Hardware & Sofware: design and implementaton. Andrea Ranieri

Transcription

1 & Sofware: design and implementaton Andrea Ranieri

2 Part 1

3 What's the purpose of designing and developing new hardware components for UMVs? Each component basically has to solve one (well, sometimes more than one) specific problems: engines and controllers provide propulsion sensors provide data computing boards provide processing capacity to coordinate everything

4 Design of new non-trivial components is essential It allows to carefully plan and somehow simulate the purchase/development process: 1. formalize the requirements 2. do a make-or-buy decision 3. design an alpha-prototype on paper 4. see if everything fits mechanically and electrically into the UMV (and maybe into the other UMVs) simulate unit tests and integration tests 5. goto step 1 and refine until error < ε

5 Use case: build a high performance Computer Vision system to be mounted on a UMV. Step 1: requirements what does it mean high performance? in the context of UMVs, where hardware is usually close to industrial-grade specifications, high performance may be everything >= to a decent desktop computer able to do advanced processing on HD stereo images at high FPS.

6 Step 1.01: rule out impossible requirements no LIDAR underwater underwater LIDAR systems exist but are way too expensive no Kinect (no IR underwater)

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8 Step 2: make or buy? It largely depends on the underlying technology. Buy: Buy the process is fast & easy if the technology is reliable enough usually costs less money than R&D + parts sometimes the software is already available, working and tested if the component is backed by a big open-source community -> 90% of work is already done

9 Step 2: make or buy? It largely depends on the underlying technology. Make: Make the process is slow and usually expensive (both in parts and in effort) if the technology is bleeding-edge, disruptive or niche market, it may yield substantial savings or even a profit both hardware and software will be taylormade for the application.

10 Step 2: make or buy the stereo camera? definitely buy (and go mainstream) Bumblebee XB3 is relatively inexpensive lot of software and functionalities already available a large community of users tech specs may be better firewire is a pain in the ass

11 Step 2: make or buy the high performance mini-pc? Raspberry Pi 3, ODROID-C2/XU3 are not enough Intel NUC CPUs are fine but NUCs can't be upgraded and their GPUs are not so fast Best long-term solution: make a high performance mini-pc

12 Step 3: alpha-prototype on paper Powerful i7 mini-pc, with room for additional GPU Bumblebee XB3 (very compact) Firewire underwater cable

13 Step 3: alpha-prototype on paper Mini-pc i7 specs: 1. Intel Core I7-6700K 2. ASRock Z170 Gaming-ITX/ac 3. 2x8GB DDR4 3733MHz LPX 4. Samsung 256GB SSD SM951 M.2 5. StarTech Mini PCI-Express to FireWire adapter 6. [Leave room for an additional GPU]

14 Step 3: alpha-prototype on paper Mini-pc i7

15 Step 4: see if everything fits mechanically and electrically (and thermically?) the PCI-Ex 16x slot is completely on the edge of the board -> the cylinder will be considerably bigger than the motherboard form factor or it won't be a cylinder at all? or we could try another PCI-Ex extender when the cylinder is sealed, the system will probably produce enough heat to become unstable in air circuit partitioning and power on via SW

16 What's next?

17 GPS Laser scanners Radar accelerometers gyroscopes Acoustic data wheel encoders Inertial data small radar arrays mounted around the side of the vehicle Odometry dense 3D point cloud colored as seen by the laser: intensity of echoed infrared laser beams proximity Visual data cameras

18 Obviously, we can't compete with Google, if only for the budget But we can try to copy Google cars has about 150k in equipment and uses 70k Velodyne HDL-64E as LIDAR system On the sea surface and in air, we could use Velodyne VLP-16 ~= 10k overall

19 Google LIDAR Navigaton System

20 Questions?

21 Questions?

22 Part 2 Sofware

23 Sofware What are the differences with hardware? 1. Software is intangible It's hard to understand its development effort 2. Software is easy to reproduce Cost is only in its development In other engineering products, costs are in manufacturing 3. Software is labor-intensive Hard to automate Only prototyping and testing can be successfully automated

24 Sofware What are the differences with hardware? 4. Software requires highly specialized workforce Untrained people can hack something together Quality and performance bugs are hard to catch 5. Software is easy to modify Developers often make changes without full and detailed knowledge of the code PROs: abstraction and encapsulation (information hiding) CONs: unexpected behaviors 6. Software does not wear out It deteriorates mainly by having its design changed: erroneously in ways that were not anticipated

25 Sofware Much software has poor design and it will get worse if not rewritten from scratch Demand for software is always high and rising We are in a perpetual software crisis We still have to fully learn to engineer software We still have to fully learn how to package and distribute software Linux software packages and ROS packages are good examples, but there is margin for improvement

26 Sofware Today software has become way too complex to fit entirely in one head or to be written by one or a few people At the end, the process of engineering software is just a matter of: recycling other's work trusting libraries and frameworks you're using (optional, but highly recommended) repackage and make open-source for others to improve your work

27 Sofware Focusing on C++, there are only a few major portable SW components Standard Template Library (STL): part of the C++ ISO Standard itself, almost bugfree Boost Libraries: free peer-reviewed portable C++ libraries, very few bugs. Cyclically, Boost features are ported into STL (smart pointers, threads, lambdas, etc.) Qt: cross-platform framework (UI, networking, DB, etc.), the graphical part is in continuous development (= a little bit buggy) OpenCV: imaging and computer vision Crypto++: cryptography and security ROS: middleware framework for communication, visualization, packaging and many other tools/libraries for C++/Python development

28 Sofware For anything else not covered by these libraries, one has to: search for smaller, less actively developed/maintained libraries write the code himself

29 Sofware Enter ROS! ROS opens the way to a huge amount of very well written (and tested) software packages The shortest term advantage is on sensors GPS, camera, LIDAR, multibeam, IMU, Kinect, Leap Motion, etc. etc. etc. The long term advantage is to have a distro-like packaging and distribution system that speeds up the software production

30 Sofware Let's see a few examples: Example 1 - Mono odometer - used with an old analog camera 720x576 compared with Teledyne DVL (Doppler Velocity Log). OpenCV with SURF feature detection and extraction PROs: detection and extraction is ready to use, easily portable on GPU CONs: rotation-invariant - (need to move to ORB?)

31 Sofware Let's see a few examples: Example 2 - ROS Leap Motion driver + OGRE-based libhand ROS Leap Motion driver is ready to use Needed to repackage and made libhand compile Leap Motion data is very noisy -> kalman filter No physics of the hand or constraints on the joints