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Robotprogrammatie: enkele
lessen uit de praktijk, trends
en uitdagingen
Eric Demeester
Faculteit Industriële Ingenieurswet...
Robotprogrammatie: enkele lessen uit de praktijk
Outline
2
• ACRO activities: robots with vision and a plan
o Mobile, assi...
Robotprogrammatie: enkele lessen uit de praktijk
Overview ACRO activities
3
• Profibus & profinet, PLC (UCLL)
o Training f...
Robotprogrammatie: enkele lessen uit de praktijk
Outline
4
• ACRO activities: robots with vision and a plan
o Mobile, assi...
Robotprogrammatie: enkele lessen uit de praktijk
Introduction
5
• Semi-autonomous robotic wheelchairs
o Motivation:
• Many...
Robotprogrammatie: enkele lessen uit de praktijk
Research questions
6
• Semi-autonomous robots should be able to answer
th...
Robotprogrammatie: enkele lessen uit de praktijk
Research questions
7
2. Where am I going?
o Key assumption: user should n...
Robotprogrammatie: enkele lessen uit de praktijk
Research questions
8
3. How should I get there?
o Even if it is known whe...
Robotprogrammatie: enkele lessen uit de praktijk
Research questions
9
o Results with different interfaces: standard joysti...
Robotprogrammatie: enkele lessen uit de praktijk
Outline
10
• ACRO activities: robots with vision and a plan
o Mobile, ass...
Robotprogrammatie: enkele lessen uit de praktijk
Autonomous apple picking
11
• Introduction:
o It is hard to find personne...
Robotprogrammatie: enkele lessen uit de praktijk
Autonomous fruit picking
12
• Current state:
This image cannot currently ...
Robotprogrammatie: enkele lessen uit de praktijk
Outline
13
• ACRO activities: robots with vision and a plan
o Mobile, ass...
Robotprogrammatie: enkele lessen uit de praktijk
Random bin picking (IWT Tetra RaPiDo)
14
• Introduction:
• Nowadays, obje...
Robotprogrammatie: enkele lessen uit de praktijk
Random bin picking (IWT Tetra RaPiDo)
15
• Current state:
o Bin picking s...
Robotprogrammatie: enkele lessen uit de praktijk
Flexible robot welding (IWT VIS SmartFactory)
16
• Introduction:
o Curren...
Robotprogrammatie: enkele lessen uit de praktijk
Flexible robot welding (IWT VIS SmartFactory)
17
• Current state:
Robotprogrammatie: enkele lessen uit de praktijk
Vision-based wood waste sorting
18
• Introduction:
o SME innovation proje...
Robotprogrammatie: enkele lessen uit de praktijk
Vision-based wood waste sorting
19
• Solution:
o Determine several featur...
Robotprogrammatie: enkele lessen uit de praktijk
Outline
20
• ACRO activities: robots with vision and a plan
o Mobile, ass...
Robotprogrammatie: enkele lessen uit de praktijk
The RADHAR project as an example
21
Vision: Robotic ADaptation to Humans ...
Robotprogrammatie: enkele lessen uit de praktijk
The RADHAR project as an example
22
Consortium
2 user
groups
NMSC, Nation...
Robotprogrammatie: enkele lessen uit de praktijk
The RADHAR project as an example
23
General overview of the framework
Robotprogrammatie: enkele lessen uit de praktijk
The RADHAR project as an example
24
Developed hardware:
Robotprogrammatie: enkele lessen uit de praktijk
Outline
25
• ACRO activities: robots with vision and a plan
o Mobile, ass...
Robotprogrammatie: enkele lessen uit de praktijk
Requirements/challenges
26
o Hardware level:
• Be able to deal with diffe...
Robotprogrammatie: enkele lessen uit de praktijk
Requirements/challenges
27
o System level: large-scale collaborative rese...
Robotprogrammatie: enkele lessen uit de praktijk
Outline
28
• ACRO activities: robots with vision and a plan
o Mobile, ass...
Robotprogrammatie: enkele lessen uit de praktijk
Solutions (lessons learnt)
29
1. Hardware abstraction and algorithmic lev...
Robotprogrammatie: enkele lessen uit de praktijk
Solutions (lessons learnt)
30
• Use of design patterns,
• e.g. to abstrac...
