Select Publications

Preprints

Dong X; Garratt MA; Anavatti SG; Abbass HA; Dong J, 2022, Lightweight Monocular Depth Estimation with an Edge Guided Network, http://dx.doi.org/10.48550/arxiv.2209.14829

Long NK; Sgarioto D; Garratt M; Sammut K, 2022, Response Component Analysis for Sea State Estimation Using Artificial Neural Networks and Vessel Response Spectral Data, http://arxiv.org/abs/2205.02375v2

Tran VP; Mabrok MA; Anavatti SG; Garratt MA; Petersen IR, 2022, Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems, http://arxiv.org/abs/2203.13959v1

Tran VP; Garratt MA; Kasmarik K; Anavatti SG, 2021, Frontier-led Swarming: Robust Multi-Robot Coverage of Unknown Environments, http://arxiv.org/abs/2111.14295v2

Dong X; Garratt MA; Anavatti SG; Abbass HA, 2021, MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation, http://dx.doi.org/10.48550/arxiv.2111.12334

Dong X; Garratt MA; Anavatti SG; Abbass HA, 2021, Towards Real-Time Monocular Depth Estimation for Robotics: A Survey, http://dx.doi.org/10.48550/arxiv.2111.08600

Nguyen HT; Garratt M; Bui LT; Abbass H, 2020, Disturbances in Influence of a Shepherding Agent is More Impactful than Sensorial Noise During Swarm Guidance, http://dx.doi.org/10.48550/arxiv.2008.12708

Nguyen HT; Nguyen TD; Tran VP; Garratt M; Kasmarik K; Anavatti S; Barlow M; Abbass HA, 2020, Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding, http://dx.doi.org/10.48550/arxiv.2004.11543

Qiu H; Garratt M; Howard D; Anavatti S, 2020, Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers, http://dx.doi.org/10.48550/arxiv.2002.09854

Long NK; Sammut K; Sgarioto D; Garratt M; Abbass H, 2019, A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach, http://dx.doi.org/10.48550/arxiv.1912.07796

Qiu H; Garratt M; Howard D; Anavatti S, 2019, Evolving Spiking Neural Networks for Nonlinear Control Problems, http://dx.doi.org/10.48550/arxiv.1903.01180

Tran VP; Garratt M; Petersen IR, 2018, Distributed Obstacle and Multi-Robot Collision Avoidance in Uncertain Environments, http://dx.doi.org/10.48550/arxiv.1811.06196

Tran VP; Garratt M; Petersen IR, 2018, Time-Varying Formation Control of a Collaborative Multi-Agent System Using Negative-Imaginary Systems Theory, http://dx.doi.org/10.48550/arxiv.1811.06206

Ferdaus MM; Pratama M; Anavatti SG; Garratt MA; Lughofer E, 2018, PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles, http://dx.doi.org/10.48550/arxiv.1811.03764

Nguyen H; Tran V; Nguyen T; Garratt M; Kasmarik K; Barlow M; Anavatti S; Abbass H, 2018, Apprenticeship Bootstrapping Via Deep Learning with a Safety Net for UAV-UGV Interaction, http://dx.doi.org/10.48550/arxiv.1810.04344

Ferdaus MM; Anavatti SG; Garratt MA; Pratama M, 2018, Development of a Sliding Mode Control Based Adaptive Fuzzy Controller for a Flapping Flight, http://dx.doi.org/10.48550/arxiv.1806.02945

Al-Mahasneh AJ; Anavatti SG; Garratt MA, 2018, Review of Applications of Generalized Regression Neural Networks in Identification and Control of Dynamic Systems, http://dx.doi.org/10.48550/arxiv.1805.11236

Ferdaus MM; Pratama M; Anavatti SG; Garratt MA, 2018, PALM: An Incremental Construction of Hyperplanes for Data Stream Regression, http://dx.doi.org/10.48550/arxiv.1805.04258

Ferdaus MM; Pratama M; Anavatti SG; Garratt MA, 2018, A Generic Self-Evolving Neuro-Fuzzy Controller based High-performance Hexacopter Altitude Control System, http://dx.doi.org/10.48550/arxiv.1805.02508

Ferdaus MM; Anavatti SG; Garratt MA; Pratama M, 2018, Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle, http://dx.doi.org/10.48550/arxiv.1802.01262

Ferdaus MM; Pratama M; Anavatti SG; Garratt MA; Pan Y, 2018, Generic Evolving Self-Organizing Neuro-Fuzzy Control of Bio-inspired Unmanned Aerial Vehicles, http://dx.doi.org/10.48550/arxiv.1802.00635


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