In order to address issues of opacity in production information and difficulties in collecting equipment data in shipbuilding workshops,a digital twin ship manufacturing workshop monitoring system is designed based on the Unity physics platform.The essential steps in building a virtual reality platform are outlined,encompassing the creation of a virtual ship workshop,the development of data transmission methods for multi-source heterogeneous data acquisition,implementation of data-driven methods for achieving virtual-real synchronization,and enhancement of data visualization capabilities.By practically designing a real-time monitoring system for the welding assembly line production process in shipbuilding,the 3D scene reproduction of the welding assembly line and production monitoring of the digital twin workshop in the virtual scene are achieved.The feasibility of constructing this system is validated through the consistency verification between the virtual and physical workshops.
Taking inspiration from the natural gait transition mechanism of quadrupeds, devising a good gait transition strategy is important for quadruped robots to achieve energy-efficient locomotion on various terrains and velocities. While previous studies have recognized that gait patterns linked to velocities impact two key factors, the Cost of Transport (CoT) and the stability of robot locomotion, only a limited number of studies have effectively combined these factors to design a mechanism that ensures both efficiency and stability in quadruped robot locomotion. In this paper, we propose a multi-gait selection and transition strategy to achieve stable and efficient locomotion across different terrains. Our strategy starts by establishing a gait mapping considering both CoT and locomotion stability to guide the gait selection process during locomotion. Then, we achieve gait switching in time by introducing affine transformations for gait parameters and a designed finite state machine to build the switching order. Comprehensive experiments have been conducted on using our strategy with changing terrains and velocities, and the results indicate that our proposed strategy outperforms baseline methods in achieving simultaneous efficiency in locomotion by considering CoT and stability.
In this paper, a Digital Twin (DT) method is proposed for quickly predicting the stress fields of deep-diving spherical shells based on a simulation database. The DT method can completely cover the stress field distribution of the entire pressure-resistant shell and provide stress predictions for different sizes of deep-diving protection shells. The DT model has a three-level structure. The Level-1 DT maps the Finite Element Model to the digital model, which is verified by experiments. The difference between the experimental and numerical results is less than 9.4%. Then, the Level-2 DT deduces the digital model and expands the simulation database samples. The uncalculated stress field results in the simulation database could be obtained by the local Lagrangian interpolation method. Furthermore, the Level-3 DT can rapidly predict the stress field distribution in deep-diving spherical shell digital models. By comparison with different optimization algorithms, the Particle Swarm Optimization algorithm demonstrates strong optimization capabilities and fast convergence, and the error compared with the simulation results is less than 1%. Finally, combined with actual sensor measurements and historical experience data, this method can be used to carry out real-time mapping and synchronous deduction of the service state of the dynamic stress digital twin.
Cooperative vehicle infrastructure system(CVIS)is one of the advanced solutions to enhance intersection vehicle passage safety.Due to the lack of clear specifications and standards regarding the dynamic timing and transition processes of system object state interaction in existing CVIS technologies,ensuring the safety of passage control logic is challenging.This study utilizes formal language to describe the functional logic of CVIS in unsignalized intersections,verifying the safety of system object state interaction and control logic to improve vehicle passage safety at unsignalized intersections.Simulations are conducted for scenarios including single-vehicle non-conflict,dual-vehicle conflict,and multi-vehicle conflict to identify state interactions and enable transition paths.By integrating tools and requirement specification statements for system safety attribute verification,the reliability and safety of control logic are demonstrated,providing a credible basis for developing high-security architecture for CVIS.
The autonomous control of flippers plays an important role in enhancing the intelligent operation of tracked robots within complex environments. While existing methods mainly rely on hand-crafted control models, in this paper, we introduce a novel approach that leverages deep reinforcement learning (DRL) techniques for autonomous flipper control in complex terrains. Specifically, we propose a new DRL network named AT-D3QN, which ensures safe and smooth flipper control for tracked robots. It comprises two modules, a feature extraction and fusion module for extracting and integrating robot and environment state features, and a deep Q-Learning control generation module for incorporating expert knowledge to obtain a smooth and efficient control strategy. To train the network, a novel reward function is proposed, considering both learning efficiency and passing smoothness. A simulation environment is constructed using the Pymunk physics engine for training. We then directly apply the trained model to a more realistic Gazebo simulation for quantitative analysis. The consistently high performance of the proposed approach validates its superiority over manual teleoperation.
