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Matt Garratt

Matt Garratt
UNSW Sydney | UNSW · School of Engineering and Information Technology

PhD (ANU), GradDippAppComp (CQU), BE (Sydney)

About

237
Publications
64,462
Reads
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2,972
Citations
Citations since 2016
152 Research Items
2231 Citations
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20162017201820192020202120220100200300400
20162017201820192020202120220100200300400

Publications

Publications (237)
Article
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 motion response information. This study focuses on a model-free machine learning approach to SAWB-based sea state estimation (SSE), using neural networks (NNs) to map vessel...
Article
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. The algorithm combines two elements: swarm rules for maintaining a close-knit formation and frontier search for driving exploration and coverage. Inspired by natural sy...
Article
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 laborious, and accurately stable control of trajectories can be difficult to maintain under exogenous disturbances and uncertain system parameters. This article introduces...
Article
Full-text available
While the concept of swarm intelligence was introduced in 1980s, the first swarm optimisation algorithm was introduced a decade later, in 1992. In this paper, nineteen representative original swarm optimisation algorithms are analysed to extract their common features and design a taxonomy for swarm optimisation. We use twenty-nine benchmark problem...
Preprint
Full-text available
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 motion response information. This study focuses on a model-free machine learning approach to SAWB-based sea state estimation (SSE), using neural networks (NNs) to map vessel...
Article
Collective behaviours such as swarm formation of autonomous agents offer the advantages of efficient movement, redundancy, and potential for human guidance of a single swarm organism. However, tuning the behaviour of a group of agents so that they swarm, is difficult. Behaviour-bootstrapping algorithms permit agents to self-tune behaviour adapted f...
Conference Paper
Recent work has developed value functions that can recognize emergent swarming behaviour and distinguish it from random behaviour. To date, this work has been done in point-mass swarm simulations. This paper proposes a transfer learning approach that can improve the performance of a value system for recognising swarming in simulated and real robots...
Preprint
Full-text available
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 laborious, and accurately stable control of trajectories can be difficult to maintain under exogenous disturbances and uncertain system parameters. This paper introduces a...
Article
Full-text available
Image based Localization (IbL) uses both Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) data for accurate pose estimation. However, under conditions where there is a large perspective difference between the SfM images and SLAM keyframes, the SfM-SLAM co-visibility graph becomes sparse. As a result, the scale drift can...
Article
Path planning for multiple Unmanned Ground Vehicles (UGVs) is a critical problem for UGV autonomy and is increasingly attracting attention due to its wide applications. This paper presents a continuous ant colony-based multi-UGV path planner, which consists of UGV path planning and multi-UGV coordination. A continuous Ant Colony Optimisation with a...
Chapter
In recent years, increases in industrial residue have become a significant environmental threat. These residues can cause problems for natural ecosystems and their inhabitants, including animals and humans. Environmental monitoring through sensing is one approach to predict or detect the presence of pollution from such residue. One of the emerging...
Article
As an essential component for many autonomous driving and robotic activities such as ego-motion estimation, obstacle avoidance and scene understanding, monocular depth estimation (MDE) has attracted great attention from the computer vision and robotics communities. Over the past decades, a large number of methods have been developed. To the best of...
Article
Depth estimation from a single RGB image has attracted great interest in autonomous driving and robotics. State-of-the-art methods are usually designed on top of complex and extremely deep network architectures, which require more computational resources. Moreover, the inherent characteristic of the backbone used by the existing approaches results...
Preprint
In mobile robotics, area exploration and coverage are critical capabilities. In most of the available research, a common assumption is global, long-range communication and centralised cooperation. This paper proposes a novel swarm-based coverage control algorithm that relaxes these assumptions. The algorithm combines two elements: swarm rules and f...
Preprint
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 single RGB image has attracted great interest in the research community. In recent years, the application of Deep Neural Networks (DNNs) has significantly boosted the accur...
Preprint
As an essential component for many autonomous driving and robotic activities such as ego-motion estimation, obstacle avoidance and scene understanding, monocular depth estimation (MDE) has attracted great attention from the computer vision and robotics communities. Over the past decades, a large number of methods have been developed. To the best of...
