Programming Conventions

In the following different programming conventions are introduced. These apply mainly to C++ code. For details about other languages, see also the Interfaces section.


The header files of RobWork are distributed across a number of directories each having its own namespace. The structure of namespaces reflects the directory containing the code. For example

// Include header files:
#include <rw/models/WorkCell.hpp>
#include <rw/kinematics/Frame.hpp>
using namespace rw::models; //Namespace for WorkCell included by #include<rw/models/WorkCell.hpp>
using namespace rw::kinematics; //Namespace for Frame included by #include <rw/kinematics/Frame.hpp>

All classes related to the RobWorkStudio package are placed in a namespace rws.


All classes of the rw directory are provided in a single library named rw.

The subdirectories of the rwlibs directory each correspond to a different library. The subdirectory rwlibs/xyz corresponds to the library named rw_xyz and contains the objects in the namespace rwlibs::xyz. For example, suppose your program contains the following include statement:

#include <rwlibs/pathplanners/rrt/RRTPlanner.hpp>

To build this program, you should link with rw_pathplanners.

C++ shared pointer conventions

The RobWork libraries make extensive use of non-copyable objects (such as object referred to by interface) shared by pointer between different algorithms. Ownership of objects is managed by the shared pointer type rw::core::Ptr. If an object needs access to a non-copyable object, the constructor of the object will conventionally take a rw::core::Ptr type as parameter.

Classes that are commonly referred to by shared pointer define a shortcut for this pointer type. If the class is named T, the name of the pointer type will be T::Ptr, and the type of the pointer will be rw::core::Ptr<T>. Often there will also be a T::CPtr defined for pointers to constant objects that can not be modified. This will give the type rw::core::Ptr<const T>.

Here are some examples of these such pointer types:

  • rw::math::QMetric::Ptr

  • rw::models::WorkCell::Ptr

  • rw::proximity::CollisionDetector::Ptr

  • rw::pathplanning::QSampler::Ptr

  • rw::pathplanning::QToQPlanner::Ptr

Here are some examples of constructor functions for such objects:

  • rw::math::MetricFactory::makeEuclidean()

  • rw::proximity::CollisionDetector::make()

  • rw::pathplanning::QSampler::makeUniform()

  • rwlibs::pathplanners::RRTPlanner::makeQToQPlanner()

The rw::core::Ptr type differs from standard shared pointer implementations by allowing the pointer to refer to a stack allocated object or another object for which an entity has already taken the full ownership. To make the use of such objects easy, a pointer to T can be implicitly converted to Ptr<T>, but the implicitly constructed rw::core::Ptr type does not take ownership of the object. If the rw::core::Ptr type should take ownership of the entity, you must explicitly call the rw::core::ownedPtr() function. This example illustrates the idiom:

 1#include <rw/core/Ptr.hpp>
 3using namespace rw::core;
 5class T {
 6    public:
 7        typedef rw::core::Ptr<T> Ptr;
 8        typedef rw::core::Ptr<const T> CPtr;
10        static Ptr make()
11        {
12            return ownedPtr(new T);
13        }

In everyday programming, the construction of rw::core::Ptr types is managed by the constructor functions for the various objects. Only if you write your own extensions for interfaces in RobWork will you need to explicitly call rw::core::ownedPtr().


There are two main structures for performing serialization in RobWork.

First, there is a XML DOM parser interface that lets you easily read and write XML data. This interface is used throughout RobWork for reading and writing well defined data exchange formats. The XML DOM parser should not be used as a class serialization structure but rather in properly defined loader and saver factories. The XML DOM parser is especially useful as an extension mechanism for current parsing formats, eg. the user can write a plugin that plugs into an existing parsing implementation such as the workcell format.

Secondly, there is a generic serialization framework which enables serialization of classes. This framework enables serialization to different archive types and can be used for serializing many different formats, including binary and user defined formats.

XML DOM parsing

The backend to the XML DOM parser is pluginable and the full features such as validation is therefore depending on the backend. Both boost property tree and xercess backends are supported.

Here is a small example on creating a DOM parser which parses from an input stream (instream)

DOMParser::Ptr parser = DOMParser::make();
DOMElem::Ptr root = parser->getRootElement();

The root class can now be used for parsing the xml data. Lets assume we have a xml file that looks like:

<group someboolproperty="true" someintproperty="10">
  <elem-string> </elem-string>
  <elem-int> </elem-int>
  <elem-double-list>0.1 0.2 0.1</elem-double-list>
DOMELem::Ptr gelem = root->getElement("group"); // throws an error if "group" is not there
bool boolprop = gelem->getAttributeValueAsBool("someboolproperty", true); // defaults to true
int intprop = element->getAttributeValueAsInt("someintproperty", 2); // defaults to 2
// iterate over all elements in group
for(DOMElem::Ptr child : gelem->getChildren()) {
    if(child->isName("elem-string")) {
        std::string str-value = child->getValue();
    } else if (child->isName("elem-int")) {
        int int-val = child->getValueAsInt();
    } else if (child->isName("elem-double-list")) {
        std::vector<double> double-val-list = child->getValueAsDoubleList(); // default seperator is space ' '

