# Progress in Construction Robot Path-Planning Algorithms: Review
## 1 Introduction
These robots require powerful performance and stability to operate in complex terrains and harsh environments. However, the degree of automation in CRCE is relatively lower compared to CRBE, often relying more on manual control. Common examples of CRCE include excavation robots, concrete spraying robots, and tunnel boring machines. On the other hand, CRBE focuses on indoor precision tasks, such as bricklaying, wall treatment, decoration, and pipe installation. These robots demand high precision and flexibility to adapt to complex building structures and diverse construction needs. As a result, CRBE often achieves fully automated operations. Typical examples include bricklaying robots, wall spraying robots, and 3D printing construction robots. Path planning is crucial for advancing autonomous navigation technology in construction robots and has become a significant topic in academic research. When constructing roads, bridges, and other large structures, CRCE robots often operate in environments with narrow roads, steep slopes, and many curves. Additionally, many remote areas have weak or no mobile communication signals, although the on-site environment remains relatively stable. Therefore, these robots can rely on offline global path-planning algorithms, such as A* and Dijkstra, for autonomous navigation. In contrast, CRBE robots working in residential and office buildings face different room layouts, significant structural variations, and the presence of moving workers. These factors make it challenging to obtain a stable and reliable map in advance. However, since these work sites are usually located near urban areas, mobile communication resources are readily available. As a result, CRBE robots can use sensors to gather real-time environmental information and plan their next moves using local path-planning algorithms. Local path-planning algorithms are further divided into classical and intelligent algorithms based on their principles. Classical algorithms, such as the Artificial Potential Field (APF) and Dynamic Window Approach (DWA), use mathematical models or predefined rules for path planning. These algorithms are suitable for helping construction robots complete simple path-planning tasks. Intelligent algorithms, compared to classical algorithms, integrate technologies related to artificial intelligence and machine learning. These algorithms offer superior learning and adaptability, making them suitable for high-dimensional and complex environments. However, they are more complex to implement and require significant computational resources. Common examples include Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). Alternatively, Deep Reinforcement Learning (DRL) techniques can be employed, enabling robots to learn and adapt to their surroundings autonomously without the need for a pre-defined map. These algorithms, however, demand a large amount of raw data and substantial computational power. Examples of DRL algorithms include Q-learning and Monte Carlo Methods. This article categorizes the surveyed path-planning algorithms, as illustrated in Figure 2, based on the aforementioned rationale. It reviews commonly used algorithms in  
Figure 1: Overview of construction robots. the field of path planning from recent years, analyzes their advantages and disadvantages, and summarizes improvement strategies. Additionally, it provides insights into future trends in building robot path-planning. The aim of this study is to offer a comprehensive reference for related research, enhance the path-planning capabilities of construction robots, and promote the intelligent development of the construction industry. The data sources for this article include the library database of Beijing University of Civil Engineering and Architecture and Google Scholar. The search keywords were: \"algorithm name\", \"robot path-planning\", and \"specific improvement strategies\" (e.g., path quality, obstacle avoidance), with a publication year range of 2018-2024.