Article
A Multi-operator continuous Ant Colony Optimisation (MACOR) is proposed in this paper to solve the real-world problems. An adaptive multi-operator framework is proposed for selecting the suitable operator during different evolutionary stages by considering the historical performance of operators and the convergence status of the population. To impr...
Article
Full-text available
This paper aims to design an enhanced self-adaptive interval type-2 fuzzy control system (ESAF2C) for stabilization of a quadcopter drone under external disturbances. Due to the ability to accommodate the footprint-of-uncertainty (FoU), an interval type-2 Takagi-Sugeno fuzzy scheme is employed to directly address the uncertainties in the nonlinear...
Article
Full-text available
Several artificial intelligence-based systems have been proposed to help human operators in safety-critical domains such as air-traffic control systems, health and farming. In scenarios where multiple autonomous systems are utilised, the cognitive capacity of humans hits its limits quickly, and sometimes the success of these systems is obstructed....
Article
Leveraging the benefits of the multiobjective particle swarm optimization (PSO) technique, we introduce a new concept of adaptive strictly negative imaginary (SNI) controllers. The proposed adaptive control systems are specifically designed to minimize a certain performance index, representing the objective of our control design, which is to obtain...
Chapter
In this paper, an effective approach for real-time multi-obstacle detection and tracking in the navigation module is discussed.Francis, SobersAnavatti, Sreenatha G.Garratt, MatthewAbbass, Hussein A. To calculate a feasible path for an autonomous ground vehicle (AGV) from the start position to goal position, efficient Dstar lite global planner is ad...
Article
Quadrotor system is subject to multiple disturbances, including both internal and external effects (e.g. wind gusts, coupling effects, and unmodeled dynamics). For example, severe wind disturbances may significantly degrade trajectory tracking during the flight of autonomous aerial vehicles, or even cause loss of control or failure of a tracking mi...
Article
This paper presents a compelling combination of the two negative-imaginary (NI) control systems: a consensus-based formation control framework for a hybrid multi-vehicle system and a new dynamic obstacle detection and avoidance algorithm. The incorporation of the two techniques permits robots to self-detect collisions and then self-create a safe pa...
Chapter
Uninhabited aerial vehicles (UAVs) are widely used in many areas for completing complex missions such as tracking targets, search and rescue, farming (shepherding in the traditional sense) and mapping. Mission planning for shepherding a UAV swarm is advantageous for Human-Swarm Teaming. While most research on shepherding see the shepherd as a simpl...
Article
Full-text available
A novel bidirectional fuzzy brain emotional learning (BFBEL) controller is proposed to control a class of uncertain nonlinear systems such as the quadcopter unmanned aerial vehicle (QUAV). The proposed BFBEL controller is non-model based and has a simplified fuzzy neural network structure and a novel bidirectional brain emotional learning (BBEL) al...
Chapter
This chapter presents the applications of an interval Type-2 (IT2) Takagi-Sugeno (T-S) fuzzy system for modeling and control the dynamics of a quad-copter unmanned aerial vehicle (UAV). In addition to being complex and non-linear, the dynamics of a quadcopter are under-actuated and uncertain, making the modeling and control tasks across its full fl...
Chapter
Robotic aircraft are often required to operate in harsh environments (e.g., underground mining, cluttered environments, and battlefields). In this chapter, we discuss an adaptive (evolving) fuzzy system that has the ability to learn and to configure itself based on the human way of learning, which is also somewhat akin to the principles of natural...
Chapter
Full-text available
This chapter presents an effective approach to path planning combined with task assignments for a group of unmanned aerial vehicles (UAVs). Path planning for a UAV is a challenging task. Handling multiple UAVs in dynamic environments makes planning more complicated. On the other hand, coordination and cooperation is very significant for multi-UAV p...
Chapter
This chapter aims to demonstrate how rule-based Artificial Intelligence algorithms can address a few human swarm teaming challenges. We will start from the challenges identified by the cognitive engineering community for building human autonomy teaming and how they scale to human swarm teaming. The discussion will follow with a description of rule-...
Chapter
Shepherding is a specific class of flocking behaviour where external agents (the shepherd) influence the movements of a group of agents (the flock). In nature, a powerful example is herding a flock of sheep by an influential sheepdog. When the sheepdog encroaches on the sheep’s influence zone, the sheep is essentially “repelled”. Optimising this ph...
