| 03-IBAP-MRCA | Modern Robot Control Architectures (in englischer Sprache)
Vorlesung
ECTS: 6
Termine:
wöchentlich Mo 10:00 - 12:00 DFKI RH1 B0.10 Vorlesung
wöchentlich Do 14:00 - 16:00 DFKI RH1 B0.10 Übung
https://lvb.informatik.uni-bremen.de/ibap/03-ibap-mrca.pdfRobotics is a complex field that emerged at the intersection of multiple disciplines such as physics, mathematics and computer science. New advances in hardware and software design and progress in artificial intelligence enable robotics research to pursue higher goals and achieve increased autonomy in various environments. For instance, robots can operate in disaster zones for search and rescue operations, can be employed in rehabilitation and healthcare, space and underwater exploration, etc. Given the complexity of such scenarios, it is essential to develop robust robotic systems with a high degree of autonomy, able to assist humans in difficult and tedious tasks. This course aims to provide the fundamentals of modern robot control approaches that enable robotic agents to operate in the environment autonomously. The course introduces a basic understanding of autonomous robots, along with tools and methods to control various types of mobile robotic platforms and manipulators. Firstly, the course presents the types of sensors and actuators employed in autonomous robotic platforms. Secondly, it offers a formal understanding of the robot geometry, its kinematic and dynamic models. Finally, the course provides methods and approaches to control the robotic system from a deliberative and reactive point of view. Students will put this knowledge into practice during tutorials and exercise sheets using Python implementation and robot simulations. Contents - Introduction to Robotics and AI: long term robot autonomy, artificial intelligence, deliberative vs. reactive control, robotic applications.
- Sensing and Actuation Modalities: types of sensors and actuators, sensor fusion, actuator control.
- Robot Geometry and Transformations: robot transformations in the 3D space, exponential and logarithmic maps, forward and inverse geometric models.
- Kinematics: definition of twists and wrenches for rigid bodies, geometric Jacobian formulation, forward and inverse kinematics.
- Dynamics: an introduction to Lagrangian and Newtonian mechanics, robot dynamics formulation, recursive Newton-Euler algorithm.
- Localization: direct and probabilistic methods for robot localization, odometry, global localization, particle filter.
- Path Planning: path vs. trajectory generation, graph-based methods for path planning (e.g. Djikstra, A\*).
- Kinodynamic Planning: transcribing a dynamic planning problem into trajectory optimization, direct and indirect methods, costs and constraints.
- Reinforcement Learning-based Control: mathematical foundations, discrete vs continuous methods, reinforcement learning for closed-loop robot control.
- Dynamic Control: PD gravity compensation control, computed torque control, admittance vs impedance control.
- Optimal Control: energy-shaping control, LQR and time-varying LQR control.
Learning Outcomes At the end of the course, the student is expected to be able to: - Define robot autonomy and list its key aspects.
- Describe the sensor and actuator modalities used in robotics, and explain their relevance for robot control.
- Implement and understand the low-level actuator control methods.
- Compute the 3D world coordinate transformations for rigid bodies.
- Apply the robot forward and inverse geometric model.
- Describe a robotic system based on its kinematic and dynamic properties.
- Use probabilistic methods for robot localization.
- Generate an optimal path for a mobile robot or manipulator using graph search methods.
- Plan a path taking into account the robot kinodynamic properties.
- Use reinforcement learning methods to control simple robotic systems.
- Apply dynamical and optimal control methods on robotic systems such that they are robust against disturbances.
- Assess the strengths and limitations of different control methods presented in the course.
- Identify open challenges in robotics research and current trends in state-of-the-art.
- Communicate confidently using the terminology in the field of robotics.
- Cooperate and work in teams in order to solve tasks.
Examination a) Submission of 6 worksheets in groups of 4 students and group interview for final grade (Übungsaufgaben und Fachgespräch). b) Individual oral exam without worksheet submission (mündliche Prüfung). References - Mechanics of Robotic Manipulation, Mathew T. Masen, MIT press, 2001.
- Algebra and Geometry, Alan F. Beardon, Cambridge University Press, 2005.
- Modelling and Control of Robot Manipulators, Lorenzo Sciavicco, Bruno Siciliano, Springer, 2000.
- Probabilistic Robotics (Intelligent Robotics and Autonomous Agents), Sebastian Thrun, Wolfram Burgard, and Dieter Fox, MIT Press, 2005.
- Introduction to Autonomous Mobile Robots, Siegwart R., Nourbakhsh I., Scaramuzza D., MIT press, 2011.
- Automated Planning: Theory and Practice, Malik Ghallab, Dana Nau, Paolo Traverso, Elsevier, 2004.
- Behaviour-based robotics, R. C. Arkin, MIT press, 1998.
- Modern Robotics: Mechanics, Planning, and Control, Kevin M. Lynch and Frank C. Park, Cambridge University Press, 2017.
| Frank Kirchner
M. Sc. Mihaela Popescu (Organizer)
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