PDF versionLeader, Lecturer
Lecturer
Lecturer
Lecturer
Timing and Structure
Michaelmas term, 100% coursework
Prerequisites
3C5 useful; 3C8 useful; 3F2 useful; 3F3 useful
Aims
The aims of the course are to:
- Introduce fundamentals of robotics
- Case studies of practical applications such as robotic manipulation, locomotion and navigation
- Hands-on exercises on robot development through projects
- Presentation of research and development
Objectives
As specific objectives, by the end of the course students should be able to:
- understand different design strategies and architectures of intelligent and adaptive machines.
- design and integrate systems with basic components (actuators, sensors, controllers, and simulators).
- model and analyze kinematics and dynamics of robot systems.
Content
Introduction 2 Lectures (F. Iida)
- Landscape of robotics: Theories, technologies, applications and research areas
- Fundamentals of intelligent autonomous robots; Robotics and AI (Intelligence as search algorithms, Frame problem, Frame-of-reference problem, Grounding problem, Embodiment, DoF problem); Robotics and biology (Similarities and differences, Biological inspirations, Modeling of animals and machines, Case studies)
- The spectrum of robot architecture (Sense-Think-Act paradigm, Reflex based architecture, Behavior-based architecture, Passivity-based architecture)
- Introduction of research tools and areas (mainly for coursework)
Robot motion control 4 Lectures (F. Iida)
- Kinematic and dynamic control of robot motions (robotic arms, hands, wheels, legs)
- Underactuated robotics, passivity-based robot control, impedance control
- Simulation and analysis of robot motion and stability
Robot planning and navigation 2 Lectures (F. Iida)
- Theories and methods for planning of complex robot motions
- Theories and methods for robot navigation
Robot sensing and perception 2 Lectures (P. Maiolino)
- Robot sensors, and sensing technologies
- State-estimation, recognition, and categorization
Robot learning and autonomy 2 Lectures (F. Iida)
- Theories and methods of robot learning
- Case studies of robot learning and autonomy
Advanced topics and case studies 2 lectures (R. Cipolla, J. Hughes)
- Discussion of a few case studies of advanced robotics with the latest technologies of computer vision, machine learning, navigation, and manipulation.
Project presentation and competition 2 lectures (F. Iida)
- Students should present the simulation models of their robots and discuss outcome of the investigations
Coursework
Each student will be assessed by the following three components of coursework:
30%: Individual report to a problem set (submission deadline in the 5th week). The problem set consists of theoretical questions about robot control as well as some hands-on exercise on robot simulation. Details will be instructed in the first lecture.
20%: Group presentation and robot competition (in the 8th week). Students will work in a team of 2-4 people to develop and investigate their own manipulation/locomotion robots based on the kits provided. In the last week of the term, each team should give a 10-minute presentation and demonstrate the performance for competition. Details will be instructed in the first and second lectures.
50%: Individual dossier about the development and investigation of the projects (submission deadline in the 11th week). Each student should write a report about the project, and demonstrate how the theories and methods introduced in the lectures are used.
Booklists
Please see the Booklist for Group M Courses for references for this module.
Examination Guidelines
Please refer to Form & conduct of the examinations.
Last modified: 20/09/2018 17:38