Multi-Objective Immune Optimization of Path Planning for Ship Welding Robot

In order to improve the welding efficiency of the ship welding robot, the path planning of the welding robot based on immune optimization is proposed by taking the welding path length and energy loss as the optimization goals. First, on the basis of the definition of the path planning of the welding robot, the grid modeling of the robot’s working environment and the triangular modeling of the welding weldments are carried out. Then, according to the working process of the welding robot, the length objective function, including the welded seam path and the welding torch path without welding, is constructed, and the energy loss function is constructed based on the kinematics and Lagrange function. Finally, the immune optimization algorithm based on cluster analysis and self-circulation is introduced to realize the multi-objective optimization of the path planning for the ship welding robot. The test results of four kinds of ship welding weldments show that compared with the simple genetic algorithm, immune genetic algorithm, ant colony algorithm, artificial bee colony, particle swarm optimization, and immune cloning optimization, the proposed multi-objective immune planning algorithm is the best in terms of planning path length, energy consumption, and stability. Furthermore, the average shortest path and its standard deviation, the average minimum energy consumption and its standard deviation, and the average lowest convergence generation and its standard deviation are reduced by an average of 9.03%, 54.04%, 8.23%, 19.10%, 27.84%, and 52.25%, respectively, which fully verifies the effectiveness and superiority of the proposed welding robot path planning algorithm

Comparison of changes in vegetation and land cover types between Shenzhen and Bangkok

As important node cities in the Belt and Road region, Shenzhen and Bangkok are faced with similar environmental threats posed by the high-speed social development process. Rapid urbanization leads to changes in vegetation growth and land cover types and then affects ecosystem services. In the current study, we used a time-series normalized difference vegetation index dataset from 2000 to 2019 and two land cover type datasets from 2000 to 2018 to investigate and compare the spatiotemporal characteristics of the changes in vegetation and land cover types of the two cities. We found that the trend of vegetation change was mainly affected by the change in land cover types, while the interannual fluctuation of vegetation change was likely related to the extreme climate events caused by El Nino-Southern Oscillation events. However, different urbanization strategies led to opposite vegetation change trends in Bangkok and Shenzhen after 2005. With urbanization, the vegetation coverage (P-v) of Shenzhen increased from 48% in 2000 to 62% in 2018. The total urban green spaces (except croplands) of Shenzhen have remained above 33% of the total area since 2006. However, the total urban green space in Bangkok accounted for only 8% of the total area in 2018, which was even lower than the area percentage of Shenzhen's forests in the same year. Rapid urbanization without adequate urban green spaces caused a decreasing trend ofP(v)in Bangkok. Green development under the Belt and Road Initiative requires serious considerations of environmental quality and urban livability during the rapid urbanization