Hostname: page-component-77f85d65b8-457wm Total loading time: 0 Render date: 2026-03-26T07:59:23.541Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi-robot area coverage and source localization method based on variational sparse Gaussian process

Published online by Cambridge University Press:  21 May 2025

Kai Cao Open the ORCID record for Kai Cao [Opens in a new window] ,
Yunbo Wei Open the ORCID record for Yunbo Wei [Opens in a new window] ,
Yangquan Chen ,
Song Gao Open the ORCID record for Song Gao [Opens in a new window] ,
Kun Yan  and
Shibo Yang
Kai Cao
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China School of Engineering (MESA-Lab), University of California, Merced, CA, USA
Yunbo Wei
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
Yangquan Chen
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China School of Engineering (MESA-Lab), University of California, Merced, CA, USA
Song Gao*
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
Kun Yan
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
Shibo Yang
Affiliation:
School of Electronic Information Engineering, Xi’an Technological University, Xi’an, China
*
Corresponding author: Song Gao; Email: gaos@xatu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper introduces a distributed online learning coverage control algorithm based on sparse Gaussian process regression for addressing the problem of multi-robot area coverage and source localization in unknown environments. Considering the limitations of traditional Gaussian process regression in handling large datasets, this study employs multiple robots to explore the task area to gather environmental information and approximate the posterior distribution of the model using variational free energy methods, which serves as the input for the centroid Voronoi tessellation algorithm. Additionally, taking into consideration the localization errors, and the impact of obstacles, buffer factors and centroid Voronoi tessellation algorithms with separating hyperplanes are introduced for dynamic robot task area planning, ultimately achieving autonomous online decision-making and optimal coverage. Simulation results demonstrate that the proposed algorithm ensures the safety of multi-robot formations, exhibits higher iteration speed, and improves source localization accuracy, highlighting the effectiveness of model enhancements.

Keywords

multi-robotcentroid Voronoi tessellationvariational sparse Gaussian processbuffering factorsource localization

