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Intelligent construction robots, as the key equipment to implement the intelligent transformation of the construction industry, represent the level of intelligent construction with their autonomous construction capability, and local path planning is the key technology for efficient robot construction. Aiming at the environmental characteristics of the construction site, the difficulties of local path planning for intelligent construction robots are explored. Firstly, classical local path planning algorithms such as artificial potential field, time elasticity band, Bug, dynamic window, etc. are analyzed. Secondly, local path planning algorithms of artificial intelligence such as reinforcement learning, deep reinforcement learning, fuzzy control, swarm intelligence, etc. are summarized. Finally, the limitations of various methods on the application of intelligent construction robots in the construction site environment are analyzed, and the development trend of local path planning algorithms of intelligent construction robots is discussed, aiming to provide certain ideas and suggestions for the research on local path planning of intelligent construction robots in complex construction sites, improve the autonomous construction ability of robots, and promote the development of intelligent construction technology. [ABSTRACT FROM AUTHOR]

This study aims to explore the optimal remote sensing salinization detection index ( SDI) model for the inversion of soil salinization in the Alar reclamation area. Based on Landsat8 OLI remote sensing images and field measured data, this study built the remote sensing SDI models using the salinity index ( SI), the normalized difference vegetation index ( NDVI), the modified soil adjusted vegetation index ( MSAVI), and the surface albedo. Then, using these models, this study extracted the soil salinization information on the Alar reclamation area and verified the model precision. Finally, this study determined the optimal remote sensing - based SDI model through comparative analysis. The results are as follows. The four types of remote sensing - based SDI models SDI1 ( SI - NDVI), SDI2 ( SI - MSAVI), SDI3 ( SI - Albedo), and SDI4 ( Albedo - MSAVI) had general classification precision of 83. 45%, 69. 78%, 53. 23%, and 71. 94%, respectively. Model SDI1 was the most suitable for the inversion of the degree of soil salinization in the Alar reclamation area. Models SDI2 and SDI4 can be utilized as a reference for soil salinization monitoring of the Alar reclamation area. As revealed by the inversion results of the SDI model, the reclamation area is dominated by non - saline and lightly saline soils, with heavily saline soil and saline soil primarily distributed in the northeast and southeast. Model SDI1 established based on SI and NDVI has high accuracy in extracting the soil salinization information of the Alar reclamation area and can be used as the remote sensing - based SDI model for the inversion of soil salinization in reclamation areas. This study can provide an effective technical reference for the control and prevention of soil salinization. [ABSTRACT FROM AUTHOR]