Assembly precision analysis (APA) plays an important role in the whole life cycle of complex products design, manufacturing, assembly and even remanufacturing. Assembly precision information model (APIM) is usually complex since it is affected by many factors, such as design tolerance of parts, assembly process scheme, assembly sequence planning and tolerance of positioning tooling, etc. Therefore, it is of practical significance for APA to reasonably reduce the workload of assembly precision information (API) modeling. A semantic simplification approach for APIM is proposed in this paper, which mainly takes semantic relations between APIM and design tolerance of parts into consideration. Initially, ontology of structure knowledge of APIM is constructed according to a tolerance standard. Furthermore, simplification rules are respectively established by considering two semantic relations: one semantic relation between deviation change direction and deviation accumulation direction and the other semantic relation among multiple geometric characteristics on the same geometric feature. Additionally, by utilizing ontology reasoning function, the simplified semantic APIM is generated. Finally, the effectiveness of the proposed method is demonstrated by a practical example of engine front auxiliary drive equipment. It is expected that our work would lay the foundation for APA of complex products based on actual measured data.

To address the challenges of low efficiency, instability, and difficulties in meeting multiple constraints simultaneously in multi-AGV (Automated Guided Vehicle) task scheduling for intelligent manufacturing and logistics, this paper introduces a scheduling method based on multi-feature constraints and an improved deep reinforcement learning (DRL) approach (Improved Proximal Policy Optimization, IPPO). The method integrates multiple constraints, including minimizing task completion time, reducing penalty levels, and minimizing scheduling time deviation, into the scheduling optimization process. Building on the conventional PPO algorithm, several enhancements are introduced: a dynamic penalty mechanism is implemented to adaptively adjust constraint weights, a structured reward function is designed to boost learning efficiency, and sampling bias correction is combined with global state awareness to improve training stability and global coordination. Simulation experiments demonstrate that, after 10,000 iterations, the minimum task completion time drops from 98.2 s to 30 s, the penalty level decreases from 130 to 82, and scheduling time deviation reduces from 12 s to 0.5 s, representing improvements of 69.4%, 37%, and 95.8% in the same scenario, respectively. Compared to genetic algorithms (GAs) and rule-based scheduling methods, the IPPO approach demonstrates significant advantages in average task completion time, total system makespan, and overall throughput, along with faster convergence and better stability. These findings demonstrate that the proposed methodology enables effective multi-objective collaborative optimization and efficient task scheduling within complex dynamic environments, holding significant value for intelligent manufacturing and logistics systems.

Most existing assembly accuracy analysis methods focus on rigid assemblies or assume assemblies to be rigid bodies, neglecting the influence of assembly deformation in weak rigid components (WRCs) such as thin-walled structures, cantilever structures, etc. As a result, the assembly accuracy analysis becomes inaccurate, and the accuracy of key components cannot be effectively controlled. This may lead to serious issues such as forced assembly, repair, and rework. To address these problems, this study proposes a rigid–flexible coupling-based assembly accuracy analysis method for WRCs. The stiffness matrix and assembly deformation of WRCs are calculated, and by coupling assembly deformation with other assembly deviations, a rigid–flexible coupling assembly accuracy data model is established. This model incorporates multiple deviation sources, including assembly process variations, design tolerances, and assembly deformations. Assembly deviation transfer modeling and accumulation calculation methods for WRCs are investigated, enabling assembly accuracy simulation and statistical analysis. A case study on WRC assembly accuracy analysis is conducted, and the results demonstrate that the proposed method improves the accuracy of assembly analysis for WRCs, verifying its reliability.

Mobile robots operating in structured indoor environments face significant challenges including the “kidnapped robot” problem, sensor accumulation errors, and environmental perceptual ambiguity, which collectively lead to slow convergence and inadequate accuracy in relocalization. To address these critical issues, this paper proposes an Adaptive Fusion Relocalization Algorithm (AFRA) based on a likelihood-field measurement model. The core innovations of AFRA include the construction of a refined likelihood-field model that effectively integrates LiDAR and Ultra-Wideband (UWB) data, significantly enhancing the accuracy of observation likelihood through probabilistic modeling of hybrid noise. Furthermore, a particle filter framework incorporating dynamic particle scheduling and adaptive resampling mechanisms is developed to achieve an optimal balance between precision and computational efficiency. The Experimental results demonstrate that AFRA maintains relocalization errors within ±0.035 m, improving accuracy by 45.3% compared to the best-performing single sensor, while achieving a 40.7% acceleration in convergence speed. These advancements substantially enhance the robustness and real-time performance of mobile robot localization in complex scenarios.