Robot arm part 2 : ROS connection

As the name implies, ROS is not just another library to get familiar with. It is an operating system for robots. That is also quite different to a traditional operating system. As I didn’t want to learn a whole bunch of stuff first, I learn about the concepts and facilities as I move along.

After I modeled the robot arm with a urdf xml file, and it moved in the simlator, I wanted to connect ros to the physical arm. I found some tutorials for rosserial on how to connect to an arduino. So, I adapted these examples to the robot arm. The microcontroller board has many similarities to an arduino, but some things are different. First, I compiled the firmware. I had to copy some files from rosserial_arduino, and modified them accordingly. Hooking up the servos as ros subscribers is actually quite easy. The arduino examples use a standard python script on the computer. It looked as if I could use the same. But the robot arm only runs when the RTS level is high. As most libs and programs don’t do that by default, my robot arm did nothing. So, I copied some scripts from rosserial and modified them. In the process I learned about the statserial program that displays the status of the different serial pins. Now, the arm moved to the initial position and waited. Meanwhile I tried to connect to it with the modified python script, but I still got “Lost sync with device, restarting…”.

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Optimizing compile time of a large C++ project

The codebase of our PointLine CAD is certainly quite large. sloccount calculated roughly  770’000 lines of C++ code. I know, this is not a very good metric to describe a project, but it gives an idea. Over time the compile time steadily increased. Of course we also added a lot of new stuff to the product. We also used advanced techniques to reduce the risk of bugs, that have to be paid with compile time. But still, the increase was disproportionate. We mitigated it by using IncrediBuild. Just like distcc, it distributes the compilation load across different machines on the LAN. If I’m lucky, I get about 20 cores compiling for me.

About once a year, one of us does some compile time optimization and tunes the precompiled headers. I did so about three years ago, and then this week it was my turn again. Reading what I could find about precompiled headers on the internet and applying that, I could get only a small speedup, roughly 10%. So I cleaned up the physical structure of the codebase. Here are some things I performed: Continue reading “Optimizing compile time of a large C++ project”

OpenCL First Steps

There is an increasing noise about GPGPU computing and how much faster than CPU (even parallel) it is. If you didn’t hear about all that, GPGPU is about using the computer’s graphics card(s) to do general purpose computations. The key to the performance lies in the parallel architecture of these devices. From what I read, an average graphics card has 64 parallel units, but they are not as versatile as the CPU of which a typical PC these days has 4 cores. That means, if the task is well suited, it can boost performance significantly, but if not, it’s nothing more than a lot of wasted work.

So I wanted to see for myself. To get started I read the book “OpenCL Programming Guide“. It gave a good overview. But now it was time to give it a try.

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