Module Details

The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title Robotics
Code CSCK505
Coordinator Dr F Grasso
Computer Science
Year CATS Level Semester CATS Value
Session 2022-23 Level 7 FHEQ Whole Session 15


1. To introduce students to the key issues surrounding the development of robots and robot control.

2. To provide students with a deep and systematic understanding of a wide range of current topics in the field of robot control.

3. To allow students to experiment with techniques central to the operation of robots using a simulated environment.

4. To enable students to implement robotic control solutions to commercial challenges.

5. To provide students with a deep understanding of the legal and ethical frameworks in which robots operate.

Learning Outcomes

(M1) A deep and systematic understanding of robot systems and their application.

(M2) A critical and comprehensive insight into a range of topics central to the field of robotics.

(M3) An ability to implement robot solutions using a range of tools and techniques.

(M4) A comprehensive awareness of the legal and ethical setting in which robotic systems operate.

(S1) Communication skills in electronic as well as written form.

(S2) Self-direction and originality in tackling and solving problems.

(S3) An ability to act autonomously and professionally when planning and implementing solutions to computer science problems.

(S4) Experience of working in development teams, respecting others, co-operating, negotiating/persuading, awareness of interdependence with others.



Week 1: Introduction to robotics.
Application of robotics; soft robotics, robot types (mobile robots, humanoid robots, drones, robot arms, etc.); Legal, Social, Ethical and Professional Issue (LSEPI); safety considerations; social and economic impact; off-line programming tools.

Week 2: Behaviour based robots.
Machine intelligence, machine learning, behaviours and actions, generating behaviours, link to multi agent systems.

Week 3: Belief systems and Bayesian filters
Belief representations, real world approximations, dealing with uncertainty, state estimation, recursive Basyes filters, prediction and correction.

Week 4: Sensors
The sonar and colour sensor models, sensing and estimation, control, machine vision

Week 5: Mobile robots
Locomotion, kinematics, path planning, navigation, obstacle avoidance.

Week 6: Maps and mapping
Mobile robots, robot motion models, map representation, map learning, simultaneous localization and mappi ng,

Week 7: Localisation
Noise and aliasing, probabilistic Map-Based Localisation. Kalman Filter Localisation

Week 8: Robot Arms
Axis transformations, forward and inverse kinematics, SCARA robots.

Teaching and Learning Strategies

The mode of delivery is by online learning, facilitated by a Virtual Learning Environment (VLE). This mode of study enables students to pursue modules via home study while continuing in employment. Module delivery involves the establishment of a virtual classroom in which a relatively small group of students (usually 10-25) work under the direction of a faculty member. Module delivery proceeds via a series of eight one-week online sessions, each of which comprises an online lecture, supported by other eLearning activities, posted electronically to a public folder in the virtual classroom. The mode of learning includes a range of required and optional eLearning activities, including but not limited to: lecture casts, live seminars, self-assessment opportunities, and required and suggested further reading and try-for-yourself activities. Communication within the virtual classroom is asynchronous, preserving the requirement that students are able to pursue the module in their own time, within the weekly time-frame of each online session. An important element of the module provision is active learning through collaborative, cohort-based, learning using discussion fora where the students engage in assessed discussions facilitated by the faculty member responsible for the module. This in turn encourages both confidence and global citizenship (given the international nature of the online student body).

Teaching Schedule

  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24


Timetable (if known)              
Private Study 86


EXAM Duration Timing
% of
Penalty for late
CONTINUOUS Duration Timing
% of
Penalty for late
Group Presentation: Robotics group exercise resulting in a demonstrable system and group video report (10 minutes) describing and analysing the approach taken and the system developed.  12    30       
Programming: Individual software solution to a robotics' challenge resulting in a demonstrable system and supporting analysis in the form of a brief report (500 words).  12    30       
Discussion question 2: Actively participate in online discussion on a specific topic related to robotics, demonstrating an understanding of the key issues and showing original thought.    20       
Discussion Question 1: Participate actively in an online discussion to critically discuss the global robotics "landscape".    20       

Recommended Texts

Reading lists are managed at Click here to access the reading lists for this module.