Generic serialization

The generic serialization is not a centralized controlled serialization as the XML serialization described above. There are two main concepts to the generic serialization: Archive, Serializable

An Archive is an instance that formats data that can be read or written to it from a Serializable object/class. As such interfaces for both InputArchive and OutputArchive exists. A serializable class is a class that either inherits from the rw::common::Serializable (intrusive) or using an extrusive way that overwrites the functions

void rw::common::serialization::write(const THE_CLASS&, OutputArchive& oar);
void rw::common::serialization::read(THE_CLASS&, InputArchive& iar);

In any case save and load methods should be determined for the individual serializable classes. In these methods read/write operations on the archive should be performed such as to store or restore the state of the class. The use of an archive can be quite simple:

INIArchive ar(std::cout);
ar.write(1, "length");
ar.write(1.120, "width");
ar.write(5.120, "height");
length : 1
width : 1,120
height : 5.120

As can be seen an identifier can be associated to the value that is to be serialized. This id makes type checking possible and should allways be used/defined. The archive interface define serialization of primitives (int, float, double, string) and vectors of primitives. More complex types will need to implement their own serialization pieced together from the primitives.

Finally, the archive also defines a way to enter scopes. A scope is simply a grouping of value-identifier pairs.

void write(const KDTreeQ<VALUE_TYPE>& out, OutputArchive& oarchive) {
    oarchive.write(out._dim, "dim");
    oarchive.write((int)out._nodes->size(), "nrNodes");
    oarchive.write((boost::uint64_t)out._root, "rootId");
    for(int i=0;i<out._nodes->size();i++) {
        const TreeNode &node = (*out._nodes)[i];
        oarchive.write((boost::uint64_t)&node, "id");
        oarchive.write( node._axis, "axis");
        oarchive.write( node._deleted, "del");
        oarchive.write( node._left, "left");
        oarchive.write( node._right, "right");
        oarchive.write( node._kdnode->key, "Q");
        oarchive.write( node._kdnode->value, "value");

Archive types

There is currently: * an rw::common::INIArchive which prints in INI format * an rw::common::BINArchive which prints in binary compressed format


When developing code that can run in parallel it is encouraged that the RobWork rw::common::ThreadPool concept is used. The ThreadPool provides a flexible way for the user to specify the number of threads that should be used in multi-threaded applications. By adding work to a ThreadPool instead of launching separate threads, the user has the ability to limit the resources used by the application. At the same time the application is able to efficiently utilize the given resources.

The following example shows how a rw::common::ThreadPool can be created, and how work is added to the queue. The work added to the pool is handled in the order it is added. In this example a list of arguments can be given from commandline. The first argument is the number of threads to use (beside the main thread itself). Followed by that is an arbitrary number of image filenames. The example will then try to load the image files in parallel according to the number of threads given by the user. Note that zero threads is valid, as this would cause all work to be executed directly in the main thread. Work will in this case be executed directly in the addWork function. To avoid that the program ends before work has finished, the waitForEmptyQueue() function will block until there is no more work available.

In the loadFile function the isStopping() method is checked. If the rw::common::ThreadPool is stopped before work has finished, the work added to the pool should check the isStopping() function at regular intervals to allow the application to shutdown gracefully. This is especially important in long running tasks.

 1#include <rw/common/ThreadPool.hpp>
 2#include <rw/loaders/ImageLoader.hpp>
 4#include <boost/bind/bind.hpp>
 5#include <boost/function.hpp>
 7using namespace rw::common;
 8using namespace rw::core;
 9using rw::loaders::ImageLoader;
10using rw::sensor::Image;
12static boost::mutex outMutex;
13static boost::mutex imageMutex;
14static std::vector<Image::Ptr> images;
16void loadFile(ThreadPool* pool, const std::string &file) {
17	{
18		boost::mutex::scoped_lock lock(outMutex);
19		std::cout << "Loading " << file << std::endl;
20	}
21	Image::Ptr image;
22	try {
23		image = ImageLoader::Factory::load(file);
24	} catch (Exception&) {
25		image = NULL;
26	}
27	if (pool->isStopping())
28		return;
29	{
30		boost::mutex::scoped_lock lock(imageMutex);
31		images.push_back(image);
32	}
35int main(int argc, const char* argv[]) {
36	ThreadPool::Ptr pool = ownedPtr(new ThreadPool(std::atoi(argv[1])));
37	for (int i = 2; i < argc; i++) {
38		ThreadPool::WorkFunction work = boost::bind(&loadFile,boost::arg<1>(),argv[i]);
39		pool->addWork(work);
40	}
41	pool->waitForEmptyQueue();
42	return 0;

When writing multi-threaded applications one often need to branch out into many parallel tasks, then wait for the tasks to finish and finally combine the results. Then one might need to branch out yet again into new parallel tasks based on the result. Also each parallel task might be able to parallize its work into even more subtasks. This can in principle happen in a hierachy where each subtask can have its own parallel subtasks and so forth.