Robotprogrammatie: enkele lessen uit de praktijk
Solutions (lessons learnt)
31
• Component-based software engineering:
• C...
Robotprogrammatie: enkele lessen uit de praktijk
Solutions (lessons learnt)
32
• Evolution towards a more complex but very...
Robotprogrammatie: enkele lessen uit de praktijk
Solutions (lessons learnt)
33
2. Robotics related level:
• Probabilistic ...
Robotprogrammatie: enkele lessen uit de praktijk
Plan recognition
34
• How to represent intentions?
1. We assume: users wi...
Robotprogrammatie: enkele lessen uit de praktijk
Plan recognition
35
2. Furthermore, we assume that users have a certain m...
Robotprogrammatie: enkele lessen uit de praktijk
Plan recognition
36
• Intention estimation scheme:
1. Generate plan hypot...
Robotprogrammatie: enkele lessen uit de praktijk
Plan recognition
37
2. A probability distribution is maintained over the ...
Robotprogrammatie: enkele lessen uit de praktijk
Plan recognition
38
• Example of plan recognition performance:
Robotprogrammatie: enkele lessen uit de praktijk
Shared control
39
• The probability distribution over user plans may be
m...
Robotprogrammatie: enkele lessen uit de praktijk
Shared control
40
• Maximum likelihood versus Maximum a posteriori
versus...
Robotprogrammatie: enkele lessen uit de praktijk
Shared control
41
• Example of shared control performance:
o Benchmark te...
Robotprogrammatie: enkele lessen uit de praktijk
Shared control
42
• Example of shared control performance:
Robotprogrammatie: enkele lessen uit de praktijk
Solutions (lessons learnt)
43
3. System level:
• we adopted ROS (Robot Op...
Robotprogrammatie: enkele lessen uit de praktijk
Solutions (lessons learnt)
44
• We developed a module base class, built o...
Robotprogrammatie: enkele lessen uit de praktijk
Outline
45
• ACRO activities: robots with vision and a plan
o Mobile, ass...
Robotprogrammatie: enkele lessen uit de praktijk
Trends and challenges for the future
46
• Probabilistic programming
o Tre...
Robotprogrammatie: enkele lessen uit de praktijk
Trends and challenges for the future
47
• Insurance and liability
o Robot...
Robotprogrammatie: enkele lessen uit de praktijk
Trends and challenges for the future
48
• Plug-and-play
o there are no st...
Robotprogrammatie: enkele lessen uit de praktijk 49
Dank voor uw aandacht!
Eric Demeester
eric.demeester _at_ kuleuven.be
...
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IT is happening now 2015: Robotprogrammatie (Eric Demeester)

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Robotprogrammatie: enkele lessen uit de praktijk, trends en uitdagingen

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IT is happening now 2015: Robotprogrammatie (Eric Demeester)

  1. 1. Robotprogrammatie: enkele lessen uit de praktijk, trends en uitdagingen Eric Demeester Faculteit Industriële Ingenieurswetenschappen Technologiecampus Diepenbeek Onderzoeksgroep ACRO CEVORA IT event, 25 oktober 2015
  2. 2. Robotprogrammatie: enkele lessen uit de praktijk Outline 2 • ACRO activities: robots with vision and a plan o Mobile, assistive robots with vision and a plan o Agricultural robots with vision and a plan o Industrial robots with vision and a plan • Programming robots: learnt lessons o The RADHAR project as an example o Requirements o Solutions • Trends and challenges for the future
  3. 3. Robotprogrammatie: enkele lessen uit de praktijk Overview ACRO activities 3 • Profibus & profinet, PLC (UCLL) o Training for operators in industry o Service and consultancy regarding industrial networks • Vision and robotics (KU Leuven) o Vision: choice of cameras, optics, lighting, image processing o State estimation and machine learning, e.g.: • Object recognition/classification and 6D pose estimation • Mobile robot localisation and mapping • Estimation of human intentions by robots o Sensor-based decision making and planning, e.g.: • Collision-free trajectory planning for mobile robots and industrial manipulators • Shared human-machine control Remark: ACRO is part of Faculty of Engineering Technology => research closer to industry (higher TRL levels)
  4. 4. Robotprogrammatie: enkele lessen uit de praktijk Outline 4 • ACRO activities: robots with vision and a plan o Mobile, assistive robots with vision and a plan o Agricultural robots with vision and a plan o Industrial robots with vision and a plan • Programming robots: learnt lessons o The RADHAR project as an example o Requirements o Solutions • Trends and challenges for the future
  5. 5. Robotprogrammatie: enkele lessen uit de praktijk Introduction 5 • Semi-autonomous robotic wheelchairs o Motivation: • Many elderly and disabled people suffer from a reduced mobility • Electric wheelchairs enhance this mobility, but manoeuvring an electric wheelchair is often difficult and time-consuming • Consequences: accidents, frustration, dependence on others, reduced social contact, reduced self-esteem, lower quality of life o Solution? Equip electric wheelchairs with sensors and computing power => Combine the strengths of both human (global planning) and computer (fine motion control) Collaborations/cases with: Permobil, Invacare, HMCI, Ottobock, BlueBotics, National MS Centre Belgium, Windekind + various research institutes
  6. 6. Robotprogrammatie: enkele lessen uit de praktijk Research questions 6 • Semi-autonomous robots should be able to answer three questions: 1. Where am I?
