With the rapid advancement of communication technologies, smart factories increasingly rely on Mobile Edge Computing (MEC) to manage the surging volume of task requests. The computational burden on industrial sites can be effectively mitigated when computation-intensive tasks are offloaded to nearby edge servers by MEC. However, the limited computational and storage capacities of edge servers, combined with their susceptibility to dynamic failures in industrial environments, pose significant challenges for reliable and efficient task offloading. To overcome these challenges, we propose a Robust Task Offloading Method for Smart Factories (RTOMSF). By utilizing digital twin technology, RTOMSF provides real-time monitoring and prognostic fault detection for edge servers. Moreover, we enhance the Group Relative Policy Optimization algorithm by incorporating a noisy linear layer into the policy network, thereby improving the robustness and adaptability of task offloading decisions in a dynamic environment. Extensive experiments conducted on realistic industrial scenarios demonstrate that RTOMSF consistently surpasses state-of-the-art baselines in both reliability and efficiency.

Parallel real-time systems often contain functionalities with complex dependencies and execution uncertainties, leading to significant timing variability which can be represented as a probabilistic distribution. However, existing timing analysis either produces a single conservative bound or incurs high computational costs due to the exhaustive enumeration of every execution scenario. This significantly hinders the exploitation of the probabilistic timing behaviours during system design, leading to sub-optimal design solutions. Modelling the system as a probabilistic directed acyclic graph ($p$-DAG), this paper presents a probabilistic response time analysis based on different longest paths of the$p$-DAG across all execution scenarios, enhancing the capability of the analysis by eliminating the need for enumeration. We first identify every longest path candidate based on the structure of$\boldsymbol{p}$-DAG and compute the probability of its occurrence, where each candidate is the longest under certain execution scenarios. Then, the worst-case interfering workload is computed for each longest path candidate, forming a complete probabilistic response time distribution with correctness guarantees. Experiments show that compared to the enumeration-based approach, the proposed analysis reduces the computation cost by six orders of magnitude while maintaining a low deviation ($\mathbf{1. 0 4 \%}$on average and below$\mathbf{5 \%}$for most$\boldsymbol{p}$-DAGs).

Building an accurate map is essential for autonomous robot navigation in the environment without GPS. Compared with single-robot, the multiple-robot system has much better performance in terms of accuracy, efficiency and robustness for the simultaneous localization and mapping (SLAM). As a critical component of multiple-robot SLAM, the problem of map merging still remains a challenge. To this end, this article casts it into point set registration problem and proposes an effective map merging method based on the context-based descriptors and correspondence expansion. It first extracts interest points from grid maps by the Harris corner detector. By exploiting neighborhood information of interest points, it automatically calculates the maximum response radius as scale information to compute the context-based descriptor, which includes eigenvalues and normals computed from local structures of each interest point. Then, it effectively establishes origin matches with low precision by applying the nearest neighbor search on the context-based descriptor. Further, it designs a scale-based corresponding expansion strategy to expand each origin match into a set of feature matches, where one similarity transformation between two grid maps can be estimated by the Random Sample Consensus algorithm. Subsequently, a measure function formulated from the trimmed mean square error is utilized to confirm the best similarity transformation and accomplish the coarse map merging. Finally, it utilizes the scaling trimmed iterative closest point algorithm to refine initial similarity transformation so as to achieve accurate merging. As the proposed method considers scale information in the context-based descriptor, it is able to merge grid maps in diverse resolutions. Experimental results on real robot datasets demonstrate its superior performance over other related methods on accuracy and robustness.