Artificial Intelligence in Unmanned Autonomous Systems

课程简介

/ Indicative Syllabus Introduction to Artificial Intelligence: The topic mainly includes the basic knowledge of machine learning such as conventional classification and regression together with high-level AI, such as convolutional neural network (CNN) for image segmentation. Introduction to Unmanned Autonomous Systems: The topic mainly includes the major existing applications of unmanned autonomous systems, such as UAV and UGV. Meanwhile, the topic will include the basic knowledge of typical unmanned autonomous systems. Optimisation Algorithm to Unmanned Autonomous Systems: The topic mainly includes the optimisation algorithms such as Gauss-Newton used to solve the engineering problems related to unmanned autonomous systems. Sensors for Unmanned Autonomous Systems: The topic mainly introduces the typical sensors applicable to unmanned autonomous systems. The sensors include the light detection and ranging (LiDAR), inertial measurement unit (IMU), and camera. Basic algorithms for sensors-based positioning will be introduced. -- 1 of 3 -- Navigation for Unmanned Autonomous Systems: The topic mainly include positioning and navigation for the unmanned autonomous system using simultaneous localisation and mapping (SLAM) using LiDAR sensors together with point cloud processing, registration, AI-aided Navigation for Unmanned Autonomous Systems: The topic mainly includes the application of AI in LiDAR SLAM using object detection in unmanned autonomous systems. Case Study (mini-group projects): A design project will be carried out for students to learn the deployment of AI in unmanned autonomous systems through practice.

目标

This subject will provide students with 1. The main concepts, ideas, and techniques of advanced artificial intelligence (AI) in unmanned autonomous systems, e.g. unmanned aerial vehicles (UAV), unmanned ground vehicles (UGV); 2. The major components of typical unmanned autonomous systems fulfilling a certain function, such as environment inspection using UAVs; and 3. Expansive view into the technological trend of AI and its application in unmanned autonomous systems.

先修要求

/ Co-requisite/ Exclusion Pre-requisite: AAE3004 Dynamical Systems and Control

Teaching Pattern

Methodology Teaching is conducted through lectures and case studies (mini-group projects). Lectures are used to deliver advanced knowledge concerning various aspects of AI, data analysis, and its applications in unmanned autonomous systems The basic knowledge, research methodology, and theoretical models will be introduced. Case study will provide the understanding of how to address and formulate problems by using mathematical programming, artificial intelligence algorithms, and optimisation techniques in unmanned autonomous systems. Research methodology, data analytics skills, and algorithm design skills are taught in class as well as the related real-life scenarios using data to enhance their research abilities. Teaching/Learning Methodology Intended subject learning outcomes to be covered a b c d 1. Lecture     2. Case Study    3.