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)
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, behavior, 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 analyzing 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:
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.
- BioRobotics design project
- Control of Robotic Systems
- Robot Kinematics
- Biomedical Signal Analysis
- Programming of Embedded Systems
Because this module is a minor for engineering bachelor programs other than B-BMT, we provide two variants of the following courses: Biomedical Signal Analysis and Control of Robotic Systems. These variants are intended for all B-ME, all B-EE and (some of the) B-AT students only (see details below). In agreement with the B-AT, B-EE and B-ME educational programs the students are requested to follow these variants, because otherwise the student’s own program will not recognize the ECs of the regular course.
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 (3 EC)
The students will learn how to make a dynamical model of the robot and analyze its behavior in the time-domain and frequency domain. The students learn how to translate mechatronics system requirements to PID feedback controller design to control the dynamic behavior of the robot. The course will deal with methods to determine stability in both the continuous-time and discrete-time domain. This course will be assessed with a single MC exam.
After following Control of Robotic Systems, the student will be able to:
- Analyze the kinematics and the mechanical consequences of various types of robots.
- Create (electro)mechanical dynamic models in various forms (differential equations, transfer functions, block diagrams)
- Analyze the dynamic behavior of a mechanical system in time and frequency domain (step- and impulse response, Bode plot)
- Translate requirements of a mechatronic system (settling time, stroke, disturbance) into a motion control design. (closed loop design, position and velocity feedback, Bode plots, P(I)D-controllers, Nyquist diagrams, stability)
Control of Robotic Systems for ME/ATM6c/EE (3 EC)
This variant of Control of Robotic Systems is only for all B-ME students, all B-EE 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 B-ME students, B-EE students and those B-AT students that completed B-AT Module 6c (Systems and Control) 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 following Control of Robotic Systems for ME/ATM6c/EE, the student will be able to:
- Analyse the mechanical consequences of various robots types and drive trains and design a robot architecture based on this.
- Perform a frequency domain controller design procedure on a 2-DOF system, proof its stability and demonstrate its performance.
- Model non-linear effects in robotic drive train and compensate for this using feedforward and feedback linearization.
- 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.
- Determine typical disturbances in biomedical signals and design optimal filters based on biomedical signal characteristics.
Biomedical Signal Analysis for AT/EE (3 EC)
This variant of Biomedical Signal Analysis is only for B-AT and B-EE students to provide course content that is not already covered in the standard curriculum of AT and EE. The B-AT and B-EE students follow only this variant of the course and not the regular variant. This course will be assessed with one MC exam (60%) and two assignments (20% each).
After completing Biomedical Signal Analysis for AT/EE, the student will be able to:
- Analyze joint probability and cross-spectral features of two biomedical signals.
- Distinguish typical disturbances in biomedical signals and design optimal filters based on biomedical signal characteristics.
- Improve amplitude estimation, onset activation detection and feature classification of biomedical signals (i.e. EMG) using signal whitening transformations.
Programming of Embedded Systems (1.5 EC)
The students learn how to program real-time software on an embedded platform in the Python programming language. Furthermore, useful skills such as thinking about a program flow and the process of debugging will be taught to the students. This course will be assessed by a single programming exam.
After completing Programming of Embedded Systems, the student will be able to:
- characterize fundamental concepts (e.g. functions, data types, etc.) and methods (e.g. Python programming language, etc.) used in software engineering for mechatronic systems.
- program a controller for a robotic system that a real-time performance guarantee.
- program the measurement of signals from sensors and act accordingly.
- 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.
Assumed previous knowledge
|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 materials|
|Lecture notes and online readers will be made available for free later.|
|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|
|Self study without assistance|
|BioRobotics Design Project|
|Control of Robotic Systems|
|Control of Robotic Systems for ME/EE/AT MOD06C|
|Biomedical Signal Analysis|
|Biomedical Signal Analysis for AT/EE|
|Programming of Embedded Systems|