118 citations · 258 across the 20 of their papers we have counts for
20 papers
The race to robustness: exploiting fragile models for urban camouflage and the imperative for machine learning security
Harriet Farlow, Matthew Garratt, Gavin Mount +1
Adversarial Machine Learning (AML) represents the ability to disrupt Machine Learning (ML) algorithms through a range of methods that broadly exploit the architecture of deep learn…
Coverage Path Planning with Budget Constraints for Multiple Unmanned Ground Vehicles
Vu Phi Tran, Asanka Perera, Matthew A. Garratt +2
This paper proposes a state-machine model for a multi-modal, multi-robot environmental sensing algorithm. This multi-modal algorithm integrates two different exploration algorithms…
Lightweight Monocular Depth Estimation with an Edge Guided Network
Xingshuai Dong, Matthew A. Garratt, Sreenatha G. Anavatti +2
Monocular depth estimation is an important task that can be applied to many robotic applications. Existing methods focus on improving depth estimation accuracy via training increas…
Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data
Nathan K. Long, Daniel Sgarioto, Matthew Garratt +1
The use of the `ship as a wave buoy analogy' (SAWB) provides a novel means to estimate sea states, where relationships are established between causal wave properties and vessel mot…
Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems
Vu Phi Tran, M. A Mabrok, Sreenatha G. Anavatti +2
Quadrotors are one of the popular unmanned aerial vehicles (UAVs) due to their versatility and simple design. However, the tuning of gains for quadrotor flight controllers can be l…
MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation
Xingshuai Dong, Matthew A. Garratt, Sreenatha G. Anavatti +1
Depth is a vital piece of information for autonomous vehicles to perceive obstacles. Due to the relatively low price and small size of monocular cameras, depth estimation from a si…
Towards Real-Time Monocular Depth Estimation for Robotics: A Survey
Xingshuai Dong, Matthew A. Garratt, Sreenatha G. Anavatti +1
As an essential component for many autonomous driving and robotic activities such as ego-motion estimation, obstacle avoidance and scene understanding, monocular depth estimation (…
Frontier-led Swarming: Robust Multi-Robot Coverage of Unknown Environments
Vu Phi Tran, Matthew A. Garratt, Kathryn Kasmarik +1
This paper proposes a novel swarm-based control algorithm for exploration and coverage of unknown environments, while maintaining a formation that permits short-range communication…
Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding
Hung The Nguyen, Tung Duy Nguyen, Vu Phi Tran +5
The control and guidance of multi-robots (swarm) is a non-trivial problem due to the complexity inherent in the coupled interaction among the group. Whether the swarm is cooperativ…
A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach
Nathan K Long, Karl Sammut, Daniel Sgarioto +2
The simultaneous control of multiple coordinated robotic agents represents an elaborate problem. If solved, however, the interaction between the agents can lead to solutions to sop…
Evolving Spiking Neural Networks for Nonlinear Control Problems
Huanneng Qiu, Matthew Garratt, David Howard +1
Spiking Neural Networks are powerful computational modelling tools that have attracted much interest because of the bioinspired modelling of synaptic interactions between neurons.…
Time-Varying Formation Control of a Collaborative Multi-Agent System Using Negative-Imaginary Systems Theory
Vu Phi Tran, Matthew Garratt, Ian R. Petersen
The movement of cooperative robots in a densely cluttered environment may not be possible if the formation type is invariant. Hence, we investigate a new method for time-varying fo…
Distributed Obstacle and Multi-Robot Collision Avoidance in Uncertain Environments
Vu Phi Tran, Matthew Garratt, Ian R. Petersen
This paper tackles the distributed leader-follower (L-F) control problem for heterogeneous mobile robots in unknown environments requiring obstacle avoidance, inter-robot collision…
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti +2
There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In t…
Apprenticeship Bootstrapping Via Deep Learning with a Safety Net for UAV-UGV Interaction
Hung Nguyen, Vu Tran, Tung Nguyen +5
In apprenticeship learning (AL), agents learn by watching or acquiring human demonstrations on some tasks of interest. However, the lack of human demonstrations in novel tasks wher…
Development of a Sliding Mode Control Based Adaptive Fuzzy Controller for a Flapping Flight
Md Meftahul Ferdaus, Sreenatha G. Anavatti, Matthew A. Garratt +1
Controlling of a flapping flight is one of the recent research topics related to the field of Flapping Wing Micro Air Vehicle (FW MAV). In this work, an adaptive control system for…
Review of Applications of Generalized Regression Neural Networks in Identification and Control of Dynamic Systems
Ahmad Jobran Al-Mahasneh, Sreenatha G. Anavatti, Matthew A. Garratt
This paper depicts a brief revision of Generalized Regression Neural Networks (GRNN) applications in system identification and control of dynamic systems. In addition, a comparison…
A Generic Self-Evolving Neuro-Fuzzy Controller based High-performance Hexacopter Altitude Control System
Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti +1
Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs) is increasing sharply. Due to the complex nonlinear dynamics, a huge research int…
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti +1
Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative lea…
Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle
Md Meftahul Ferdaus, Sreenatha G. Anavatti, Matthew A. Garratt +1
Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous Unmanned Aeria…