Module Specification
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.
1. Module Title Autonomous Mobile Robotics
2. Module Code COMP329
3. Year Session 2023-24
4. Originating Department Computer Science
5. Faculty Fac of Science & Engineering
6. Semester First Semester
7. CATS Level Level 6 FHEQ
8. CATS Value 15
9. Member of staff with responsibility for the module
Dr TR Payne Computer Science T.R.Payne@liverpool.ac.uk
10. Module Moderator
11. Other Contributing Departments  
12. Other Staff Teaching on this Module
Mrs J Birtall School of Electrical Engineering, Electronics and Computer Science Judith.Birtall@liverpool.ac.uk
13. Board of Studies
14. Mode of Delivery
15. Location Main Liverpool City Campus
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
16. Study Hours 30

     10

     40
17.

Private Study

 110
18.

TOTAL HOURS

 150
 
    Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other
19. Timetable (if known)            
 
20. Pre-requisites before taking this module (other modules and/or general educational/academic requirements):

COMP111 Introduction to Artificial Intelligence; COMP116 Analytic Techniques for Computer Science
21. Modules for which this module is a pre-requisite:

 
22. Co-requisite modules:

 
23. Linked Modules:

 
24. Programme(s) (including Year of Study) to which this module is available on a mandatory basis:
25. Programme(s) (including Year of Study) to which this module is available on a required basis:
26. Programme(s) (including Year of Study) to which this module is available on an optional basis:
27. Aims
 

To introduce the student to the concept of an autonomous agent.

To introduce the key approaches developed for decision-making in autonomous systems.

To introduce the key issues with uncertainty of sensors and actuators/motors on modern robot platforms.

To introduce the key issues surrounding the development of autonomous robots.

To introduce a contemporary platform for experimental robotics.
 
28. Learning Outcomes
 

(LO1) Ability to explain the notion of an agent, how agents are distinct from other software paradigms (e.g., objects), and judge the characteristics of applications that lend themselves to an agent-oriented solution.
 

(LO2) Identify the key issues associated with constructing agents capable of intelligent autonomous action.
 

(LO3) Describe the main approaches taken to developing such agents.
 

(LO4) Describe how Bayesian belief revision can overcome the uncertainty that is inherent with sensors and actuators, due to real-world non-determinism.
 

(LO5) Identify key issues involved in building agents that must sense and act within the physical world.
 

(LO6) Program and deploy autonomous robots for specific tasks.
 

(S1) Problem Solving - Numeracy and computational skills
 

(S2) Problem solving – Analysing facts and situations and applying creative thinking to develop appropriate solutions.
 
29. Teaching and Learning Strategies
 

Teaching Method 1 - Lecture
Description:
Attendance Recorded: Not yet decided

Teaching Method 2 - Laboratory Work
Description:
Attendance Recorded: Not yet decided

Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Laboratory Work
Description: On-campus synchronous sessions
 
30. Syllabus
   

What is an agent: agents and objects; autonomous decision making; typical application areas for agent systems. Abstract architectures for agents; tasks for agents; the design of intelligent agents - reasoning agents, agents as reactive systems (e.g, subsumption architecture); hybrid agents (e.g, PRS); layered agents (e.g, Interrap)

The sense - decide - act loop. Sensors: passive versus active sensors; light sensors; infra-red sensors; ultrasound sensors. Actuators: motors & servo motors; kinematics; manipulators. Movement: path planning; localisation; Principles of SLAM (Simultaneous Localisation and Mapping), including Bayesian Beliefs, Kalman Filters, Probablistic Sensor Models and Probablistic Motion Models. A contemporary experimental robotics platform.

Guest lectures covering contemporary topics in Robotics will also be delivered.

The schedule of topics is as follows:
- Introduction to Robotics, and the Development API
- Wheeled based Kinem atics, Locomotion & Odometry
- Beliefs and Bayesian Filters
- Agents and Behaviour Based Robots
- Probabilistic Motion Model
- Advanced Perception and Probabilistic Sensor Model
- Markov Localisation and Particle Filters
- Maps, Landmarks and Mapping with Known Poses
- Kalman Filters, Simultaneous Localisation and Mapping (SLAM)
- Exploration, Navigation and Obstacle Avoidance
 
31. Recommended Texts
  Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.
 

Assessment
32. EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
   
33. CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
  (329) Group Programming Assignment 1 Standard UoL penalty applies for late submission. This is not an anonymous assessment. Assessment Schedule (When) :Semester 1 30 50
  (329.1) Class Test 1 60 20
  (329.2) Class Test 2 0 20
  (329.3) Engagement Tasks 0 10