IN2106 Dodo Alive! - Resurrecting the Dodo with Robotics and AI: Mechanism Design & Control

Lecturer (assistant)
  • Sami Haddadin [L]
  • Riddhiman Laha
  • Dennis Ossadnik
  • Kim Peper
  • Abdalla Swikir
Number0000001898
TypePractical course
Duration6 SWS
TermWintersemester 2022/23
Language of instructionEnglish
Position within curriculaSee TUMonline

Admission information

Objectives

Upon completion of this laboratory, the students have acquired profound knowledge in planning, design and control of dynamically walking bipedal robots both methodologically and practically. In addition to their problem-solving and teamworking capabilities, the students expand their management and organization skills

Description

Imagine that you are at the natural history museum. You wander around and take a look at different parts of the museum. Suddenly, a robot in the form of a long extinct species – the Dodo – moves towards you, starts talking to you and tells you about itself. This course covers constructing an autonomous, intelligent, walking, Dodo-like robot, which requires a combination of a wide spectrum of fields, including design, control, navigation, locomotion, perception and AI. During this laboratory course, the students will develop the necessary skills to design and construct various components of “Robo-Dodo”, as part of a long-term, iterative project. This laboratory aims at the design and construction of mechanisms and the development of control strategies for bipedal robots. The basic components developed in the laboratory “Dodo Alive! HW/SW Components Development” shall now be integrated into a prototype. The course is structured as follows: After some introductory classes, a practical phase will follow in which the students work closely together in interdisciplinary groups. The students will then be divided into groups focusing on the mechanism design, which encompasses the CAD design of the leg mechanism and other parts of the robot, or the control part, which includes locomotion control, trajectory planning and perception. The long-term goal is to bring the famous Dodo back to life within a collective effort.

Prerequisites

Participation in the laboratory “Dodo Alive! HW/SW Components Development” is recommended but not necessary. We expect sound knowledge in one or more of the following fields: - Modeling and Control (Matlab/Simulink) - Mechanics, robotics and multi body systems - Mechanism design (CAD) - Electronics (PCB layout design, bus systems, microcontroller programming) - Programming (C, C++) - Perception

Teaching and learning methods

- Introductory classes - Independent student work - Team work (including supervised and unsupervised work in the laboratory)

Examination

The examination consists of a practical group project, a ten-minute presentation and a written report. Within the final project, the students show that they are able to conceptualize, plan and realize the integration of complex mechatronic systems and to develop and implement the required control methods. The work is evaluated by the functionality of the students’ approach, the development and testing process and the understanding of the underlying theoretic concepts. In addition to the tasks mentioned above, the students of the Faculty of Informatics work on a current publication in the field of legged robots. The students summarize the content in a 5-page report and present their results in a 10-minute presentation.

Recommended literature

- M. W. Spong, S. Hutchinson, M. Vidyasagar, ‘Robot Modeling and Control’, vol. 3, New York: Wiley, 2006. - R. Alexander, ‘Locomotion of Animals’, Springer, 1982. - R. M. N. Alexander, R. McNeill, G. Goldspink, ‘Mechanics and energetics of animal locomotion’, Chapman and Hall, 1977. - H. Choset, K. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. Kavraki, S. Thrun, ‘Principles of Robot Motion: Theory, Algorithms, and Implementation’, MIT Press, 2005. - B. Siciliano, O. Khatib, ‘Springer Handbook of Robotics’, Springer, 2016. - M. W. Spong, S. Hutchinson, M. Vidyasagar, ‘Robot modeling and control’, vol. 3. New York: Wiley, 2006. - C. M. Bishop, ‘Neural networks for pattern recognition’, Oxford university press, 1995.

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