Chapter
A novel technique has been developed for autonomous swarm-based unknown environment scouting. A control method known as swarm shepherding was employed, which replicates the behaviour seen when a sheepdog guides a herd of sheep to an objective location. The guidance of the swarm agents was implemented using low computation cost, force-based behaviou...
Article
Full-text available
This paper tackles the distributed leader–follower cooperative control problem for networked heterogeneous unmanned aerial vehicle–unmanned ground vehicle (UAV‐UGV) systems in unknown environments requiring formation keeping, obstacle avoidance, inter‐robot collision avoidance, and reliable robot communications. To adopt various formations, we desi...
Chapter
Developmental evolution of collective swarm behaviours promises new ways to evolve swarms with different movement characteristics. Preliminary work has developed value functions that can recognize emergent swarm behaviour and distinguish it from random behaviour in point-mass boid simulations. This paper examines the performance of several variants...
Article
Wind gusts are a significant barrier to the outdoor operation of teams of Unmanned Aerial Vehicle (UAVs) which operate in close proximity to each other or obstacles. In these situations, traditional control methods such as PID control may not perform adequately. Based on the Strictly Negative Imaginary (SNI) systems theory, this paper presents a no...
Article
Recently, Type-2 fuzzy systems have become increasingly prominent as they have been applied to various nonlinear control applications. This article presents an adaptive fuzzy controller based on the sliding-mode control theory. The proposed self-adaptive interval Type-2 fuzzy controller (SAF2C) is based on the Takagi-Sugeno (TS) fuzzy model and it...
Preprint
This paper presents an novel online system identification technique based on a recursive interval type-2 Takagi-Sugeno fuzzy C-means clustering technique (IT2-TS-FC) for modeling nonlinear uncertain dynamics of autonomous systems. The construction of the fuzzy antecedent parameters is based on the type-2 fuzzy C-means clustering (IT2FCM) technique,...
Article
This article studies nonlinear system identification of a small scale and flybar-free unmanned helicopter, the Trex450 chopper, built using commercial off-the-shelf components. We employ the real-time input-output data, obtained from human-controlled flight tests, operating the aircraft under severe ground effects during the vertical flight maneuve...
Article
We comprehensively discuss state-of-the-art integrated guidance and control (IGC) systems, in applications ranging from guided missiles to unmanned vehicles. Unlike separate guidance and control systems, IGC systems consider both control and guidance loops simultaneously, taking into account the cross-coupling relations and the limitations between...
Chapter
Full-text available
Due to many factors that range from ethical considerations and accountability to technological imperfection in autonomous systems, humans will continue to be an integral part of any meaningful autonomous system. While shepherding offers a technological concept that allows a human to operate a significantly larger number of autonomous systems that a...
Chapter
The coordination of unmanned air–ground vehicles has been an active area due to the significant advantages of this coordination wherein unmanned air vehicles (UAVs) have a wide field of view, enabling them to effectively guide a swarm of unmanned ground vehicles (UGVs). Due to significant recent advances in artificial intelligence (AI), autonomous...
Preprint
The guidance of a large swarm is a challenging control problem. Shepherding offers one approach to guide a large swarm using a few shepherding agents (sheepdogs). Noise is an inherent characteristic in many real-world problems. However, the impact of noise on shepherding is not well-studied. This impact could take two forms. First, noise in the sen...
Conference Paper
Full-text available
This paper describes a study on the perceived risk and trust of members of the general public regarding artificial intelligence applications. It assesses whether there is a difference in the perceptions of risk and trust in artificial intelligence expressed by the general public compared with those studying computer science in higher education. We...
Conference Paper
Swarm guidance, such as the case of guiding a group of sheep away from a field, is a challenging task. As the swarm size increases, it becomes necessary that multiple control points, or sheepdogs, are needed to guide the swarm. In this paper, a swarm of unmanned aerial vehicles (UAVs) acts as a moving safety network (aka a formation) that not only...
Article
Formation control of networked multi-agent systems has been widely implemented in robotics. We present a novel framework for swarm multi-agent systems based on the relative-position output feedback consensus supported with the new concept of adaptive strictly negative imaginary consensus controller leveraging the learning capability of neural netwo...
