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Collaborative ground and aerial robots in hazard mapping based on heterogeneous coverage

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Abstract

A collaborative scheme of multiple ground and aerial robots applying a heterogeneous coverage control approach is presented. It aims to provide a density map of a contaminated area from hazardous material. Compared to a homogeneous scheme, heterogeneity enhances the coverage level by minimizing error and variance due to the estimation process. In this scheme, a weighting formulation based on the different characteristics of ground and aerial robots is formalized. The contaminated area is partitioned unequally according to the number of deployed robots corresponding to the robot’s weight and type. It shows better estimation values of the estimated density distribution map than the homogeneous scheme. The operation time needed to provide an estimation map of density distribution over the region is also faster than the homogeneous scheme.

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Acknowledgements

We sincerely thank the Endowment Fund of Education, Ministry of Finance, the Republic of Indonesia (LPDP RI), for partially funding this research and financial support of the doctoral study through the BUDI-DN scholarship (Grant Number: 201812210113689). We also thank Telkom University, the first author’s home base institution, for supporting the doctoral study.

Funding

The Endowment Fund of Education partially funded this research, Ministry of Finance, the Republic of Indonesia (LPDP RI), supporting doctoral scholarship (BUDI-DN, Grant Number: 201812210113689).

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All authors contributed to the study’s conception and design. ANJ wrote material preparation, data collection, analysis, and manuscript. Meanwhile, other authors directed and supervised the research; reviewed, commented, and corrected previous manuscript versions. All authors read and approved the final manuscript.

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Correspondence to Agung Nugroho Jati.

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Jati, A.N., Trilaksono, B.R., Hidayat, E.M.I. et al. Collaborative ground and aerial robots in hazard mapping based on heterogeneous coverage. Int J Intell Robot Appl 7, 615–630 (2023). https://doi.org/10.1007/s41315-023-00288-w

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