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After following this module, the student will be able to:
- Systematically approach a design project from user requirements to device evaluation (synthesis and application, analysis and evaluation);
- Design a robot for application to a biomedical problem using multidisciplinary knowledge from mechanical, electrical, control and software engineering domains (application and synthesis);
- Create a kinematic model of the robot to control the joints to perform useful movements and tasks (analysis, synthesis, application and evaluation);
- Extract biological signals from the human body that can be used to control a robot (application and synthesis);
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Individuals with movement disorders have difficulty with participating in daily life. Robots have the potential to assist them when needed, and in this module we will design and build a robot that does just that.
Robotics is the branch of technology that deals with the design, fabrication, operation, and application of robots, as well as computer systems for their control, sensory feedback, and information processing. These technologies deal with automated machines that can take the place of humans in dangerous environments or manufacturing processes, or resemble humans in appearance, behaviour, or cognition. Worldwide scientific and industrial demand for skilled engineers with advanced systems and control knowledge of robotic systems that can apply this knowledge in biomedical or general high-tech systems is strongly increasing.
The elective module BioRobotics applies high-tech systems & control knowledge of robotic design and fabrication to the biomedical interaction with the human body, and thereby combines a vast number of disciplines. During the module, a robot has to be built that interacts with the human body to improve the quality of life for the individual with a movement disorder.
In the project, students have to design and realize a robot. During this, they will learn to:
- Go through the design process systematically, by analysing impaired human function, specifying user requirements and technical requirements, generating ideas and concepts, evaluating concepts using modelling and calculation, presenting a final design, realizing the system in hard- and software, evaluating the performance with human interaction, and reporting the results verbally and in writing.
- Integrate knowledge from multiple disciplines such as biomedical, mechanical, electrical, software and control engineering.
- Make mechatronic simulation models of two-dimensional robots, by which conceptual designs can be evaluated on performance criteria such as precision, speed, stiffness, strength, play, friction, natural frequencies and crossover frequency, on the basis of which the concepts can be adjusted.
- Obtain and process biological signals (EMG) for usage in steering a robot.
The project is chosen to maximize the application of the knowledge gained in the following courses:
- BioRobotics design project
- Control of Robotic Systems
- Robot Kinematics
- Biomedical Signal Analysis
- Programming of Embedded Systems
In agreement with the TOM philosophy, the project and courses are strongly intertwined. All global learning objectives of the module are addressed through multiple educational forms, and therefore by multiple, complementary methods of assessment.
Course variants:
Because this module is a minor for engineering bachelor programs other than B-BMT, we provide two variants of the course “Control of Robotic Systems”. The variants are “A” and “B”. Please take care when registering for the minor to choose the correct course. Details on the different variants are described in the relevant section below.
Students in B-AT who have done Systems and Control in M6 should follow Variant “B”. All B-ME, B-EE, and B-AP students should also follow variant “B”. This is required for the student’s own program to recognize the ECs of the course.
All other students applicable for the minor should follow variant “A”.
Courses within the module:
BioRobotics Design Project (5 EC)
In the project, students have to analyze the needs of a hypothetical patient, design a robot, build the mechanical construction of the robot using wood laser-cut to their specifications, program the signal analysis and robot control methods in an embedded controller and analyze the performance and acceptability of the device when interacting with humans.
The project combined with the four courses leads to a very efficient and lasting knowledge transfer. After completing the project, the student will be able to:
- systematically approach a design project from user requirements to device evaluation.
- design a robot for application to a biomedical problem using multidisciplinary knowledge from mechanical, electrical, control and software engineering domains.
- create full kinematic and basic dynamic models that can be used to evaluate design and control concepts of a robot.
- measure and interpret signals from the human body that can be used to control a robot.
- develop embedded control software to control a robot by use of a programmable micro-controller.
Teachers and experts, using the following components, assess the learning objectives of the design project (both components need to have been completed with a grade of 5.5 or higher):
- Live presentation and demonstration: half of project grade (group)
- Report: half of project grade (group)
Control of Robotic Systems A (3 EC)
The students will learn how to develop a dynamical model of a robotic system and analyze its behavior in time and frequency domain. The students learn how to translate requirements of a mechatronic system (settling time, stroke, disturbance) into a motion control design (closed-loop design, position and velocity feedback, Bode diagrams, P(I)D controllers, stability). This course will be assessed with a single MC exam.
After completing Control of Robotic Systems A, the student will be able to:
- Create a linear time-invariant model of an electro-mechanical system.
- Analyze the dynamic behavior of a mechanical system in time and frequency domain.
- Design a controller to ensure stability and meet certain specifications.
- Analyze stability and performance of overall system behavior in a continuous-time setting.
