Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

Vu Phi Tran; Matthew A. Garratt; Kathryn Kasmarik; Sreenatha G. Anavatti

doi:10.1109/JAS.2023.123087

Volume 10 Issue 3

Mar. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(3): 646-661

V. P. Tran, M. A. Garratt, K. Kasmarik, and S. G. Anavatti, “Dynamic frontier-led swarming: Multi-robot repeated coverage in dynamic environments,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 646–661, Mar. 2023. doi: 10.1109/JAS.2023.123087

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V. P. Tran, M. A. Garratt, K. Kasmarik, and S. G. Anavatti, “Dynamic frontier-led swarming: Multi-robot repeated coverage in dynamic environments,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 646–661, Mar. 2023. doi: 10.1109/JAS.2023.123087

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Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

doi: 10.1109/JAS.2023.123087

School of Engineering and Information Technology, University of New South Wales, Canberra, Australian Capital Territory 2612, Australia

Funds: This work was supported by the DEFENCE SCIENCE & TECHNOLOGY GROUP (DSTG) (9729). The Commonwealth of Australia supported this research through a Defence Science Partnerships agreement with the Australian Defence Science and Technology Group

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Author Bio:
Vu Phi Tran received the B.E. degree in automation and control engineering from the HCMC University of Technology and Education, Vietnam, in 2010, the M.S. degree in mechatronics engineering from the Asian Institute of Technology, Thailand, in 2015, and the Ph.D. degree in the field of aerospace engineering from the University of New South Wales, Australia, in 2019. From 2010 to 2012, he was a Lecturer in the Department of Automation and Control Engineering, HCMC University of Technology and Education, Vietnam. Since 2020, Dr. Vu has been a Research Fellow at the University of New South Wales, Australia. His current research interests include adaptive control, robust control, consensus and formation control, neural networks, fuzzy systems, UAV systems and robotics

Matthew A. Garratt (Senior Member, IEEE) received the B.E. degree in aeronautical engineering from Sydney University, Australia, in 1990, the graduate Diploma in applied computer science from Central Queensland University, Australia, in 1997, and the Ph.D. degree in the field of biologically inspired robotics from The Australian National University, Australia, in 2008. He is a Professor at the School of Engineering and Information Technology (SEIT), University of New South Wales, Australia. He is currently the Chair of the CIS Task Force on the Ethics and Social Implications of CI. His research interests include sensing, guidance, and control for autonomous systems with particular emphasis on biologically inspired and computational intelligence approaches

Kathryn Kasmarik (Senior Member, IEEE) received the bachelor degree (Advanced, Hons. I, University Medal) in computer science and technology from the University of Sydney, Australia, in 2002, and the Ph.D. degree in computer science from the National ICT Australia, Sydney, and the University of Sydney, in 2007. She is an Associate Professor of Computer Science at the University of New South Wales, Australia. She has coauthored two books and over 100 refereed conference and journal papers. Her research lies in the areas of autonomous mental development and computational motivation, with applications in virtual characters in computer games, developmental robotics, and intelligent environments. Her research is principally concerned with the development of algorithms for self-motivated learning agents. Dr. Kasmarik is a Guest Editor of the IEEE Transactions on Cognitive and Developmental Systems, the Chair of the IEEE Technical Committee on Cognitive and Developmental Systems from 2017 to 2018; the Chair of the ACT Chapter of the IEEE Computational Intelligence Society from 2017 to 2018; ACT Section IEEE Women in Engineering from 2019 to 2020 and currently secretary of the IEEE ACT Section since 2021

Sreenatha G. Anavatti received the Ph.D. degree from the Indian Institute of Science, India. He was an Associate Professor with the Indian Institute of Technology Bombay, India. He moved to Australia in 1998. He is a Senior Lecturer with the School of Engineering and Information Technology, University of New South Wales Canberra, Australia. He has supervised a number of Ph.D. scholars over these years. His current research interests include the application of artificial intelligence techniques like fuzzy and neural systems for UAVs, underwater vehicles, ground vehicles, identification and control of dynamic systems, navigation and path planning for autonomous vehicles, evolutionary fuzzy systems and their applications to dynamic systems
Corresponding author: Vu Phi Tran, e-mail: phivus2@gmail.com
Received Date: 2022-10-13
Accepted Date: 2022-11-09

Available Online: 2023-01-10

Abstract

Abstract

A common assumption of coverage path planning research is a static environment. Such environments require only a single visit to each area to achieve coverage. However, some real-world environments are characterised by the presence of unexpected, dynamic obstacles. They require areas to be revisited periodically to maintain an accurate coverage map, as well as reactive obstacle avoidance. This paper proposes a novel swarm-based control algorithm for multi-robot exploration and repeated coverage in environments with unknown, dynamic obstacles. The algorithm combines two elements: frontier-led swarming for driving exploration by a group of robots, and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles. We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments. We measure both repeated coverage performance and obstacle avoidance ability. Through a series of comparison experiments, we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.
- Artificial pheromones,
- distributed control architecture,
- dynamic obstacle avoidance,
- multi-robot coverage,
- stigmergy,
- swarm robotics

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References

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Highlights

Present the novel multi-robot repeated area coverage and obstacle avoidance methods
Propose a flocking model to maintain a close-knit formation
Construct an evaluation of our method in different settings
Conduct a comparative study benchmarking our algorithm against other methodologies

Dynamic Frontier-Led Swarming: Multi-Robot Repeated Coverage in Dynamic Environments

doi: 10.1109/JAS.2023.123087

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通讯作者: 陈斌, bchen63@163.com

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