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Robotics and Vision Design

General objectives 

Today robots are being applied in many fields, from industrial automation and defense to agriculture, health care and assistance of handicapped persons. 

By following the minor Robotics and Vision Design, you will learn the state-of-the-art of robotics and vision techniques and you will learn to apply this knowledge to design and realize an intelligent robot prototype using commercial-off-the-shelf (COTS) equipment. 
More specifically, you will learn how to: 

  1. model the kinematics and simulate (arm-type and mobile) robotic systems;
  2. design a robot controller and implement it on a platform such as ROS, the Robot Operating System;
  3. translate control tasks into optimization problems and how to solve these with a computer program;
  4. design a vision system (optics and image capturing) for robotic systems;
  5. apply various image processing techniques to extract relevant features;
  6. design and evaluate learning algorithms to learn complex behavior using data from different types of sensors;
  7. analyze design problems of an external stakeholder in which intelligent robots will be used;
  8. investigate and evaluate results from (scientific) literature and exploit these for the purpose of the project; 
  9. design, implement, test and integrate robotic and vision subsystems to realize an intelligent robot product for an external stakeholder;
  10. guarantee the quality of the design and the realized product by performing a rigorous requirements analysis and verification. 

 

Summary of contents 

The minor consists of 6 courses and a project with a company as one of the stakeholders. The theory and application of various robotics and vision techniques are dealt with in the courses. The project focuses on the integration of the techniques. 

Courses and project with learning goals: 

  1. Robot modeling, learning goal 1;
  2. Robot control, learning goals 2 and 3;
  3. Intelligent methods, learning goal 3;
  4. Image capturing and processing, learning goals 4 and 5;
  5. Pattern recognition, learning goal 5 and 6;
  6. Machine learning, learning goal 6;
  7. Project, learning goals 7-10.

The courses are taught in classes and practical sessions. In the project you work in a team with a lecturer as a project coach. As support for the projects, you also get a number of tutorials to learn working with the Robot Operating System (ROS). 

Leerdoelen

Competency levels 

The focus of this minor is on learning existing technology in robotics and vision and to develop creativity in applying these techniques to design new or improve existing tools or products. 

After successful completion of this minor, you can demonstrate that you are able to: 

  • model and simulate robotic systems; 
  • explain and apply techniques for modeling, simulation, optimization, learning and control of robotics and vision systems and their integration; 
  • solve optimization problems with numerical methods; 
  • design a vision system (optics and image capturing) for robotic systems; 
  • design intelligent robotic solutions, showing creativity and the effective integration of multiple subsystems; 
  • document the design and its argumentation in a well structured manner; 
  • realize a successful intelligent robotic system and document the realization process. 

In addition you can demonstrate in the scope of the project one of the following competences: 

  • investigate and evaluate scientific literature in robotics and vision technology to further clarify and find solutions for specific (sub)problems; 
  • manage a design project towards the presentation of a successful result within possibilities and constraints on time, budget and people; 
  • manage the technical aspects of the project towards the realization and documentation of a successful system by keeping a helicopter view on the whole system and keeping track of critical technical issues; 
  • give well motivated advise, clarify stakeholders needs and keep good relation with internal and external stakeholders.

Aanvullende informatie

Indication of target group 

Third and final year Bachelor of Engineering (BEng/BE) students with a background in mechatronics, motion technology, physics, mechanical engineering, electrical engineering, mathematics or computer science. 

 

Teaching methods + studyload 

Courses and educational organization: 

Part 1: 

  • Robot modelling (study load corresponds to 3ECTS): 
  • each week one lecture of 90 minutes  
  • each week one practical of 90 minutes 
  • Intelligent methods (study load corresponds to 3ECTS): 
  • each week one lecture of 90 minutes 
  • each week one practical of 90 minutes 
  • Image capturing and processing (study load corresponds to 3ECTS): 
  • each week one lecture of 90 minutes  
  • each week one practical of 90 minutes 

 

Part 2: 

  • Robot control (study load corresponds to 3ECTS): 
  • each week one lecture of 90 minutes  
  • each week one practical of 90 minutes 
  • Machine learning (study load corresponds to 3ECTS): 
  • each week one lecture of 90 minutes  
  • each week one practical of 90 minutes 
  • Pattern recognition (study load corresponds to 3ECTS): 
  • each week one lecture of 90 minutes  
  • each week one practical of 90 minutes 

 

Project organization (study load corresponds to 2 x 6ECTS): 

  • weekly meetings with project coach 
  • several tutorials on learning Linux and ROS 
  • guest lectures

 

Minimum- and maximum participation 

minimum of 10 students 

maximum of 40 students 

 

Contact hours 

The minimal number of contact hours per week is: 10.5 hours (these are clock hours).  

