Progress in Construction Robot Path-Planning Algorithms: Review
Source abstract
Abstract
Construction robots are increasingly becoming a significant force in the digital transformation and intelligent upgrading of the construction industry. Path planning is crucial for the advancement of building robot technology. Based on the understanding of construction site information, this paper categorizes path-planning algorithms into two types: global path-planning and local path-planning. Local path planning is further divided into classical algorithms, intelligent algorithms, and reinforcement learning algorithms. Using this classification framework, this paper summarizes the latest research developments in path-planning algorithms, analyzes the advantages and disadvantages of various algorithms, introduces several optimization strategies, and presents the results of these optimizations. Furthermore, common environmental modeling methods, path quality evaluation criteria, commonly used sensors for robots, and the future development of path-planning technologies in swarm-based construction robots are also discussed. Finally, this paper explores future development trends in the field. The aim is to provide references for related research, enhance the path-planning capabilities of construction robots, and promote the intelligent development of the construction industry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Robotic Path Planning Algorithms · Advanced Manufacturing and Logistics Optimization · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100089, China
Funding
National Natural Science Foundation of China General Program · China Postdoctoral Science Foundation Project · Youth Scientific Research and Innovation Special Project
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