Information

Type
Research Article
Information
Robotica , Volume 43 , Issue 6 , June 2025 , pp. 2100 - 2120
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Wang, G., Wang, W., Ding, P., Liu, Y., Wang, H., Fan, Z., Bai, H., Hongbiao, Z. and Du, Z., “Development of a search and rescue robot system for the underground building environment,” J. Field Robot. 40(3), 655–683 (2023).CrossRefGoogle Scholar
Simon, M. E., Baldissera, F. L., deQueiroz, M. H. and Cabral, G., “Multi-robots coordination system for urban search and rescue assistance based on supervisory control theory,” J. Control Autom. Electr. Syst. 40(3), 484–495 (2023).CrossRefGoogle Scholar
Du, Y., “Multi-uav search and rescue with enhanced a algorithm path planning in 3d environment,” Int. J. Aerosp. Eng. 2023, 1–18 (2023).CrossRefGoogle Scholar
Song, X., Cheng, Y., Qi, Q. and Zou, X.. “A Path Planning Method for Robot-aided Aero-engine Ffeet Inspection Considering Resource Reuse Strategy.” In: IEEE International Conference on Industrial Technology (ICIT) (2022) pp. 1–6.Google Scholar
Best, G. and Hollinger, G. A.. “Decentralised Self-organising Maps for Multi-robot Information Gathering.” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020) pp. 4790–4797.Google Scholar
Zhang, T., Qin, V., Tang, Y. and Li, N.. “Source Seeking by Dynamic Source Location Estimation.” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021) pp. 2598–2605.Google Scholar
Wiedemann, T., Vlaicu, C., Josifovski, J. and Viseras, A., “Robotic information gathering with reinforcement learning assisted by domain knowledge: An application to gas source localization,” IEEE Access. 9, 13159–13172 (2021).CrossRefGoogle Scholar
Gundana, D. and Kress-Gazit, H., “Event-based signal temporal logic synthesis for single and multirobot tasks,” IEEE Robot. Autom. Lett. 6(2), 3687–3694 (2021).CrossRefGoogle Scholar
Queralta, J. P., Taipalmaa, J., Pullinen, B. C., Sarker, V. K., Gia, T. N. and Tenhunen, H., “Collaborative multi-robot search and rescue: Planning, coordination, perception, and active vision,” IEEE Access. 8, 191617–191643 (2020).CrossRefGoogle Scholar
Queralta, J. P., Li, Q., Schiano, F. and Westerlund, T.. “VIO-UWB-based Collaborative Localization and Dense Scene Reconstruction within Heterogeneous Multi-robot Systems.” In: International Conference on Advanced Robotics and Mechatronics (ICARM) (2022) pp. 87–94.Google Scholar
Zhou, M., Wang, Z., Wang, J. and Dong, Z., “A hybrid path planning and formation control strategy of multi-robots in a dynamic environment,” J. Adv. Comput. Intell. Intell. Inform. 26(3), 342–354 (2022).CrossRefGoogle Scholar
An, X., Wu, C., Lin, Y., Lin, M., Yoshinaga, T. and Ji, Y., “Multi-robot systems and cooperative object transport: Communications, platforms, and challenges,” IEEE Open J. Comput. Soc. 4, 23–36 (2023).CrossRefGoogle Scholar
Orr, J. and Dutta, A., “Multi-agent deep reinforcement learning for multi-robot applications: A survey,” Sensors 23(7), 3625 (2023).CrossRefGoogle ScholarPubMed
Renzaglia, A. and Briñón-Arranz, L., “Search and localization of a weak source with a multi-robot formation,” J. Intell. Robot. Syst. 97(3), 623–634 (2020).CrossRefGoogle Scholar
Mascarich, F., Kulkarni, M., Petris, P. D., Wilson, T. and Alexis, K., “Autonomous mapping and spectroscopic analysis of distributed radiation fields using aerial robots,” Auton. Robot. 47(2), 139–160 (2023).CrossRefGoogle Scholar
Chen, X., Yang, B., Huang, J., Leng, Y. and Fu, C., “A reinforcement learning fuzzy system for continuous control in robotic odor plume tracking,” Robotica 41(3), 1039–1054 (2023).CrossRefGoogle Scholar
Karagüzel, T. A., Turgut, A. E., Eiben, A. E. and Ferrante, E., “Collective gradient perception with a ffying robot swarm,” Swarm Intell. 17(1), 117–146 (2023).CrossRefGoogle Scholar
Jiang, M., Liao, Y., Guo, X., Cai, H., Jiang, W., Yang, Z., Li, F. and Liu, F., “A comparative experimental study of two multi-robot olfaction methods: Towards locating time-varying indoor pollutant sources,” Build. Environ. 207, 108560 (2022).CrossRefGoogle Scholar
Chang, L., Shan, L., Zhang, W. and Dai, Y., “Hierarchical multi-robot navigation and formation in unknown environments via deep reinforcement learning and distributed optimization,” Robot. Comput. Integr. Manuf. 83, 102570 (2023).CrossRefGoogle Scholar
Jang, H., Park, M. and Oh, H., “Improved Socialtaxis for information-theoretic source search using cooperative multiple agents in turbulent environments,” Expert Syst. Appl. 225, 120033 (2023).CrossRefGoogle Scholar
Chen, L., Zhou, C., Wang, Y., Zong, Y., Lu, T. and Chen, C., “Autonomous search investigation for radioactive leaked source based on an updated infotaxis method during nuclear emergency rescue,” Nucl. Eng. Des. 416, 112769 (2024).CrossRefGoogle Scholar
An, S., Park, M. and Oh, H., “Receding-horizon RRT-Infotaxis for autonomous source search in urban environments,” Aerosp. Sci. Technol. 120, 107276 (2022).CrossRefGoogle Scholar
