# Progress in Construction Robot Path-Planning Algorithms: Review
## 4 Local Path-Planning Algorithms
### Intelligent Algorithm
#### 4.3.3 Ant Colony Optimization (ACO)  
Ant colony Optimization is a heuristic optimization algorithm inspired by the foraging behavior of ants in nature. In nature, ants release pheromones to mark their paths, and when ants find food while walking along a path, they release more pheromones on that path. The concentration of pheromones decreases over time, creating a negative feedback mechanism. When choosing paths, ants tend to select those with higher concentrations of pheromones, thus forming a form of collective intelligence. This mechanism ensures that the algorithm explores new paths and avoids converging prematurely on suboptimal solutions, as shown in Figure 10. This algorithm has been applied in the field of path planning for construction robots. When ants choose paths, they do not simply opt for the one with the highest pheromone concentration; instead, they make choices based on a certain probability. This probability is generally related to both the pheromone concentration and the heuristic information of the path. The Ant Colony Optimization (ACO) algorithm, which simulates evolutionary algorithms, has clear advantages over traditional algorithms, including strong robustness, good global optimization capabilities, efficiency when facing complex problems, good readability, and a wide range of applications. If, in large-scale construction projects, a large number of construction robots need to work collaboratively, the Ant Colony Algorithm can be used to optimize swarm control strategies, achieving efficient task allocation and path-planning and avoiding conflicts between robots. However, it also has some limitations: a lack of effective search strategies in the early stages of search can lead to significant blindness in complex problems; convergence speed is usually slow, especially in larger-scale problems; the algorithm needs multiple iterations to capture optimal solution; and ants can be trapped in local optima easily [80; 81]. Figure 10: Schematic diagram of ACO algorithm mechanism. [PERSON] proposed an improved algorithm aimed at optimizing issues such as low search efficiency, slow convergence speed, and the tendency to fall into local optimal solutions in the Ant Colony Algorithm. By providing unevenly distributed initial pheromones, it avoids the blind search of the Ant Colony Algorithm in its initial stages. By introducing a damping coefficient into the heuristic function, the convergence speed of the algorithm is accelerated. Combining the advantages of the MMAS algorithm model and the elite ant algorithm, the pheromone update rules were improved to prevent the algorithm from converging too early. A pseudo-random state transition rule is used to determine the next path, enhancing the algorithm's global search capability and convergence speed [82]. [PERSON] proposed an innovative method called the Improved Ant Colony Algorithm based on Population Information Entropy (AIACSE). This algorithm describes the diversity of the population during the iteration process through information entropy and constructs a non-uniform initial pheromone distribution to reduce the blindness in the early stages of search; it adopts a non-uniform distribution initialization strategy based on the A algorithm, avoiding the blindness in the evolutionary phase and improving the convergence speed. The adaptive parameter adjustment strategy based on population information entropy automatically adjusts the values of parameters according to the changes in population entropy, maintaining the balance between exploration and exploitation. Through comparative experiments, this improved algorithm performs excellently in terms of convergence speed and success rate [83]. [PERSON] proposed an improved ACO algorithm. First, a directional factor is added to the heuristic function to improve the convergence speed of the algorithm.