  7. 7. Robotprogrammatie: enkele lessen uit de praktijk Research questions 7 2. Where am I going? o Key assumption: user should not be required to communicate explicitly the desired navigation assistance o Consequence: user (navigation) plans or intentions are hidden and should be estimated from uncertain user signals and sensor signals o This is the problem of plan (intention) recognition: Plan recognition is the problem of inferring the goal of an actor and his plan to achieve this goal, based on a sequence of actions performed by the actor.
  8. 8. Robotprogrammatie: enkele lessen uit de praktijk Research questions 8 3. How should I get there? o Even if it is known where the user would like to drive to, it remains unclear how the manoeuvre should be executed jointly by human and robot o This is the problem of shared control: The situation in which the control of a device is shared between one or more users and one or more robotic controllers.
  9. 9. Robotprogrammatie: enkele lessen uit de praktijk Research questions 9 o Results with different interfaces: standard joystick, switch interface, brain-computer interface, haptick joystick
  10. 10. Robotprogrammatie: enkele lessen uit de praktijk Outline 10 • ACRO activities: robots with vision and a plan o Mobile, assistive robots with vision and a plan o Agricultural robots with vision and a plan o Industrial robots with vision and a plan • Programming robots: learnt lessons o The RADHAR project as an example o Requirements o Solutions • Trends and challenges for the future
  11. 11. Robotprogrammatie: enkele lessen uit de praktijk Autonomous apple picking 11 • Introduction: o It is hard to find personnel for fruit picking o High wages, low prices for fruit on the global market o Orchard management: selective spraying/fertilization • Research questions: o Autonomously determine the fruit’s position o Autonomously pick the fruit without damaging the fruit nor the tree, keeping the stem on the apple o Feasibility study: is it possible to perform this operation with vision and a robot arm? o Can it be performed fast enough? o And many other ... (logistics, wheather conditions, fruit selection on the spot, etc.)
  12. 12. Robotprogrammatie: enkele lessen uit de praktijk Autonomous fruit picking 12 • Current state: This image cannot currently be displayed.