- Identify the parameters of the system model via step and sinusoidal SS response tests and apply common discretization methods for digital implementation of controllers.
Control of Robotic Systems B (3 EC)
This variant of Control of Robotic Systems is only for: all B-ME students, all B-EE students, all B-AP students and for those B-AT students that completed B-AT Module 6c (Systems and Control). This is to provide course content that is not already covered in the standard curriculum of these programs. The aforementioned students follow only this variant of the course and not the regular variant. This course will be assessed with a single MC exam (50%) and a two-part assignment (50%).
After completing Control of Robotic Systems B , the student will be able to:
- Create an approximate model of a nonlinear dynamical system by applying linearization technique to design general control structures via algebraic methods.
- Decouple the dynamics of a MIMO plant statically to apply SISO control techniques.
- Analyze the stability of a dynamical system through Lyapunov Stability Theory and design a control mechanism.
- Identify the parameters of the system model via step and sinusoidal SS response tests and apply common discretization methods for digital implementation of controllers.
- Independently research and implement novel control strategies and present the findings in a concise manner.
Robot Kinematics (2.5 EC)
The students learn to apply geometrical concepts from Lie group theory to serial robotic manipulators; in this case to design and analyse planar robot kinematics. Derivation of direct forwards kinematics and forward/backward differential kinematics allow the students to implement high-level position control in their project's embedded control solution. This course will be assessed with one MC exam
After completing Robot Kinematics, the student will be able to:
- Systematically analyze the kinematics and kineostatics of 2D mechanisms using Screw Theory.
- Run simulations of forward and inverse kinematic calculation methods.
- Use these simulations to design and optimize position controllers for the 2D mechanisms.
Biomedical Signal Analysis (3 EC)
The student learns how to convert neurophysiological signals to useable control inputs for the robots. The signals are often highly non-linear and very noisy, and thus require extensive processing. Special attention is given to the time-frequency relation of signals, to be able to relate them to control theory of robotic systems. This course will be assessed with a single MC exam.
After completing Biomedical Signal Analysis, the student will be able to:
- Characterize elementary notation of signal analysis methods in time and spectral domain.
- Explain potential discrepancies in these methods due to discretization and stochasticity of biomedical signals and propose an appropriate solution.
- Distinguish random signal characteristics using probability theorem. Analyze joint probability of two biomedical signals.
- Distinguish the joint characteristics of two biomedical signals and analysis common variation of the signals in time and frequency domain.
- Determine typical disturbances in biomedical signals and design filters using conventional and model base approaches.
Programming of Embedded Systems (1.5 EC)
The students learn how to program an embedded platform in the (Micro)Python programming language. Furthermore, skills such as thinking about a program flow and the process of debugging will be taught. This course will be assessed by a single programming exam.
After completing Programming of Embedded Systems, the student will be able to:
- explain, use, and apply fundamental concepts (e.g. functions, data types, etc.) and software development tools (e.g. Python, Git, etc.) used in software engineering.
- program a microcontroller in MicroPython for sensor signal acquisition and control of a robotic system in real time.
- communicate data in real time between micro-controller and high level computer for data logging, visualization, validation and debugging.
General Notes on Assessment
- Course objectives are assessed using multiple-choice (MC) exams in week 8.
- Programming of Embedded Systems will be assessed with an individual programming assessment on the Monday in week 7.
- All tests for the courses have a re-exam opportunity in week 10 of the module. If either the project’s presentation+demonstration grade or the report grade is insufficient, students have to do a reparation for this/these in quarter 1B. Such a reparation will maximally be awarded the grade of 5.5
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| Introductory courses on statistics, dynamics and control theory. Some pre-knowledge of MATLAB is recommended, although self-study of MATLAB tutorials would be sufficient. |
| Bachelor Biomedical Engineering |
| | Required materialsHandouts| Lecture notes and online readers will be made available for free later. |
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Recommended materialsBook| Shiavi, "Introduction to Applied Statistical Signal Analysis", 3e editie, ISBN: 978-0-12-088581-7. Freely available online: http://www.sciencedirect.com/science/book/9780120885817 |
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Instructional modesDesign| Presence duty | | Yes |
| Lectorial
| Lecture| Presence duty | | Yes |
| Practical| Presence duty | | Yes |
| Presentation(s)| Presence duty | | Yes |
| Project supervised| Presence duty | | Yes |
| Project unsupervised| Presence duty | | Yes |
| Self study without assistance| Presence duty | | Yes |
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TestsBioRobotics Design Project
| Control of Robotic Systems A
| Control of Robotic Systems B
| Robot Kinematics
| Biomedical Signal Analysis
| Programming of Embedded Systems
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