 

Partners 

Festo, TNO, Berg Hortimotive, Lely, HIT, Alten, PRIVA, Robot Care Systems, TU Delft, and various other companies  

 

Miscellaneous 

The courses, project, exams and all documents and reports will be in the English language. The members of the project groups will have different nationalities, which will prepare students to work in an international environment. 

Minorkrant De Haagse Hogeschool

Bekijk onze minorkrant om alle HHs-minoren op een rijtje te zien.

Ingangseisen

Entry requirements 

Prerequisites for this minor are mastering the following subjects: 

Matrix calculus: matrix vector multiplication, solving set of linear equations;  

  • Dynamics: speed, acceleration, free body diagrams and equation of motion; 
  • Basics of control engineering: transfer functions, block schemes, system responses;  
  • Introduction in programming: some experience with writing of programs in a compiler or interpreter language, such as C, C++, Python or Matlab;  
  • Experience with design projects: knowledge of the V-model, functional decomposition, experience with working in project groups, writing a plan of approach, parallel planning, goal oriented working.  

In addition, you should have obtained at least 60 ECTS of the main phase (hoofdfase) of your study. The minor is meant for engineering students such as mechatronics, eletrical engineering, mechanical engineering, applied physics and computer science. Unless necessary competences can be shown explicitly, the minor cannot be done by students from industrial product design, architecture, (chemical) process technology, communication and multimedia design and technical business administration.      

To subscribe for this minor you have to send a motivation letter to the minor coordinator (see above). This letter has a maximum of 250 words and contains: 

  • your motivation to do this minor; 
  • explanation why you satisfy the prerequisites. 

 

If you have a deficiency in one of above mentioned subjects, you are still encouraged to apply for this minor, but you may need to follow an additional course or have to do some self study to prepare for an entrance test. If you have any questions regarding the entry requirements, feel free to ask one of the contact persons. 

Toetsing

Description of tests and tminimum pass rate 

The minor consists of two parts: 

 

The assessment of part 1 consists of: 

course assessments (week 8, resit week 10): 

  • Robot modeling (study load corresponds to 3ECTS) consists of theoretical exam (mark) and practical assessment (pass/fail) 
  • Intelligent methods (study load corresponds to 3ECTS) consists of  theoretical exam (mark) and practical assessment (pass/fail) 
  • Image capturing and processing (study load corresponds to 3ECTS) consists of theoretical exam (mark) and practical assessment (pass/fail) 
  • project assessment part 1 (in week 9, resit in week 10; study load corresponds to 6ECTS) consists of: 
  • Systems design (structure, motivation behind and description) 
  • Proof of principle 
  • Documentation of project work 
  • Demonstration (critical) subsystem 
  • Presentation (content: problem, global design and questions adequately answered, form: structure, quality of presentation material, self-confidence) 

 

The assessment of part 2 consists of: 

course assessments (week 8, resit week 10): 

  • Robot control (study load corresponds to 3ECTS) consists of theoretical exam (mark) and practical assessment (pass/fail) 
  • Machine learning (study load corresponds to 3ECTS) consists of theoretical exam (mark) and practical assessment (pass/fail) 
  • Pattern recognition (study load corresponds to 3ECTS) consists of theoretical exam (mark) and practical assessment (pass/fail) 
  • project assessment part 2 (in week 9, resit in week 10; study load corresponds to 6ECTS) consisting of: 
  • Report consisting of: 
  • detailed design (structure, motivation behind and description) 
  • realized product (agreement with design, realization process, quality and successfulness in terms of agreement with stakeholders requirements) 
  • Presentation (content: problem, design and technical realization well-presented and questions adequately answered, form: structure, quality of presentation material, self-confidence) 
  • Demonstration of realized product 

 
The final mark is given only when the individual assessments are all at least 5.5 and is calculated as a weighted average of the assessments where the weights corresponds to the study load. 

Literatuur

Study aids 

The teaching material will be announced before the summer holidays. Examples of textbooks being used are: 

  • M. Spong et al., Robot Modeling and Control, Wiley, 2005. 
  • W. Burger and M. J. Burger, Principles of Digital Image Processing, Springer, Fundamental Techniques and Core Algorithms  
  • C. Woodford and C. Philips, Numerical methods with worked examples: Matlab edition, Springer, 2012. 
  • S. Marsland, Machine learning, an algorithmic perspective, 2nd edition, CRC Press, 2015. 

Rooster

Information about scheduling will be sent to you 10 days before the start of the minor.