  13. 13. Robotprogrammatie: enkele lessen uit de praktijk Outline 13 • ACRO activities: robots with vision and a plan o Mobile, assistive robots with vision and a plan o Agricultural robots with vision and a plan o Industrial robots with vision and a plan • Programming robots: learnt lessons o The RADHAR project as an example o Requirements o Solutions • Trends and challenges for the future
  14. 14. Robotprogrammatie: enkele lessen uit de praktijk Random bin picking (IWT Tetra RaPiDo) 14 • Introduction: • Nowadays, objects are typically fed to robots in a well-defined, mechanised manner, which takes time and money to setup • Alternative: random bin picking: • Make a 3D scan of a bin with randomly positioned objects • Recognise and estimate pose of objects • Compute a collision-free trajectory taking size of robot, gripper, objects, environment into account • Research goals: o Build an open demonstrator o Evaluate existing open source code o Focus on integration of vision and robotics o Speed up of object detection and path planning o Evaluate potential of novel sensors and algorithms
  15. 15. Robotprogrammatie: enkele lessen uit de praktijk Random bin picking (IWT Tetra RaPiDo) 15 • Current state: o Bin picking setup with sheet-of-light working o Analysis of randomised path generation techniques Collaborations/cases with: ABB, KUKA, Sick, Materialise, Ceratec, Egemin, cards PLM solutions, Clock-O-Matic, Dewilde Engineering, Vision++, Exmore Benelux, Flanders Food, Intrion, Optidrive, PEC, Intermodalics, Rabbit, Robberechts, Robosoft, Robomotive, Sedac Meral, SoftKinetic, Beltech, Borit, Meditech, Sirris, Phaer,Gibas, Octinion, Dana, Van Hool
  16. 16. Robotprogrammatie: enkele lessen uit de praktijk Flexible robot welding (IWT VIS SmartFactory) 16 • Introduction: o Current trends: • globalisation, individualised products, small lot sizes => production industry should become highly flexible • High wages, lack of skilled labour, global competion => production industry should become highly automated • Factory of the future: combine flexibility and automation by using novel technologies: machine vision, path planning software, force control, intelligent transport • Research goals: zero ramp-up & auto-programming • Recognise and determine the pose of objects to be welded using 2D or 3D vision, • perform quality checks before and after welding • Computation of optimal (time, energy) trajectories for robots taking into account: robot geometry, kinematics and dynamics, singularities, components mounted on the robot, environment
  17. 17. Robotprogrammatie: enkele lessen uit de praktijk Flexible robot welding (IWT VIS SmartFactory) 17 • Current state:
  18. 18. Robotprogrammatie: enkele lessen uit de praktijk Vision-based wood waste sorting 18 • Introduction: o SME innovation project for NV Gielen o Business model: grind wood waste to wood chips and sell o Problem: percentage MDF: 15% to 50% => price of wood chips very low o Solution: separate MDF from wood • Research questions: o How to separate MDF from wood? • Not possible using only infrared reflection, density, colour • => classify using vision? o How to classify it accurately enough? o How to separate it fast enough?
  19. 19. Robotprogrammatie: enkele lessen uit de praktijk Vision-based wood waste sorting 19 • Solution: o Determine several features based on colour, texture, shape o Train multilayer perceptrions to classify wood and MDF o Onsorted input (45-55% MDF) to sorted output with 5% MDF in wood output and 2% wood in MDF output o Capacity: 7.5 tons/hour
  20. 20. Robotprogrammatie: enkele lessen uit de praktijk Outline 20 • ACRO activities: robots with vision and a plan o Mobile, assistive robots with vision and a plan o Agricultural robots with vision and a plan o Industrial robots with vision and a plan • Programming robots: learnt lessons o The RADHAR project as an example o Requirements o Solutions • Trends and challenges for the future
  21. 21. Robotprogrammatie: enkele lessen uit de praktijk The RADHAR project as an example 21 Vision: Robotic ADaptation to Humans Adapting to Robots heterogeneous user groups with (time)varying skills dynamic, 3D environments robots adapting to humans’ signals and responses Requires life-long adaptation between two interacting learning systems (human & machine)
  22. 22. Robotprogrammatie: enkele lessen uit de praktijk The RADHAR project as an example 22 Consortium 2 user groups NMSC, Nationaal Multiple Sclerosis Centrum V.Z.W. Windekind, school for children with disability 3 companies 3 universities 1 research institute
  23. 23. Robotprogrammatie: enkele lessen uit de praktijk The RADHAR project as an example 23 General overview of the framework
  24. 24. Robotprogrammatie: enkele lessen uit de praktijk The RADHAR project as an example 24 Developed hardware:
  25. 25. Robotprogrammatie: enkele lessen uit de praktijk Outline 25 • ACRO activities: robots with vision and a plan o Mobile, assistive robots with vision and a plan o Agricultural robots with vision and a plan o Industrial robots with vision and a plan • Programming robots: learnt lessons o The RADHAR project as an example o Requirements o Solutions o Problems encountered • Trends and challenges for the future
  26. 26. Robotprogrammatie: enkele lessen uit de praktijk Requirements/challenges 26 o Hardware level: • Be able to deal with different sensors, wheelchairs, interfaces, people • Real-time (predictable, fast) control even if data-intensive o Algorithmic level: • Easily transfer algorithms to other platforms • Safety • Calibration • Debugging o Robotics-related challenges: • How to program the robot to perform what it should do? • Environment: uncertain/unknown, changing, 3D, soft • Robot: kinematics and dynamics, slippage, castors, size • Concurrent tasks/processes/threads
  27. 27. Robotprogrammatie: enkele lessen uit de praktijk Requirements/challenges 27 o System level: large-scale collaborative research and development • Several research groups across Europe develop software • Portability, different platforms (Windows/Linux, Java/C++) • Little time during integration weeks (tests on the hardware) • Runtime robustness: stopping/crashing a module of a running robot application should not result into an overall stopping/crashing of the robot application • Runtime flexibility: be able to change algorithms of modules at runtime, extend the robot application with new modules at run- time, probe ingoing and outgoing data of modules at runtime.