Conference Paper
Full-text available
This paper proposes a self-evolving Takagi-Sugeno fuzzy controller for nonlinear systems with uncertainties. The self-evolving framework is used to add and prune fuzzy rules in an online manner. Our proposed nonlinear controller is model-free and does not depend on the plant dynamics. All adjustable fuzzy parameters are tuned using a sliding surfac...
Article
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 sophisticated problems. The concept of swarming, inspired by nature, can be described as the emergence of complex system-level behaviors from the interactions of relatively...
Article
Mosquitoes' exceptional sensitivity to sound and airflow inspires new collision avoidance technology
Preprint
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 cooperative or non cooperative, lessons could be learnt from sheepdogs herding sheep. Biomimicry of shepherding offers computational methods for swarm control with the potential to...
Article
Robustness in the face of uncertainties is an integral part of designing a real-time control system. Based on Negative Imaginary (NI) systems theory, we design a robust control system for accurate trajectory tracking of a quadcopter aerial vehicle. Considering the challenging dynamics of aerial vehicles, we employ a knowledge-based Fuzzy Inference...
Preprint
A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality. This is especially the case for small Unmanned Aerial Vehicles (UAVs), as the platforms are highly dynamic and susceptible to breakage. Previous approaches often require simulation models with a high level of accuracy, otherwise significant er...
Article
The movement of cooperative robots in a densely cluttered environment may be impossible if the formation type is invariant. Hence, we investigate a novel method of switching time-invariant formation control for a group of heterogeneous autonomous vehicles, which may include Unmanned Ground Vehicles (UGV) and Unmanned Aerial Vehicles (UAV). We have...
Article
Full-text available
Background: Although many electroencephalographic (EEG) indicators have been proposed in the literature it is unclear which of the power bands and various indices is best as indicators of mental workload. Spectral powers (Theta, Alpha, and Beta) and ratios (Beta/(Alpha+Theta), Theta/Alpha, Theta/Beta) were identified in the literature as prominent...
Article
Full-text available
In recent times, technological advancement boosts the desire of utilizing the autonomous Unmanned Aerial Vehicle (UAV) in both civil and military sectors. Among various UAVs, the ability of rotary wing UAVs (RUAVs) in vertical take-off and landing, to hover and perform quick maneuvering attract researchers to develop models fully autonomous control...
Article
This paper presents a three-layered approach for the mission route planning problems involving a team of autonomous vehicles where they have to collectively navigate to a number of target locations in an environment with both static and dynamic obstacles. The first layer computes the maximum distance that need to be traveled to complete a mission b...
Article
We introduce a new configuration of a robust and adaptive autopilot system for a model-scale flapping-wing aircraft. The system is specifically designed for controlling the dynamics of a flapping-wing aircraft in order to achieve high performance flapping angle tracking in the face of large uncertainties. Considering the modeling part, we leverage...
Preprint
Full-text available
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 sophisticated problems. The concept of swarming, inspired by nature, can be described as the emergence of complex system-level behaviors from the interactions of relatively...
Article
In this paper, a novel Self-Evolving General Regression Neural Network (SEGRNN) is designed for tracking control of a class of discrete-time dynamic systems with unknown dynamics and unknown external disturbances. The proposed controller starts from scratch and automatically adjusts its structure and parameters online to solve the tracking control...
Chapter
This paper introduces a deep reinforcement learning method to train an autonomous aerial agent acting as a shepherd to provide guidance for a swarm of ground vehicles. The learner is situated within a high-fidelity robotic-operating-system (ROS)-based simulation environment consisting of an Unmanned Aerial Vehicle (UAV) learning to guide a swarm of...
Chapter
Full-text available
An important factor in the operational success of any teleoperated human-swarm system is situation awareness (SA). A loss of SA has been associated with poor human performance, which can lead to misjudgement, errors, and life-threatening situations. One of the major factors that causes loss of SA is the degradation of data transmission. It is imper...
Article
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 this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to t...
Article
We introduce a new system identification technique, leveraging on the benefits of the Evolutionary Takagi-Sugeno fuzzy system and the Type-2 interval fuzzy systems. Our technique has the ability to learn-from-scratch while having a better ability to accommodate the footprint-of-uncertainties (FoU). To support its mission in meeting the challenge in...