ACS6121 Mobile Robotics & Autonomous Systems

Module Description (subject to change)

Recent research is finally leading to real advances in applications of mobile robotics and autonomous vehicles. The DARPA Grand Challenges have demonstrated the potential for fully autonomous road vehicles leading to major car companies undertaking trials of driverless cars. Unmanned aerial vehicles are routinely being deployed for a variety of applications including natural disaster search and rescue. The Curiosity rover is exploring the surface of Mars and unmanned underwater vehicles can navigate whole oceans. The technological challenges of developing such autonomous systems are varied and complex. This module introduces the concepts of autonomous vehicles and mobile robotics focussing on the control and systems engineering issues related to autonomous operation.

The module covers basic concepts in robot architectures, kinematic and dynamic modelling, control and decision making. Topics such as path planning, navigation, obstacle avoidance, simultaneous localisation and mapping (SLAM), sensors, state estimation and agent methods will be covered. Applications and techniques associated with single, co‐operative and swarming robotics will be covered with case studies demonstrating real world applications of the techniques and algorithms.

Credits: 15 (Spring semester)

Module Leader

Dr Roderich Gross

Dr Roderich Gross

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.

Learning Outcomes

Learning Outcomes

By the end of the module students will have:

  1. Classify robots by their autonomy levels and explain the technical issues that define the autonomy level. Design control architectures, reflecting on high level versus low level control, and explain these in the context of real world applications. [EA2p]

  2. Apply and analyse fundamental methods in mobile robotics, such as planning and obstacle avoidance. [EA3m]

  3. Explain the features of different sensors commonly used in autonomous vehicles and mobile robotics. Describe methods related to sensing, estimation, localization and mapping. [EA3m]

  4. Compare different software architectures and appraise different locomotion methods in mobile robotics applications. Create and evaluate controller solutions applying these in real-world scenarios. [EA1p,EA4p]

  5. Critically appraise research advances in autonomous and distributed robotics, including decentralised control, reconfigurability and learning. [EA3m]

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 and autonomous systems; brief history; why robots are important; levels of autonomy; social and ethical issues
  • Swarm robotics; networked systems
  • Planning algorithms
  • Advanced concepts of probabilistic robotics and simultaneous localisation and mapping (SLAM)
  • Bio-logically inspired robots; modular reconfigurable robots

Teaching Methods

Learning and Teaching Methods

NOTE: This summary of teaching methods is representative of a normal Semester. Owing to the ongoing disruption from Covid-19, the exact method of delivery will be different in 2020/21.

Lectures: 20 hours
Tutorials: 2 hours
Labs (including associated assessment): 12 hours
Independent Study: 114 hours

Teaching Materials

Learning and Teaching Materials

All teaching materials will be available via Blackboard (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.

You can view the latest Department response to the survey feedback here.

Recommended Reading

Recommended Reading

  • LaValle, SM. Planning Algorithms, Cambridge University Press, 2006 [freely available online]
  • Bullo, F. Lectures on Networked Systems, CreateSpace, 2018 [freely available online]
  • Thrun, S, Burgard, W, Fox D. Probabilistic Robotics, The MIT Press, 2005 [available in Information Commons, 629.892 (T)]
  • Floreano, D and Mattiussi, C. Bio-inspired artificial intelligence, The MIT Press, 2008 [available in Information Commons, 006.3 (F)]
  • Stoy, K, Brandt, D, and Christensen, DJ. Self-reconfigurable robots: an introduction, The MIT Press, 2010 [available in Information Commons, 629.892 (S)]
  • Nolfi, S and Floreano, D. Evolutionary Robotics, The MIT Press, 2004 [available in Information Commons, 629.892 (N)]
  • Siciliano, B and Khatib, O (Editors). Springer Handbook of Robotics, 2nd edition, 2016 [available in Information Commons, 629.892 (S)]