ACS6121 Robotics & Autonomous Systems

Module Lead: Roderich Gross | Profile | Email:

QAA Framework Credit Level: 7

Administered by: Department of Automatic Control & Systems Engineering

Credits: 15

Semester: Spring

Pre-Requisites: None

Co-Requisites: None

Restrictions: None

Module Overview

Module Description

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‚Äźoperative and swarming robotics will be covered with case studies demonstrating real world applications of the techniques and algorithms.

Module Syllabus

  • 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 and Assessment

Teaching Methods



Surgery Sessions


Independent Study

Assessment Methods

  • Examination (2 hours): 60%
  • Lab work and associated assignments: 40%

Teaching Materials

  • MOLE

Resit Assessment

  • Resits are not permitted.

Reading Materials

  • 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)]


  • 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 exam paper and solutions will be available after the exam
Learning Outcomes

Learning Outcomes

The following learning outcomes include the knowledge, skills, capabilities or aptitudes which you can expect to learn on this module.

These module learning outcomes have been assigned codes which correspond to the AHEP-3 learning outcomes as defined by the Engineering Council. For a full explanation of these codes, refer to the AHEP-3 Learning Outcomes.

Module Learning Outcomes

AHEP-3 Learning Outcomes


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


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


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


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


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