  28. 28. Robotprogrammatie: enkele lessen uit de praktijk Outline 28 • ACRO activities: robots with vision and a plan o Mobile, assistive robots with vision and a plan o Agricultural robots with vision and a plan o Industrial robots with vision and a plan • Programming robots: learnt lessons o The RADHAR project as an example o Requirements o Solutions • Trends and challenges for the future
  29. 29. Robotprogrammatie: enkele lessen uit de praktijk Solutions (lessons learnt) 29 1. Hardware abstraction and algorithmic level: • Use of object-oriented programming (C C++): • C++ has more features => more complex, but more readable and less maintenance cost • C++ might be slightly slower • Timely reaction, real-time control is very important => maintainability, reusability is sometimes ignored (nicely encapsulated software runs slower) • => lesson learnt: avoid optimisation until it is needed • use of GPU results were not that spectacular (< 10x improvement) • ... sometimes just use a faster PC
  30. 30. Robotprogrammatie: enkele lessen uit de praktijk Solutions (lessons learnt) 30 • Use of design patterns, • e.g. to abstract implementation details away from the functionality (e.g. Factory Method): => easily adopt “higher-level” code on different wheelchairs, user interfaces, sensors, ...
  31. 31. Robotprogrammatie: enkele lessen uit de praktijk Solutions (lessons learnt) 31 • Component-based software engineering: • Compose sw from off-the-shelf and custom-built components • Well-defined external interface that hides its internals, independently developed from where it is going to be used, clear specification of what it requires and provides and depends on => it can be composed • 5C’s principle of separation of concerns separating the communication, computation, coordination, configuration, and composition aspects in the overall software functionality. Design patterns exist to decouple these aspects … - Composition: group entities together, model interactions (= an art) - Computation: algorithmic part (the “useful” part of the sw) - Configuration: change settings of a system - Coordination: how do entities work together, life-cycle FSM - Communication
  32. 32. Robotprogrammatie: enkele lessen uit de praktijk Solutions (lessons learnt) 32 • Evolution towards a more complex but very structured and motivated Composition Pattern as the basic building block Vanthienen, Klotzbücher, Bruyninckx, "The 5C-based architectural Composition Pattern: lessons learned from re-developing the iTaSC framework for constraint-based robot programming", Journal of Software Engineering for Robotics, pp. 17-35, May 2014.
  33. 33. Robotprogrammatie: enkele lessen uit de praktijk Solutions (lessons learnt) 33 2. Robotics related level: • Probabilistic robotics: explicitly model uncertainty on all information sources, and use that uncertainty when taking decisions • For intention estimation (plan recognition) • For shared control
  34. 34. Robotprogrammatie: enkele lessen uit de praktijk Plan recognition 34 • How to represent intentions? 1. We assume: users wish to reach a certain end pose pe (xe, ye, θe) with an end velocity ve (ve, ωe)”, e.g.:
  35. 35. Robotprogrammatie: enkele lessen uit de praktijk Plan recognition 35 2. Furthermore, we assume that users have a certain mental trajectory in mind to arrive at an end state, e.g.: i1
  36. 36. Robotprogrammatie: enkele lessen uit de praktijk Plan recognition 36 • Intention estimation scheme: 1. Generate plan hypotheses i based on local paths or paths to learned end poses or end poses indicated on an a priori map.
  37. 37. Robotprogrammatie: enkele lessen uit de praktijk Plan recognition 37 2. A probability distribution is maintained over the set of possible user plans i. Initially, a uniform distribution is adopted; 3. This distribution is updated every time new user signals uk are obtained according to Bayes’ rule.