Ma, H., Zhang, T., Wu, Y., Calmon, F. P. and Li, N.. “Gaussian Max-Value Entropy Search for Multiagent Bayesian Optimization.” In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023) pp. 10028–10035.Google Scholar
Denniston, C. E., Peltzer, O., Ott, J., Moon, S., Kim, S. K., Sukhatme, G. S. and Kochenderfer, M. J.. “Fast and Scalable Signal Inference for Active Robotic Source Seeking.” In: IEEE International Conference on Robotics and Automation (ICRA) (2023) pp. 7909–7915.Google Scholar
Newaz, A. A. R., Alsayegh, M., Alam, T. and Bobadilla, L., “Decentralized multi-robot information gathering from unknown spatial fields,” IEEE Robot. Autom. Lett. 8(5), 3070–3077 (2023).CrossRefGoogle Scholar
Yungaicela-Naula, N. M., Zhang, Y., Garza-Castañon, L. E. and Minchala, L. I.. “UAV-based Air Pollutant Source Localization Using Gradient and Probabilistic Methods.” In: International conference on unmanned aircraft systems (ICUAS) (2018) pp. 702–707.Google Scholar
Li, H., Yuan, J. and Yuan, H., “An active olfaction approach using deep reinforcement learning for indoor attenuation odor source localization,” IEEE Sens. J. 24(9), 14561–14572 (2024).CrossRefGoogle Scholar
Hu, J., Niu, H., Carrasco, J., Lennox, B. and Arvin, F., “Voronoi-based multi-robot autonomous exploration in unknown environments via deep reinforcement learning,” IEEE Trans. Veh. Technol. 69(12), 14413–14423 (2020).CrossRefGoogle Scholar
Feng, Q., Cai, H., Yang, Y., Xu, J., Jiang, M., Li, F., Li, X. and Yan, C., “An experimental and numerical study on a multi-robot source localization method independent of airffow information in dynamic indoor environments,” Sustain. Cities Soc. 69, 101897 (2020).CrossRefGoogle Scholar
Luo, W., Nam, C., Kantor, G. and Sycara, K.. “Distributed Environmental Modeling and Adaptive Sampling for Multi-robot Sensor Coverage.” In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems (2019) pp. 1488–1496.Google Scholar
Zhen, Z., Chen, Y., Wen, L. and Han, B., “An intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment,” Aerosp. Sci. Technol. 100, 105826 (2020).CrossRefGoogle Scholar
Wu, Z., Dai, J., Jiang, B. and Karimi, H. R., “Robot path planning based on artiffcial potential field with deterministic annealing,” ISA Trans. 138, 74–87 (2023).CrossRefGoogle Scholar
Gao, Y., Bai, C., Fu, R. and Quan, Q., “A non-potential orthogonal vector field method for more efffcient robot navigation and control,” Robot. Auton. Syst. 159, 104291 (2023).CrossRefGoogle Scholar
Zhong, X., Tian, J., Hu, H. and Peng, X., “Hybrid path planning based on safe A* algorithm and adaptive window approach for mobile robot in large-scale dynamic environment,” J Intell. Robot. Syst. 99(1), 65–77 (2020).CrossRefGoogle Scholar
García, E., Villar, J. R., Tan, Q., Sedano, J. and Chira, C., “An efffcient multi-robot path planning solution using A* and coevolutionary algorithms,” Integr. Comput.-Aided Eng. 30(1), 41–52 (2023).CrossRefGoogle Scholar
Kiadi, M., García, E., Villar, J. R. and Tan, Q., “A*-based co-evolutionary approach for multi-robot path planning with collision avoidance,” Cybern. Syst. 54(3), 339–354 (2023).CrossRefGoogle Scholar
Wang, Y., He, Z., Cao, D., Ma, L., Li, K., Jia, L. and Cui, Y., “Coverage path planning for kiwifruit picking robots based on deep reinforcement learning,” Comput. Electron. Agric. 205, 107593 (2023).CrossRefGoogle Scholar
Chen, L., Wang, Y., Miao, Z., Mo, Y., Feng, M., Zhou, Z. and Wang, T, “Transformer-based imitative reinforcement learning for multi-robot path planning,” IEEE Trans. Ind. Inf. 19(10), 10233–10243 (2023).CrossRefGoogle Scholar
Tan, J., “A method to plan the path of a robot utilizing deep reinforcement learning and multiSensory information fusion,” Appl. Artif. Intell. 37(1), 2224996 (2023).CrossRefGoogle Scholar
Zhu, L., Cheng, J., Zhang, H., Zhang, W. and Liu, Y., “Multi-robot environmental coverage with a two-stage coordination strategy via deep reinforcement learning,” IEEE Trans. Intell. Transp. Syst. 25(6), 5022–5033 (2024).CrossRefGoogle Scholar
Chen, Y., Guo, M. and Li, Z., “Deadlock resolution and recursive feasibility in MPC-based multirobot trajectory generation,” IEEE Trans. Autom. Control 69(9), 6058–6073 (2024).CrossRefGoogle Scholar
Cao, K., Chen, Y., Gao, S., Dang, H. and An, D., “Distributed weighted coverage for multi-robot systems in non-convex environment,” Appl. Sci. 13(14), 8530 (2023).CrossRefGoogle Scholar
Wang, X., Xi, L. L., Ding, Y. and Chen, B. M., “Distributed encirclement and capture of multiple pursuers with collision avoidance,” IEEE Trans. Ind. Electron. 71(7), 7520–7530 (2023).CrossRefGoogle Scholar
Urban, R., ŠTRONER, M. and Kuric, I., “The use of onboard UAV GNSS navigation data for area and volume calculation,” Acta Montan Slovaca 25(3), 364–371 (2020).Google Scholar
Hu, J., Zhang, H., Sfarra, S., Pivarčiová, E., Yao, Y., Duan, Y. X., Ibarra-Castanedo, C., Tian, G. Y. and Maldague, X., “Autonomous dynamic line-scan continuous-wave terahertz non-destructive inspection system combined with unsupervised exposure fusion,” NDT & E Int. 132, 102705 (2022).CrossRefGoogle Scholar