Creation and validation of a simulation environment for machine learning with drones

Simulation environments are useful for a wide range of applications and their functionalities continue to improve every year. The aim of this thesis project was to create a simulation environment with high levels of realism and assess its capabilities through the use case of generating distributed drone traffic rules.<br/><br/>This thesis...

Spiking Neural Networks Based Data Driven Control: An Illustration Using Cart-Pole Balancing Example

Machine learning can be effectively applied in control loops to robustly make optimal control decisions. There is increasing interest in using spiking neural networks (SNNs) as the apparatus for machine learning in control engineering, because SNNs can potentially offer high energy efficiency and new SNN-enabling neuromorphic hardwares are being...

Heuristics-based causal discovery: Discovering causal relations through heuristics-based action planning and dynamical search space adjustment

To operate in open world environments a symbolic Artificial Intelligence (AI) to be able to adapt and incorporate new objects and relations in its Knowledge Base (KB). Symbolic AI use the objects and relations in their KB to navigate the world and create plans. These KB are filled with knowledge in advance, so they can not add new knowledge when...

The Effects of Entropy Regularization and Lyapunov Stability Constraint on Multi-Agent Reinforcement Learning for Autonomous Driving

High level decision making in Autonomous Driving (AD) is a challenging task due to the presence of multiple actors and complex driving interactions. Multi-Agent Reinforcement Learning (MARL) has been proposed to learn multiple driving policies concurrently to solve AD tasks. In the literature, multi-agent algorithms have been shown to outperform...

Grasp-RCNN for a two-fingered pinch-gripper: A multiple RCNN approach

<b>Introduction </b>- Grasping unknown objects is an important ability for robots in logistic environments. While humans have an excellent understanding of how to grasp objects because of their visual perception and understanding of the 3D world, robotic grasping is still a challenge. Due to the fast-growing development of deep learning methods,...

Automated Machine Learning in Medical Image Segmentation

Semantic Segmentation of medical images are used to improve diagnosis and treatment. In recent years, the application of machine learning methods are increasingly used. However, the design of these models is difficult and time-consuming. In this thesis, we investigated the automation of this process using an Automated Machine Learning (AutoML)...

Bayesian Deep Learning for Distilling Physical Laws from Videos

An end-to-end framework is developed to discover physical laws directly from videos, which can help facilitate the study on robust prediction, system stability analysis and gain the physical insight of a dynamic process. In this work, a video information extraction module is proposed to detect and collect the pixel position of moving objects,...

Robust map building for robot navigation in dynamic environments

Simultaneous Localisation and Mapping (SLAM) provide a novel solution for the robots to localise and navigate an unknown environment. Initial SLAM research focused mainly on the indoor environment, assuming the background to be primarily static. In contrast, the real world has dynamic interactions that restrict the implementation of SLAM to...

Reinforcement Learning Compensated Filter for Position and Orientation Estimation

Pose estimation provides accurate position and orientation information of the intelligent agents in real time. The accuracy of the estimation directly affects the performance of sequential tasks such as mapping, motion planning, and control. EKF (Extended Kalman Filter) is a standard theory for nonlinear pose estimation by modeling state...

Bayesian Deep Learning for Dynamic System Identification

System identification is a mature field in physical sciences and an emerging field in social sciences, with a vast range of applications. Nevertheless, it remains of great focus in academia. The main challenge is the efficient use of data to generate good model fits. System identification involves multi-disciplinary techniques from statistical,...

Towards Real-Time Human Pose Estimation For Mobile Device

Human pose estimation, a challenging computer vision task of estimating various human body joints' locations, has a wide range of applications such as pedestrian tracking for autonomous cars, baby monitoring, video surveillance, human action recognition, virtual reality, gaming, gait analysis, etc. A majority of the research on the development...

Deep Learning for 4D Longitudinal Segmentation of MRI Brain Tissues and Glioma

Glioma is a kind of slow-growing brain tumor which may result in severe seizures. Currently a major tool used to detect and diagnose the glioma is MRI scan. To better analyze the medical image, segmentation is usually conducted as a basic step for further processing, which partitions an integrate image into multiple physically meaningful regions...

Safe Reinforcement Learning for Automated Vehicles

Fully automated vehicles have the potential to increase road safety and improve traffic flow by taking the human element out of the driving loop. They can also provide mobility to people who are unable to operate a conventional vehicle. Safe automated vehicles must be able to respond in emergency situations or drive on slippery roads in bad...

Prediction of Fuel Consumption of Long Haul Heavy Duty Vehicles using Machine Learning and Comparison of the Performance of Various Learning Techniques

This study aims at a possible solution to predict the fuel consumption of heavy duty diesel trucks, particularly, the tractor semitrailer for their long haul operations using various machine learning techniques. It intends to provide a possible alternative to simulation or physics based models, which often are very complicated. The stringent...

Model Free Reinforcement Learning with Stability Guarantee

Model-free reinforcement learning has proved to be successful in many tasks such as robotic manipulator, video games, and even stock trading. However, as the dynamics of the environment is unmodelled, it is fundamentally difficult to ensure the learned policy to be absolutely reliable and its performance is guaranteed. In this thesis, we borrow...