now generate the publisher:

> publisher <- ros.Publisher("chatter", "std_msgs/String")

Simply call ros.Publisher with the new topic, in our case “chatter”, and the message type that is transmitted “std_msgs/String”. In the same way it is also possible to define a new message:

> message <- ros.Message("std_msgs/String")

Messages in our case are always defined as list, that may include further list. So it is possible to set and get messages values in a similar war, as you know you know it from other ROS language implementations:

> message$data <- "hello world"

Now we can pass this message to the publisher as follows:

> ros.WriteMessage(publisher, message)

And that was all ... cange the content of your message and republish it:

> message$data <- "hello world"

Have look at some complete examples in folder nodes/test/publisher.R

R-Subscriber

Creating a subscriber is as simple creating a publisher. At first you have to load the package, initialize the node and then create the subscriber:

> source(paste(system("rospack find rosR",intern=T),"/lib/ros.R",sep=""),chdir=T)
> ros.Init("R_node2")
> subscriber <- ros.Subscriber("chatter", "std_msgs/String")

As mentioned before, R is single threaded, and calling callback functions is nearly not possible. To circumvent this tiny drawback, you have to poll, if a new messages was received or not. Therefore, you have to call:

> ros.SpinOnce()
NULL

to fill the subscriber with possibly new messages. The receipt of a new message can than be identified by calling the following method:

> ros.SubscriberHasNewMessage(subscriber)
[1] TRUE

This function call will return TRUE if a new message was received otherwise FALSE. If a new message was received, this can simply be read with:

> message <- ros.ReadMessage(subscriber)
> print(message$data)
[1] hello world

Check out the examples in folder rosR/nodes.

Messages

The subscriber generates automatically the correct message format. If you publish a message it is recommended to use function:

> msg <- ros.Message("std_msgs/String")

If you want to get a message of another format, like for example a laserscan, you wil get the following result:

> msg <- ros.Message("sensor_msgs/LaserScan")

A message in this case is always a composition of lists, therefor single elements are accessed with "$". Thus, the structure of a message is quite similar to the structures in other languages, but instead of a point, you have to use a dollar. Changing and reading the header sequence would than be done as follows:

> print(msg$header$seq)
[[1]]
integer(0)
> msg$header$seq <- 100
> print(msg$header$seq)
[1] 100

The handling of arrays is a bit tricky, because in the background these are handled as C structures std::vector. Thus, the size of our new LaserScan is currently 0:

> length(msg$ranges)
[1] 0

and you can add new elements and read these values in the normal manner:

> append(msg$ranges, c(1,2,3,4,5,6,7))
> length(msg$ranges)
[1] 7
> msg$ranges[2:4]
[1] 2 3 4
> msg$ranges[2:4] <- c(4,3,2)
> msg$ranges
[1] 1 4 3 2 5 6 7

But calling functions like sum(msg$ranges) or median(msg$ranges) will not work, unless you define it in file lib/std_vector.R, or you call:

> sum(scan$ranges[1:7])
[1] 28
> median(scan$ranges[1:7])
[1] 4

This generates a copy of the structures within the std::vector and returns a R vector:

> typeof(msg$ranges)
[1] "S4"
> typeof(msg$ranges[1:7])
[1] "double"

Otherwise it would slow down the conversion of messages, just think of a camera image with 800x600 pixels with 24bits ... The handling of std::vectors in R is defined in lib/std_vector.R you are free to add new functionality ...

Other Functions

Bag-Files

At the moment it is only possible to load bagfiles into R. Use therefore the following function:

> bag <- ros.BagRead(file, c("topic_1", "topic_2", ..., "topic_n"))

You will receive a list, containing the messages, the timestamps, topics, and datatypes of every message:

> bag$topic[2]
> bag$message[2]$... # handled in the same way, as a normal message
> bag$datatype[2]

Parameter-Server

todo

Misc

There is also other functionality defined in src/ros.cpp and lib/ros.R like:

> ros.TimeNow()
> ros.Info("info")
> ros.Debug("...")
> ros.Error("...")
> ros.Warn("...")