  38. 38. Robotprogrammatie: enkele lessen uit de praktijk Plan recognition 38 • Example of plan recognition performance:
  39. 39. Robotprogrammatie: enkele lessen uit de praktijk Shared control 39 • The probability distribution over user plans may be multi-modal • Nevertheless, decisions should be made at each time instant regarding navigation assistance • 3 approaches have been proposed to make these decisions: o Maximum Likelihood (ML) o Maximum A Posteriori (MAP) o Greedy Partially Observable Markov Decision Process (greedy POMDP)
  40. 40. Robotprogrammatie: enkele lessen uit de praktijk Shared control 40 • Maximum likelihood versus Maximum a posteriori versus POMDP
  41. 41. Robotprogrammatie: enkele lessen uit de praktijk Shared control 41 • Example of shared control performance: o Benchmark test: visit the goal locations 4 – 9 – 6 – 11 – 2 – 14 – 5 – 10 and back to position 4 o Execute this in user control mode, in ML shared control mode, in POMDP shared control mode o In shared control, the wheelchair can drive farther than in user control mode, with the danger of making wrong decisions and driving too far
  42. 42. Robotprogrammatie: enkele lessen uit de praktijk Shared control 42 • Example of shared control performance:
  43. 43. Robotprogrammatie: enkele lessen uit de praktijk Solutions (lessons learnt) 43 3. System level: • we adopted ROS (Robot Operating System), an open-source middleware initiative that allows: • Easy and +/- efficient data exchange • Tools (rosbags) for data collection and replay • Dynamic configuration of components’ (nodes) parameters • Interactive GUI for data visualization • ... And other (multi-language support – C++/Python), support for distributed systems
  44. 44. Robotprogrammatie: enkele lessen uit de praktijk Solutions (lessons learnt) 44 • We developed a module base class, built on top of ROS • Used to share common functionality between modules (reuse) • This implemented a finite-state-machine: stop/start a component, be robust against errors, ... • Health monitor: each component sends a heart-beat message to let a central controller know about the wellbeing of a component • The central controller sends a heart-beat to a hardware watchdog • A GUI allows to display the health state of all modules, to activate/deactivate modules • This worked for us, but: • Required lots of integration work and programming • Will probably be duplicated by others
  45. 45. Robotprogrammatie: enkele lessen uit de praktijk Outline 45 • ACRO activities: robots with vision and a plan o Mobile, assistive robots with vision and a plan o Agricultural robots with vision and a plan o Industrial robots with vision and a plan • Programming robots: learnt lessons o The RADHAR project as an example o Requirements o Solutions • Trends and challenges for the future
  46. 46. Robotprogrammatie: enkele lessen uit de praktijk Trends and challenges for the future 46 • Probabilistic programming o Trend towards probabilistic robots (due to many successes) o Can we incorporate these probabilistic and machine learning concepts into the programming language? • E.g. prob distributions as new data type, automatically tune code using built-in learning functions (program fails => indicate the correct behaviour and learn from this example) o Initial successes: much fewer lines of code; e.g. Darpa grand challenge (autonomous car): 100.000 lines of code o But: how can it be debugged (randomness plays a crucial role)
  47. 47. Robotprogrammatie: enkele lessen uit de praktijk Trends and challenges for the future 47 • Insurance and liability o Robots should be safe, e.g. implementing obstacle avoidance but how reliable are these? Bugs keep popping up, in unpredictable way (e.g. memory leaks) o E.g. by feeding random input or pseudo-random input and see if the system crashes - a sort of automated unit tests rather than manually crafted unit tests • Currently, typically only “nominal” behaviour is evaluated; for safety “worst-case” behaviour is important • Reliable communication
  48. 48. Robotprogrammatie: enkele lessen uit de praktijk Trends and challenges for the future 48 • Plug-and-play o there are no standard methods for connecting sensors, motors, actuators, cameras and other components to robots • E.g. plug a device in, detect the device, install a driver, know what can be performed with it, ... o It’s starting (Orocos, ROS, OpenCV), but this requires still much programming at lower levels • Software deployment support: o Robot “apps” should be made to work easily on different platforms, have example programs available and good documentation
  49. 49. Robotprogrammatie: enkele lessen uit de praktijk 49 Dank voor uw aandacht! Eric Demeester eric.demeester _at_ kuleuven.be tel: +32 (0)11 27 88 15

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