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During the transition of global energy systems toward low-carbon and high-reliability operation, nuclear power plant (NPP) operation and maintenance require environmental perception methods that are safe, energy-efficient, and sufficiently accurate for confined and radiation-risk areas. To address these requirements, this paper proposes an energy-oriented multi-robot collaborative exploration and mapping framework, termed I-WFD-Gmapping-DT. The framework integrates a digital twin (DT) 5+3 model, improved wavefront frontier detection (I-WFD), energy- and risk-aware task allocation, EKF-AMCL-based initial relative pose estimation, and multi-scale Gmapping map fusion. Unlike conventional frontier-based or single-objective exploration methods, the proposed utility function jointly considers discounted information gain, obstacle-sensitive path cost, estimated battery energy, angular dispersion, and safety constraints. A ROS-Gazebo simulation of an NPP-like environment was used for 30 independent runs with randomized seeds and starting perturbations. Compared with WFD-Gmapping, the proposed method increased the three-robot coverage area percentage from 35.6 ± 2.1% to 40.5 ± 1.9%, reduced exploration time by 13.35%, reduced total and used frontier target points by 38.9% and 23.24%, respectively, and reduced estimated energy consumption by 13.9%. Map accuracy was also improved, with AE decreasing from 12.45% to 11.52%, RMSE from 7.85% to 7.18%, and SSIM increasing from 0.78 to 0.83. Additional sensitivity, ablation, runtime, and initial-pose experiments confirm the robustness of the parameter selection and the contribution of the DT-enabled feedback mechanism. The results show that I-WFD-Gmapping-DT can enhance collaborative inspection efficiency, reduce redundant motion and energy consumption, and provide reliable mapping support for intelligent NPP operation and maintenance.
Wu et al. (Thu,) studied this question.