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WB2433-03: Humanoid Robots
Responsible Instructor: M. Wisse
Instructor: P.P. Jonker
Contact Hours / Week x/x/x/x: 4/0/0/0
Education Period: 1
Start Education: 1
Exam Period: 1
Course Language: English
Expected prior knowledge: BSc. requirements
Course Contents: Humanoid robots are the research topic of the future, and partially already today. This course is organized around the central problem in humanoid robot design; they must operate fully autonomously. This results in design constraints such as energy efficiency and autonomous control. The course will treat the following topics:
Legged locomotion
collaborating robots (i.e. robot soccer)
Study Goals: The student is able to provide an overview of the technical disciplines that are involved in research and development of robotic systems. For each of the disciplines, the student is able to describe the main techniques and approaches, and to apply these on sample problems.
More specifically, the student must be able:
1. System software and hardware architecture, the student is able to:
design a modular system architecture for autonomous robots. For each of the software or hardware modules, the student can describe (1) the function of the module, (2) the services that the module provides to higher-ranking modules, (3) the services that the module requires from lower-ranking modules, (4) the type(s) of interface(s) that the module requires
2. Multibody dynamics, the student is able to:
describe which functions a (any) multibody dynamics simulation package fullfills, which types of algorithms are used in the package, and which typical problems can arise (accuracy, instability) and where these problems originate. Also, the student can describe the similarities between PD controllers and mechanical spring-damper systems
3. Robot walking, the student is able to:
describe the various existing methods to control two-legged walking robots. The student knows and is able to calculate the two most common performance criteria, namely stability (plus robustness) and efficiency. The student can describe by which means the robustness can be increased
4. Reinforcement learning, the student is able to:
explain the principle of reinforcement learning and the special case of Q-learning. The student is able to set up a learning controller (i.e. defining the length and conditions of learning trials, the inputs and outputs, and the reward structure). The student can describe the effects of various reward settings and explore rates, and name potential pittfalls and advantages
5. Actuator and sensor choice, the student is able to:
select electric DC motors and gear boxes for a given required torque-velocity pattern, and accounting for motor inertia effects and gear energy losses. The student can list the type of sensors required to measure the full state of a robot system. The student can explain why it is difficult to measure the absolute orientation of the system and provide a solution. The student can also explain how one can create a “series-elastic actuation” system
6. Vision, the student is able to:
apply an image processing library to perform low-level image processing algorithms and higher-level feature detection, enabling the automated detection of for example the location and size of an orange ball in an image. The student can explain why a color space other than RGB is used, and how the feature data can be used to obtain 3D information about the object of interest
7. Man-machine interaction, the student is able to:
describe how images of faces can be processed in order to obtain information about the face expression
Education Method: Lectures (2 hours per week)
PC practical (4 hours per week)
Literature and Study Materials: Readers, papers (will be provided through blackboard)
Assessment: Excercises
Remarks: The students are required to have a personal interest and motivation for robotics.
Percentage of Design: 25%
Last modified: 6 November 2013, 15:25 UTC
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