With the continuous expansion of maritime trade scale, ports have put forward increasingly higher requirements for transshipment efficiency. Container twist lock disassembly is a key link in the loading and unloading process, and its automation level has a significant impact on the ship’s berthing time at the port. Aiming at the demand of automated disassembly for high-precision 3D vision, this paper proposes a multi-view point cloud local registration method for twist lock recognition. First, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) is used to extract the keyhole region with the highest overlap in multi-view point clouds, reducing the interference from non-overlapping structures. Then, a two-stage strategy of “coarse registration + fine registration” is adopted: initial alignment is achieved through Random Sample Consensus (RANSAC), and the Iterative Closest Point (ICP) algorithm is improved by combining adaptive distance threshold and normal consistency constraint to complete fine registration. Experimental results show that the proposed method outperforms the global registration scheme in both accuracy and efficiency: the Root Mean Square Error (RMSE) is reduced to 2.15 mm, the Relative Mean Distance (RMD) is reduced to 1.81 mm, and the registration time is approximately 2.41 s. Compared with global registration, the efficiency is improved by 44.2%, which can meet the real-time requirements of continuous operation at automated terminals for the perception link and the time constraints for subsequent manipulator control. The research results preliminarily verify the application potential of this method in the scenario of automated twist lock disassembly.

In automated container terminals, Autonomous Rail-guided Transporters (ARTs) are responsible for horizontal transportation tasks between quay cranes and yard blocks during unloading operations. Their charging strategies directly affect operational continuity, charging resource utilization, and road traffic load. To improve the consistency between the simulation environment and the actual terminal layout, this study constructs a DXF-based directed traffic network based on the CAD/DXF layout of Tianjin Port Second Container Terminal. A coupled discrete-event simulation model integrating ART operations, charging behavior, and traffic dynamics is developed using SimPy. The study further compares several charging strategies, including threshold charging, conventional opportunity charging, safe opportunity charging, interval charging, and Distance-Aware Interval Charging (DAIC). The results indicate that the conventional opportunity charging strategy suffers from battery depletion failures under continuous unloading task flows due to the absence of mandatory low-battery protection. After introducing a safety threshold, the safe opportunity charging strategy effectively eliminates the risk of battery depletion. Considering comprehensive performance indicators, including operational success rate, task completion time, charging travel distance, charging frequency, minimum State of Charge (SOC), and road congestion level, the 40%/70% interval charging strategy demonstrates strong overall robustness, while the 40%/80% interval charging strategy shows advantages in completion time and charging resource utilization. Both strategies can therefore be regarded as key candidate charging schemes for ART operations in Tianjin Port Second Container Terminal.

Large inland waterway infrastructure projects are increasingly exposed to supply disruptions, logistics uncertainty, carbon-control pressure, and dredged-material management challenges. Although resilient supplier selection, closed-loop supply chains, and ESG-oriented optimization have been widely studied, existing models rarely integrate resilient sourcing, hub configuration, forward material supply, reverse dredged-material resourceization, and social externality penalties within a unified maritime infrastructure decision framework. To fill this gap, this study proposes an ESG-endogenous closed-loop supply-chain optimization model for construction of an inland waterway navigation hub. The model jointly optimizes resilient supplier selection, transshipment/resourceization hub activation, equipment deployment, forward material flows, and reverse dredged-material flows. Three objectives are considered: minimizing economic cost, minimizing carbon emissions, and maximizing net social benefit. In particular, a social benefit and ecological-debt penalty function is introduced to quantify the transition from beneficial reuse to disposal-related negative externalities. NSGA-II is adopted as a multi-objective solver, with parameter calibration, convergence analysis, and benchmark comparison used to evaluate computational performance. The Pinglu Canal project is used as a case study. The results produce 14 Pareto-optimal solutions and show that the lowest-cost and lowest-emission configurations may still generate negative social benefits. A low-cost ESG transition region around 197.3–197.8 million CNY is identified, where limited additional investment can activate resourceization pathways and shift the system from ecological debt to near-saturated social benefit. These findings suggest that sustainable infrastructure planning should move beyond isolated cost or carbon minimization and instead identify balanced supplier–hub–equipment–flow configurations that jointly support resilience, circularity, and ESG performance.