# Progress in Construction Robot Path-Planning Algorithms: Review
## 5 Summary  
Architectural robots are now advancing to the forefront of digital transformation and intelligent upgrading in the construction industry. Path planning, as a core element in this technological evolution, has increasingly highlighted its strategic position. Based on detailed considerations of the information richness of construction sites, path-planning algorithms are divided into two major categories: global path-planning and local path-planning. Within this framework, this paper comprehensively reviews the path-planning algorithms applied in recent years and summarizes the advantages and limitations as well as the improvement directions of various algorithms, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Algorithm Type** & **Advantage** & **Limitations** & **Improvement** \\ \hline \multirow{4}{*}{A* Algorithm} & & Efficient: A* can find the shortest path quickly & Memory usage: A* In complex scenarios, it takes up a lot of memory. & Search speed: [23, 24, 26] \\  & & Flexible: A* adapts to different problems by choosing different inspired functions. & Sensitive: A* performance depends on the choice of the heuristic function. & Success rate: [27] \\ \hline \multirow{4}{*}{Dijkstra} & Global Path-planning Algorithm & Simple: the logic of the algorithm is relatively simple and easy to implement. & Computation complexity: the computational complexity of Dijkstra is relatively high. & Path smoothing: [28, 25] \\  & & Optimality: Dijkstra ensures that the shortest path from the start node to the target node is found. & Low speed: Dijkstra search speed is low during run in dynamic environments. & Computational complexity: [29, 34] \\  & & & Identify to process the negative weight weights & Optimality: [28, 32] \\ \cline{1  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Algorithm Type** & **Advantage** & **Limitations** & **Improvement** \\ \hline \hline \multirow{9}{*}{RRT algorithm} & \multicolumn{3}{c}{Efficient: the RRT algorithm can quickly converge to the target. Flexible: RRT algorithms can easily be combined with other algorithms to form a more complex path-planning system. & Uncertainty: the randomness of the RRT algorithm makes it possible to get different results per run. & Stochastic: [36,44,45] \\  & \multirow{3}{*}{Adaptability: the RRT algorithm can adapt to the dynamic environment, that is, the path can be updated in real time when the environment changes.} & Non-optimal path: the paths generated by the RRT algorithm are not necessarily optimal, especially in complex environments. & Inefficiency: [88] \\  & \multirow{3}{*}{Local Path-planning Classic Algorithm} & Fast: APF computational complexity is relatively low, suitable for application scenarios that require rapid response. & Suboptimal: APF falls into local minima. Success rate: APF depends on local information and will become locked in a complex environment, leading to failure of path planning. & Success rate: [56,57,58,60] \\  & \multirow{3}{*}{Algorithm} & Fast: DWA can quickly calculate the direction of the robot’s motion. Flexible: DWA can adapt to different types of robots and variable environments. & Suboptimal: DWA falls into a locally optimal path. & Search speed: [63,65,66] \\  & & Barrier avoidance: DWA can dynamically identify and avoid obstacles. & Low speed: DWA has great computational complexity during operation, especially in a dynamic environment. & Path smoothing: [67] \\ \hline \multirow{9}{*}{GA Algorithm} & Fast: GA can find optimal or approximate optimal solutions to large-scale optimization problems in a short time. & Search efficiency: & \\  & \multirow{3}{*}{Identification GA Algorithm} & Fast: GA may be less efficient in the process of finding solutions, especially when the initial population diversity is insufficient. & Path quality: [69,71] \\  & & Adaptation: GA can deal with problems from different areas. Comprehensive: GA enables an efficient global search.