To facilitate easy programming of such types of tasks, the rw::common::ThreadTask can be used. The following shows the same example as before but instead implemented by the use of the rw::common::ThreadTask type.

Consider the LoadTask class that inherits from rw::common::ThreadTask. The LoadTask class is constructed with a filename that should be loaded. In the run function the main work takes place - here the file is actually loaded, and the result can afterwards be retrieved with the getImage function.

The MainTask class also inherits from rw::common::ThreadTask. This task is responsible for launching a LoadTask for each file given as input to the program. This class does not do much work in its run function. It only constructs all LoadTasks and add these as subtasks to the MainTask. The subtasks starts running as soon as they are added, and when each subtask finishes, the subTaskDone function is called. Here the result of the subtask is simply stored. If it was desired, new tasks could also be launched based on the achieved result. When there are no more subtasks running, the idle function is called. The idle function is the last chance to add new subtasks, otherwise the task will end and the done function will be called. When done is called the task is finished, and it is not possible to add more subtasks. If this task then has a parent task, the parent tasks subTaskDone will be executed. It is possible to change the behaviour of a rw::common::ThreadTask such that it does not finish after the idle function is called. By enabling the keep-alive option the task will stay in the idle state until the keep-alive is disabled or new subtasks are added. Be careful with this option. If the task does not end, the parents will not end either.

The main function is almost as before. This time a MainTask is created that uses the ThreadPool to add its work to. When the execute function is called the work is added to the pool and execution starts. By using the waitUntilDone function the main loop will not end before the MainTask has finished.

Note that multiple separate tasks and worker function can use the same pool at the same time.

As the ThreadPool is able to run with zero threads (executing directly in the main thread), so is the ThreadTask. If zero threads are used, the complete task will be done at the time execute returns. It is however not recommended to use zero threads with the ThreadTask. Internally this will cause a large recursion depth, and will inevitably cause problems with the stack size at some point.

 1#include <rw/common/ThreadPool.hpp>
 2#include <rw/common/ThreadTask.hpp>
 3#include <rw/loaders/ImageLoader.hpp>
 5using namespace rw::common;
 6using namespace rw::core;
 7using rw::loaders::ImageLoader;
 8using rw::sensor::Image;
10static boost::mutex outMutex;
12class LoadTask: public ThreadTask {
14	LoadTask(ThreadTask::Ptr parent, const std::string &file): ThreadTask(parent), _file(file) {};
16	Image::Ptr getImage() const { return _image; }
18	void run() {
19		{
20			boost::mutex::scoped_lock lock(outMutex);
21			std::cout << "Loading " << _file << std::endl;
22		}
23		try {
24			_image = ImageLoader::Factory::load(_file);
25		} catch (Exception&) {
26			_image = NULL;
27		}
28	}
31	const std::string _file;
32	Image::Ptr _image;
35class MainTask: public ThreadTask {
37	MainTask(ThreadPool::Ptr pool, const std::vector<std::string> &files): ThreadTask(pool), _files(files) {};
39	std::vector<Image::Ptr> getImages() {
40		boost::mutex::scoped_lock lock(_imageMutex);
41		return _images;
42	}
44	void run() {
45		for(const std::string &file : _files) {
46			LoadTask::Ptr task = ownedPtr(new LoadTask(this,file));
47			addSubTask(task);
48		}
49	}
51	void subTaskDone(ThreadTask* subtask) {
52		LoadTask* task = static_cast<LoadTask*>(subtask);
53		boost::mutex::scoped_lock lock(_imageMutex);
54		_images.push_back(task->getImage());
55		// More subtasks could be added at this point
56	}
58	void idle() {
59		boost::mutex::scoped_lock lock(outMutex);
60		std::cout << "All images loaded!" << std::endl;
61		// More subtasks could be added at this point
62	}
64	void done() {
65		boost::mutex::scoped_lock lock(outMutex);
66		std::cout << "Main Task ended!" << std::endl;
67		// No subtasks can be added now
68	}
71	const std::vector<std::string> _files;
72	boost::mutex _imageMutex;
73	std::vector<Image::Ptr> _images;
76int main(int argc, const char* argv[]) {
77	ThreadPool::Ptr pool = ownedPtr(new ThreadPool(std::atoi(argv[1])));
78	std::vector<std::string> files;
79	for (int i = 2; i < argc; i++)
80		files.push_back(argv[i]);
81	MainTask task(pool, files);
82	task.execute();
83	task.waitUntilDone();
84	std::cout << "Images loaded: " << task.getImages().size() << std::endl;
85	return 0;