# Progress in Construction Robot Path-Planning Algorithms: Review
## 4 Local Path-Planning Algorithms
### Classic Algorithms  
#### 4.2.1 Artificial Potential Field Method (APF)  
In 1986, [PERSON] proposed a path-planning method based on artificial potential fields. The core idea of this method is to consider the target point as an attraction source and obstacles as repulsion sources, thereby creating an artificial potential field similar to an electromagnetic field. As the robot moves within this field, it is simultaneously affected by the combined force of the target's gravitational attraction and the repulsive force from obstacles. This enables the robot to effectively avoid obstacles and move towards the target point [53; 54]. The focus of algorithmic research is on how to use \"attraction\" and \"repulsion\" to guide the robot. The APF algorithm is illustrated in Figure 7.  
APF is an algorithm widely used in the field of autonomous navigation. In the field of construction robotics, it can help robots such as tile-laying robots, floor treatment robots, and on-site logistics robots to effectively avoid obstacles, ensuring their safety during operation. However, APF has several obvious drawbacks. Firstly, it tends to fall into local optimal solutions. Secondly, when the target point is surrounded by obstacles or the distribution of obstacles is uneven, the algorithm may become stuck. Thirdly, in narrow passages and similar situations, APF may cause path oscillation. Construction robots frequently encounter scenarios with these conditions when working indoors. These issues can significantly impact the efficiency and safety of the robots.  
Figure 7: Schematic diagram of APF algorithm.  
[PERSON] proposed an improved repulsive force function to dynamically adjust the direction of the repulsive force, a method that can reduce the failure of path-planning caused by complex obstacle shapes [55].  
[PERSON] proposed incorporating the relative velocity approach into the enhanced APF. This involves the real-time monitoring of dynamic obstacles' motion states, which allows for the adjustment of the robot's path-planning strategies. This innovation increases the efficiency of obstacle avoidance and strengthens the robot's adaptability in intricate environments [56].  
[PERSON] introduced a temperature parameter into the potential field function, which can effectively prevent the robot from being trapped by local minima. This method allows the influence range and direction of the potential field to be dynamically adjusted, thereby improving the flexibility and adaptability of path planning [57].  
[PERSON] addressed the issue of local optima by adding virtual targets and evaluation functions to change the direction and influence range of gravity and resolved the situation of robot deadlock by increasing the gravitational force [58]. [PERSON] employed an adaptive local path-planning based on the Widrow-Hoff rule, which enables robots to maintain a safe distance from obstacles during movement and achieve smooth and collision-free path-planning by limiting the scaling factor of the repulsive potential field. This adaptive algorithm performs well in dynamic environments [59].  
[PERSON] combined deep learning with APF, using a feedback mechanism to continuously optimize the reward function, thereby finding more efficient paths and enhancing the robot's adaptability to changing environments [60]. [PERSON] proposed an improved APF (FV-APF) method, specifically designed for corridor environments, which better adapts to the needs of specific scenarios [61].