# Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model
## 4 Discussion  
It has been demonstrated that the tower crane effectively plans paths using the TC-DWA algorithm across various scenarios, highlighting its adaptability and precision. This section aims to delve deeper into the comparative analysis of the TC-DWA algorithm against traditional path planning methods, specifically the Rapidly Exploring Random Tree (RRT) and the Artificial Potential Field (APF) methods, to establish the ultimate effectiveness of our approach in dynamic environments. The RRT algorithm is a well-known path planning technique that operates by randomly sampling points in the configuration space from the starting point. It constructs a tree by connecting the nearest point that is free of obstacles to each sampled point until it explores the vicinity of the endpoint. While RRT is capable of exploring large spaces and can find paths in complicated environments, it often generates paths that are not optimal or smooth. This can result in operational inefficiencies, particularly in scenarios requiring precise movements, such as tower crane operations. The inherent randomness in path generation can lead to routes that are unnecessarily convoluted, as evidenced by the simulation results presented in Figure 13 The steep and complex paths generated by RRT highlight its limitations in adhering to the operational constraints of tower cranes, such as avoiding workers and maintaining safe distances from obstacles. Figure 12: (**a**) Simulation of TC\(-\)DWA tower crane operation in scenario E1; (**b**) Simulation of TC\(-\)DWA tower crane operation in scenario E2. On the other hand, the APF method represents a dynamic path planning approach that utilizes a potential field to guide movement. Figure 14 displays the APF algorithm's performance in similar scenarios. In this model, the goal point exerts an attractive force, while obstacles produce repulsive forces, directing the movement of the robot along the gradient of the potential field. While this method offers smoother paths, it can fail to respect the physical constraints of tower crane operations. Our simulations indicated that while APF managed to achieve shorter path lengths, it frequently encountered difficulties in avoiding moving obstacles, especially as construction conditions became more complex. The failures observed in the APF method underscore the need for a path planning solution that can dynamically adapt to the changing environment while ensuring safety and efficiency. The comparative analysis of the three methods includes planned path lengths and the number of avoidance failures (instances where the planned route did not maintain a safe distance from obstacles). All results were run 20 times, and the mean (Mean) and standard deviation (SD) are provided. The best results for each experimental scenario are highlighted in bold. From Table 5, it is evident that the traditional static path planning algorithm, RRT, struggles to handle complex and dynamic environments, failing to generate high-quality planned paths and effectively managing moving obstacles. The APF algorithm, as a  
Figure 14: (**a**) Simulation of APF tower crane operation in scenario E1; (**b**) Simulation of APF tower crane operation in scenario E2. Figure 13: (**a**) Simulation of RRT tower crane operation in scenario E1; (**b**) Simulation of RRT tower crane operation in scenario E2. dynamic path planning method, achieved the shortest path lengths in all three scenarios; however, it encountered situations where it could not avoid moving obstacles, and its success rate in obstacle avoidance decreased with increasing complexity of construction conditions. The TC-DWA algorithm proposed in this study, while resulting in longer operational path lengths, successfully avoided obstacles in all scenarios, ensuring worker safety. Due to the variations in construction environments, the increased path lengths fluctuated between 1.36 and 1.44 times compared to the path lengths planned by the APF algorithm.