ORCID Profile
0000-0003-0222-430X
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Aerospace Engineering | Control Systems, Robotics and Automation | Artificial Intelligence and Image Processing | Zoology | Neural, Evolutionary and Fuzzy Computation | Animal Behaviour | Information Systems Management | Flight Control Systems | Interdisciplinary Engineering Not Elsewhere Classified | Research, Science And Technology Policy | Control Engineering | Electrical Engineering | Flight Dynamics | Aircraft Performance | Interdisciplinary Engineering |
Combined operations | Emerging Defence Technologies | Air transport | Law enforcement | Navy | Technological and organisational innovation | Aerospace Transport not elsewhere classified | Other | Computer software and services not elsewhere classified | Expanding Knowledge in the Biological Sciences | Transport not elsewhere classified
Publisher: Springer Science and Business Media LLC
Date: 16-10-2010
Publisher: IEEE
Date: 12-2012
Publisher: Elsevier BV
Date: 10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: IEEE
Date: 2008
Publisher: IEEE
Date: 07-2012
Publisher: American Institute of Aeronautics and Astronautics (AIAA)
Date: 10-2017
DOI: 10.2514/1.G002537
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 07-2012
Publisher: Association for Computing Machinery (ACM)
Date: 15-09-2023
DOI: 10.1145/3611652
Abstract: The behaviour of social insects such as bees and ants has influenced the development of swarm robots. To enable robots to cooperate together, swarm robotics employs principles such as communication, coordination, and collaboration. Collaboration among multiple robots can lead to a faster task completion time compared to the utilisation of a single, complex robot. One of the key aspects of swarm robotics is that control is distributed uniformly across the robots in the swarm, which boosts the system’s resilience and fault-tolerance. Through the use of the robots’ embodied sensors and actuators, this distributed control often facilitates the emergence of collective behaviours through the interaction of the robots with one another and with the environment. The purpose of this survey is to examine the reasons behind the lack of utilisation of swarm robots in multi-tasking applications, which will be accomplished by studying previous research works in the field. We examine the literature from the perspective of multi-tasking: we pay particular attention to concepts that contribute to the progress of swarm robotics for multi-tasking applications. In order to do this, we first examine the different studies in multi-tasking swarm robotics, covering platforms, multi-tasking scenarios, sub-task allocation methodologies, as well as performance metrics. We then highlight several swarm robotics-related disciplines that have significant effect on the development of swarm robotics for multi-tasking problems. We propose two taxonomies: the first categorises works based on the characteristics of the scenarios being handled, while the second taxonomy categorises works based on the swarming strategies utilised to achieve multi-tasking capabilities. We finish with a discussion of swarm robots’ existing limitations for real-world multitasking applications, as well as recommendations for future research directions.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: IEEE
Date: 06-09-2023
Publisher: SAGE Publications
Date: 06-2004
Abstract: Flying insects provide a clear demonstration that living organisms can display surprisingly competent mechanisms of guidance and navigation, despite possessing relatively small brains and simple nervous systems. Consequently, they are proving to be excellent organisms in which to investigate how visual information is exploited to guide locomotion and navigation. Four illustrative ex les are described here, in the context of navigation to a destination. Bees negotiate narrow gaps by balancing the speeds of the images in the two eyes. Flight speed is regulated by holding constant the average image velocity as seen by the two eyes. This automatically ensures that flight speed is reduced to a safe level when the passage narrows. Smooth landings on a horizontal surface are achieved by holding image velocity constant as the surface is approached, thus automatically ensuring that flight speed is close to zero at touchdown. Roll and pitch are stabilized by balancing the signals registered by three visual organs, the ocelli, that view the horizon in the left, right and forward directions respectively. Tests of the feasibility of these navigational strategies, by implementation in robots, are described.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: IEEE
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2020
Publisher: Elsevier BV
Date: 2011
Publisher: Elsevier BV
Date: 07-2022
Publisher: SPIE-Intl Soc Optical Eng
Date: 15-09-2017
Publisher: MDPI AG
Date: 13-10-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Publisher: Springer Science and Business Media LLC
Date: 17-08-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: IEEE
Date: 2007
Publisher: Wiley
Date: 12-05-2009
DOI: 10.1002/RNC.1458
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 10-2017
Publisher: MDPI AG
Date: 10-03-2023
Abstract: This paper presents the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). Our system has two major learning stages, namely, structure learning and parameters learning. The structure phase does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one initial fuzzy rule. The rules are then continuously updated and pruned in an online fashion to achieve the desired set point. For the phase of learning parameters, all adjustable parameters of the fuzzy system are tuned by using a sliding surface method, which is based on the gradient descent algorithm. This method is used to minimize the difference between the expected and actual signals. Our proposed control method is model-free and does not require prior knowledge of the plant’s dynamics or constraints. Instead, data-driven control utilizes artificial intelligence-based techniques, such as fuzzy logic systems, to learn the dynamics of the system and adapt to changes in the system, and account for complex interactions between different components. A robustness term is incorporated into the control effort to deal with external disturbances in the system. The proposed technique is applied to regulate the dynamics of a mobile robot in the presence of multiple external disturbances, demonstrating the robustness of the proposed control systems. A rigorous comparative study with respect to three different controllers is performed, where the outcomes illustrate the superiority of the proposed learning method as evidenced by lower RMSE values and fewer fuzzy parameters. Lastly, stability analysis of the proposed control method is conducted using the Lyapunov stability theory.
