ÐÂÏã¸ÛÁùºÏ²Ê¿ª½±½á¹û

Building real robotic systems is expensive and time consuming. In cases where the systems are new, this investment may not ultimately pay off. This module explicitly addresses how one creates mathematical and computational models of real and realistic robotic systems, how one analyses the performance of those models in simulation, and how the results of that analysis should be interpreted. This will be a practical module based on underpinning engineering principles.

Aims:

The aims of this module are to:

• Provide students with enabling knowledge to employ practical techniques for designing, building, and assessing and optimising the performance of real-world robotic and AI systems.
• Support students in problem solving and creating practical solutions in robotics an AI against functional and non-functional user requirements, testing and assessing those in simulated and real-world environments and articulating the limitations of those assessments.
• Provide students with the tools for the critical analysis including reasoning about the appropriateness and quality of practical solutions produced in the context of the problems defined.

Intended learning outcomes:

On successful completion of this module, a student will be able to:

1. Design (and justify the design of) a simulation of a cyber-physical system based on a mixture of traces, mathematical modelling, and random components.
2. Implement the simulation and then run it to obtain results.
3. Justify a process for verifying and validating the simulation, given the results.
4. Assess the sensitivity of the simulation to small changes in key parameters.
5. Report on the results obtained, reflecting on the strength of the conclusions that can be drawn and/or on the requirements for further data gathering to improve confidence.

Indicative content:

The following are indicative of the topics the module will typically cover:

• The role of modelling and simulation: advantages and disadvantages.
• Type of simulation: discrete event, Monte Carlo, agent-based, systems dynamics.
• Available simulation software.
• Basics: random number generation, seeding random number generators.
• Basics: Experimental design.
• Introduction to case study – a ‘digital twin’ for a CPS.
• The simulation process: Problem definition – define the problem and the performance metrics, System definition – define appropriate levels of abstraction to address the problem.
• Model building and formulation.
• Input modelling: collecting data and fitting to theoretical distributions.
• Model testing, from components to integrated model.
• Model coding.
• Simulation verification and validation.
• Experimentation.
• (Care in) Interpreting and reporting results.
• Hybrid (hardware in the loop) simulation.
• Examples of the simulation of human behaviour.

This module will also provide students with an initial understanding of experimental research design to be further developed in group projects in Year 3 and Individual projects in Year 4.

Requisite conditions:

To be eligible to select this module as optional or elective, a student must be registered on a programme and year of study for which it is formally available.