ACS6121 Robotics and Autonomous Systems

Module Description

Robotics and autonomous systems are having an increasing impact on society and the way we live. From advanced manufacturing and surgical robots to unmanned aerial systems and driverless cars this exciting area is presenting increasing technological challenges. This module provides students with the advanced knowledge and understanding to apply control and systems engineering concepts to the closely related disciplines of robotics and autonomous systems.

The module covers theoretical and technical analysis and design aspects of mobile and manipulator robots with reference to their applications. The module further covers advanced techniques in autonomous decision making for robots and autonomous vehicles.

Credits: 15 (Spring semester)

Module Leader

Dr Roderich GrossDr Roderich Gross
Room F09, Pam Liversidge Building

If you have any questions about the module please talk to us during the lectures or the labs in the first instance. It is likely that other students will learn from any questions you ask as well, so don’t be afraid to ask questions.

Outside of lectures please contact one of us via email, or drop in to see one of us.

Other teaching staff

Dr Jonathan AitkenDr Jonathan Aitken
Room B05, Pam Liversidge Building

Professor Sandor VeresProfessor Sandor Veres
Room B19, Amy Johnson Building

Learning Outcomes

Learning Outcomes

By the end of the module students will have:

  1. Explain concepts of model error, plant uncertainty, sensitivity and robustness, and frequency-domain multivariable plant analysis. [SM1fl, EA1fl]

  2. Evaluate and critically appraise, real world scenarios where robotics and autonomous systems might be applied, make informed choices about the relative merits of their use, and explain associated social and ethical issues. [SM3fl, D1fl, ET1fl, ET2fl, ET5fl, ET6fl]

  3. Compare different robotic configurations and systems, critically evaluate their advantages and disadvantages in open ended real world problems, derive and apply kinematic and dynamic models of robots. [SM2fl, EA1fl, ET4fl, ET6fl]

  4. Explain sensing and actuation systems applied to robotic systems, and the importance of using multiple sensors and of data fusion in robotic and autonomous systems. [SM1fl, EP2fl]

  5. Apply a high level of control to different robotic systems, critically compare different control architectures, explain the importance of high level versus low level control, and describe different software and hardware architectures used in mobile robotics. [EA2fl, D2fl, ET6fl]

  6. Explain and apply algorithmic approaches for path planning, navigation and obstacle avoidance, and of the advanced concepts of probabilistic robotics and simultaneous localisation and mapping (SLAM) and the practical implications of their application. [SM2fl, SM3fl, EA3fl]

This module satisfies the AHEP3 (Accreditation of Higher Education Programmes, Third Edition) Learning Outcomes that are listed in brackets after each learning outcome above. For further details on AHEP3 Learning Outcomes, see the downloads section of our accreditation webpage.



  • Introduction to robotics; brief history; types and applications of robotics; why robots are important; social and ethical issues.

  • Kinematics and dynamics modelling; trajectory planning; sensing and actuation systems; control of flexible manipulators; healthcare and medical robotics; assistive and rehabilitation robotics.

  • Autonomous systems: Autonomy and its classification; high-level and low-level control architectures; software and hardware architectures used in mobile robotics; algorithmic approaches to path planning, navigation and obstacle avoidance; multi-sensor data fusion; advanced concepts of probabilistic robotics and simultaneous localisation and mapping (SLAM); co‐operative and swarm robotics; decentralised control and agent methods.

Teaching Methods

Learning and Teaching Methods

Lectures: 24 hours
Tutorials: 6 hours
Labs (including associated assessment): 6 hours
Independent Study: 114 hours

Teaching Materials

Learning and Teaching Materials

All teaching materials will be available via MOLE.



  • 2 hour formal exam 60%

  • Lab work and associated assignments 40%

The resit for this module is usually by examination only.



  • You will be provided feedback on your performance in the laboratory/assignment element of the module within two weeks of completion of the work.

  • You will have an opportunity to view marked Exam Scripts once Exam results have been confirmed by the Faculty and released to students. The date of this review session will be announced by the Departmental Office.

  • The exam paper and solutions will be available after the exam period.

Student Evaluation

Student Evaluation

Students are encouraged to provide feedback during the module direct to the lecturer. Students will also have the opportunity to provide formal feedback via the Faculty of Engineering Student Evaluation Survey at the end of the module.

Recommended Reading

Recommended Reading

  • Siciliano, B and Khatib, O (Editors), 2008, The Springer Handbook of Robotics [available in Information Commons, 629.892 (S)]

  • Floreano, D and Mattiussi,C, 2008. Bio-inspired artificial intelligence, The MIT Press, 2008 [available in Information Commons, 006.3 (F)]

  • Stoy, K; Brandt, D and Christensen, DJ, 2010, Self-reconfigurable robots: an introduction, The MIT Press [available in Information Commons 629.892 (S)]

  • Nolfi, S and Floreano, D, 2004, Evolutionary Robotics, The MIT Press [available in Information Commons, 629.892 (N)]

  • Samad, T & Weyrauch, J (Eds), 2000, Automation, Control and Complexity –an integrated approach, John Wiley & Sons, ISNB 0-471-81654-X [available in Information Commons, 629.8 (A)]

  • Meystel, A M and Albus, J S, Intelligent Systems: architecture, design and control, Wiley-Interscience ISBN 0-471-19374-7