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.

(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.

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

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

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

Due to Covid-19, in 2021/22, one or more of the following delivery methods will be implemented based on the current local conditions.
(a) Hybrid delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Laboratory Work
Description: Mix of on-campus/on-line synchronous/asynchronous sessions

(b) Fully online delivery and assessment
Teaching Method 1 - Lecture
Description: On-line synchronous/asynchronous lectures
Teaching Method 2 - Laboratory Work
Description: On-line synchronous/asynchronous sessions

(c) Standard on-campus delivery
Teaching Method 1 - Lecture
Description: Mix of on-campus/on-line synchronous/asynchronous sessions
Teaching Method 2 - Laboratory Wor k
Description: On-campus synchronous sessions