Abstract
In the context of Industry 5.0, the significance of MRN (Mobile Robot Navigation) cannot be overstated, as it is crucial for facilitating the collaboration between machines and humans. To augment MRN capabilities, emerging technologies such as federated learning (FL) are being utilized. FL enables the consolidation of knowledge from numerous robots located in diverse areas, enabling them to collectively learn and enhance their navigation skills. By integrating FL into MRN systems, Industry 5.0 can effectively utilize collaborative intelligence for efficient and high-quality production processes. When considering information representation in MRN, the adoption of picture fuzzy sets (PFSs), which expand upon the concept of intuitionistic fuzzy sets, offers significant advantages in effectively handling information inconsistencies in practical situations. Specifically, by leveraging the benefits of multi-granularity (MG) probabilistic rough sets (PRSs) and three-way decisions (3WD) within the FL framework, an efficient MRN approach based on FL and 3WD is thoroughly investigated. Initially, the adjustable MG picture fuzzy (PF) PRS model is developed by incorporating MG PRSs into the PF framework. Subsequently, the PF maximum deviation method is utilized to calculate various weights. In order to determine the optimal granularity of MG PF membership degrees, the CODAS (Combinative Distance based ASsesment) method is employed, known for its flexibility in handling both quantitative and qualitative attributes whereas effectively managing incomplete or inconsistent data with transparency and efficiency. After determining the optimal granularity, the MRN method grounded in FL and 3WD is established. Finally, a realistic case study utilizing MRN data from the Kaggle database is performed to validate the feasibility of our method.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (62272284; 62072291; 62072294; 61972238; 61703363), the Special Fund for Science and Technology Innovation Teams of Shanxi (202204051001015), the Cultivate Scientific Research Excellence Programs of Higher Education Institutions in Shanxi (CSREP) (2019SK036), the Graduate Education Innovation Programs of Shanxi University (SXU2022Y256), and the Training Program for Young Scientific Researchers of Higher Education Institutions in Shanxi.
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Zhang, C., Hou, H., Sangaiah, A.K., Li, D., Cao, F., Wang, B. (2024). Efficient Mobile Robot Navigation Based on Federated Learning and Three-Way Decisions. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14447. Springer, Singapore. https://doi.org/10.1007/978-981-99-8079-6_32
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