A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 10 Issue 3
Mar.  2023

IEEE/CAA Journal of Automatica Sinica

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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
Citation: 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

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

doi: 10.1109/JAS.2023.123087
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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  • 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.

     

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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

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