# Progress in Construction Robot Path-Planning Algorithms: Review
## 7 Conclusions  
The primary goal of the path optimization of construction robot planning is to ensure safety and the second is to improve work efficiency to save cost. This paper reviews the commonly used algorithms in the path-planning of construction robots and collects the current optimization algorithms in the field of various robots, including ground robots, underwater robots, aerial robots, etc. The main optimization directions of the algorithm include search speed, path smoothing, and obstacle avoidance. At the same time, according to the mastery of the construction site information, the path-planning algorithm is divided into two categories: global path-planning and local path-planning. Local path planning can further be divided into the classical algorithm, intelligent algorithm, and reinforcementlearning algorithm. Common environment modeling methods, path quality assessment standards, and common robot sensors are also introduced. Finally, we summarize the various algorithms, analyze their advantages and disadvantages, and look forward to the future development direction. The A* and Dijkstra algorithms represent classic global search techniques. Their fundamental principle involves identifying the minimum estimated cost from the current node to the target destination. By utilizing random sampling, RRT can efficiently establish a path to the target point; however, ensuring path quality remains a challenge. The APF method employs a physical potential field to represent environmental data, assisting in obstacle avoidance and optimal path determination. Nevertheless, in intricate environments, it may easily become trapped in local optima. Drawing inspiration from natural evolutionary principles, the GA serves as a highly effective global search strategy, although its convergence rate is slower than that of alternative algorithms. The DWA integrates the robot's motion model with its environmental representation, utilizing a window model to assess the robot's motion states and evaluate viable trajectories. Both the ACO and PSO algorithms originate from the behavioral patterns observed in biological groups. ACO mimics the foraging behavior of ants searching for food, employing pheromones to direct the search process in order to ultimately find the best solution. Conversely, PSO reflects the foraging dynamics of flocks and schools, adjusting the positions of particles to seek the optimal outcome. However, like ACO, PSO can also fall prey to local optima. Reinforcement learning boasts substantial learning and decision-making prowess, enabling it to devise optimal paths even with limited information; furthermore, it displays considerable adaptability and flexibility within complex and uncertain environments. Construction robot path planning is an important branch of research to improve its autonomy and has attracted the attention of the construction industry in the last decade. Although many path-planning algorithms are applied in practical scenarios, there are still many exploration directions:  
1. Due to the complex and changeable construction environment and the difficulty of construction tasks, the single path-planning algorithm and its improved model, although near-perfect, are still difficult to meet actual engineering needs. Therefore, most of the research directions began to turn to the combined algorithm. Compared with a single algorithm, the combined algorithm can well combine the advantages of each algorithm to make up for the defects of the individual algorithm itself, so as to better complete the construction task. 2. The importance of path-planning technology based on reinforcement learning in the field of path-planning for architectural robots is gradually increasing. Based on the current development situation and the future development needs of the construction industry, the research direction of reinforcement learning technology in the future includes combining it with the traditional path-planning method and applying reinforcement learning to multi-agent collaborative path-planning.