Publisher: Elsevier BV
Date: 03-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer International Publishing
Date: 2019
Publisher: MDPI AG
Date: 19-05-2023
DOI: 10.3390/S23104892
Abstract: Robot swarms are becoming popular in domains that require spatial coordination. Effective human control over swarm members is pivotal for ensuring swarm behaviours align with the dynamic needs of the system. Several techniques have been proposed for scalable human–swarm interaction. However, these techniques were mostly developed in simple simulation environments without guidance on how to scale them up to the real world. This paper addresses this research gap by proposing a metaverse for scalable control of robot swarms and an adaptive framework for different levels of autonomy. In the metaverse, the physical/real world of a swarm symbiotically blends with a virtual world formed from digital twins representing each swarm member and logical control agents. The proposed metaverse drastically decreases swarm control complexity due to human reliance on only a few virtual agents, with each agent dynamically actuating on a sub-swarm. The utility of the metaverse is demonstrated by a case study where humans controlled a swarm of uncrewed ground vehicles (UGVs) using gestural communication, and via a single virtual uncrewed aerial vehicle (UAV). The results show that humans could successfully control the swarm under two different levels of autonomy, while task performance increases as autonomy increases.
Publisher: Institution of Engineering and Technology (IET)
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Elsevier BV
Date: 06-2021
Publisher: Wiley
Date: 04-2008
DOI: 10.1002/ROB.20239
Abstract: Experiments from biology suggest that the sensing of image motion or optic flow in insects provides a means of determining the range to obstacles and terrain. When combined with a measure of ground speed from another sensor such as global positioning system, optic flow can be used to provide a measure of an aircraft's height above terrain. We apply this principle to the control of height in a helicopter, leading to the first optic flow–based terrain‐following system for an unmanned helicopter. Using feedback of the height estimated from optic flow ranging to the collective pitch control of the helicopter, it has been possible to maintain terrain clearance in flights of up to 2 km. In this paper, we present flight test data demonstrating the successful application of this to an 80‐kg Yamaha RMAX unmanned helicopter and an 8‐kg electric helicopter. To complete this work, we have extended the optic flow image interpolation algorithm ( I 2 A ) to include an adaptive capability providing a greater dynamic range. The new algorithm, called the iterative image interpolation algorithm ( I 2 A ), exhibits excellent robustness in an outdoor environment and makes it suitable for flight control in a real‐world environment. © 2008 Wiley Periodicals, Inc.
Publisher: IEEE
Date: 11-2011
Publisher: Elsevier BV
Date: 06-2013
Publisher: IEEE
Date: 07-2012
Publisher: IEEE
Date: 2008
Publisher: Elsevier BV
Date: 03-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 11-2017
Publisher: IEEE
Date: 04-2013
Publisher: American Institute of Aeronautics and Astronautics
Date: 26-06-2010
DOI: 10.2514/6.2010-7733
Publisher: Cambridge University Press (CUP)
Date: 10-2016
DOI: 10.1017/S1743921317001247
Abstract: Planetary nebulae retain the signature of the nucleosynthesis and mixing events that occurred during the previous AGB phase. Observational signatures complement observations of AGB and post-AGB stars and their binary companions. The abundances of the elements heavier than iron such as Kr and Xe in planetary nebulae can be used to complement abundances of Sr/Y/Zr and Ba/La/Ce in AGB stars, respectively, to determine the operation of the slow neutron-capture process (the s process) in AGB stars. Additionally, observations of the Rb abundance in Type I planetary nebulae may allow us to infer the initial mass of the central star. Several noble gas components present in meteoritic stardust silicon carbide (SiC) grains are associated with implantation into the dust grains in the high-energy environment connected to the fast winds from the central stars during the planetary nebulae phase.
Publisher: Springer Science and Business Media LLC
Date: 08-2010
Publisher: IEEE
Date: 11-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Wiley
Date: 06-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Publisher: IEEE
Date: 11-2011
Publisher: IEEE
Date: 2006
Publisher: IEEE
Date: 11-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
Publisher: Elsevier BV
Date: 12-2011
Publisher: IEEE
Date: 02-2017
Publisher: IEEE
Date: 07-2009
Publisher: IEEE
Date: 02-2017
No related organisations have been discovered for Matthew Garratt.
Start Date: 06-2004
End Date: 06-2008
Amount: $215,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2020
End Date: 06-2024
Amount: $373,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 10-2022
End Date: 10-2025
Amount: $411,224.00
Funder: Australian Research Council
View Funded ActivityStart Date: 12-2004
End Date: 12-2010
Amount: $1,950,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2010
End Date: 06-2013
Amount: $290,000.00
Funder: Australian Research Council
View Funded Activity