# Progress in Construction Robot Path-Planning Algorithms: Review
## 4 Local Path-Planning Algorithms
### Intelligent Algorithm  
#### 4.3.1 Genetic Algorithm (GA)  
A genetic algorithm (GA) is an optimization technique inspired by the principles of natural biological evolution. This algorithm improves the quality of the population, which is equivalent to the quality of problem solutions, by simulating the biological evolution process. The process includes three key steps: selection, crossover, and mutation. GA eliminates less fit individuals and evolves better ones, ultimately converging to the optimal or near-optimal solution. The evolutionary process of GA is illustrated in Figure 8.  
Genetic algorithms possess powerful global search capabilities, effectively exploring the solution space, and are capable of parallel searching. These features make GA algorithms highly efficient in searching. GAs have applications in a variety of problems, such as operations management, multimedia, and more [68]. Genetic algorithms are also extensively used in autonomous robot navigation. For example, in construction sites, multiple material transport robots and structural assembly robots need to cooperate to complete tasks such as hauling and assembly. Genetic algorithms assist in task allocation while helping robots with path planning, enhancing overall efficiency. However, genetic algorithms also have significant drawbacks, such as slow convergence speed, premature convergence, and low precision.  
Figure 8: GA evolution process flowchart.  
[PERSON] proposed an improved genetic algorithm aimed at the problem of mobile robots in unstructured random environments path-planning. This algorithm can accurately detect collisions and reflect the quality of the robot's path. The algorithm can capture optimal paths in dynamic random environments [69].  
[PERSON] proposed an algorithm mixing the improved genetic algorithm with the dynamic window approach, which enhances the dynamic obstacle avoidance capabilities for robots while quickly generating shorter and smoother paths [70]. [PERSON] introduced an innovative approach for mobile robot pathfinding, which is centered around a Multi-Objective Genetic Algorithm (MRPS-MOGA). This method utilizes several advanced techniques, including tournament selection, cyclic crossover, and adaptive bit-string mutation. These strategies work in conjunction to effectively identify the most optimal path for mobile robots. One of the significant advantages of this algorithm is its enhanced performance in situations where the computational requirements for determining the optimal path are minimal, thus making it particularly efficient in real-time applications [71].  
[PERSON] proposed a multi-objective genetic algorithm based on an elitist strategy (ML-RMOEGA) for the path-planning problem of multi-locomotion-mode robots (MLR). The algorithm adopts an elitist strategy to improve the global convergence of genetic algorithms by preserving the best individuals, thereby avoiding premature convergence. To address the premature convergence issue of genetic algorithms, map analysis operators and population diversity expansion operators were proposed. These operators enhance the algorithm's global search capability. By changing the weights of optimization objectives, the multi-objective optimization performance of the algorithm was tested. The results indicate that the MLRMOEGA algorithm can optimize the value of specific objective functions according to the decision-maker's intent. Additionally, it generates relatively balanced paths when considering multiple equal optimization objectives [72].