Search

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]

Due to hot toxic smoke and unknown risks under fire conditions, detection and relevant reconnaissance are significant in avoiding casualties. A fire reconnaissance robot was therefore developed to assist in the problem by offering important fire information to fire fighters. The robot consists of three main systems, a display operating system, video surveillance, and mapping and positioning navigation. Augmented reality (AR) goggle technology with a display operating system was also developed to free fire fighters' hands, which enables them to focus on rescuing processes and not system operation. Considering smoke disturbance, a thermal imaging video surveillance system was included to extract information from the complicated fire conditions. Meanwhile, a simultaneous localization and mapping (SLAM) technology was adopted to build the map, together with the help of a mapping and positioning navigation system. This can provide a real-time map under the rapidly changing fire conditions to guide the fire fighters to the fire sources or the trapped occupants. Based on our experiments, it was found that all the tested system components work quite well under the fire conditions, while the video surveillance system produces clear images under dense smoke and a high-temperature environment; SLAM shows a high accuracy with an average error of less than 3.43%; the positioning accuracy error is 0.31 m; and the maximum error for the navigation system is 3.48%. The developed fire reconnaissance robot can provide a practically important platform to improve fire rescue efficiency to reduce the fire casualties of fire fighters. [ABSTRACT FROM AUTHOR]