| Title |
Effective Collision Detection Method for Autonomous Crane Path Planning - A Case-Based Simulation - |
| Authors |
Woojoo Lee ; Sangmin Park ; SangHyeok Han ; Sungkon Moon |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2025.26.6.048 |
| Keywords |
Autonomous Crane; Collision Detection; Path Planning; Bounding Volume |
| Abstract |
This study conducts an experimental comparison of collision detection techniques within the path planning process of autonomous cranes. Autonomous cranes are increasingly regarded as essential automated equipment for transporting heavy materials safely and efficiently; however, the risk of collision remains high in the confined and complex environments of construction sites. To address this challenge, three bounding volume methods? AABB (Axis-Aligned Bounding Box), OBB (Oriented Bounding Box), and CBV (Cylindrical Bounding Volume)?were integrated into an A*-based path planning simulation. The simulation was performed 50 times in a 3D environment modeled after a real heavy industrial project in Alberta, Canada, and the performance of each method was evaluated using three indicators: collision avoidance rate, path length, and computation time. Results showed that all three methods achieved a 100% collision avoidance rate, confirming their fundamental safety. Among them, OBB produced the most efficient path lengths, while CBV demonstrated the fastest computation times. These outcomes suggest that CBV provides a balanced alternative between precision and real-time applicability, and that the choice of technique should be strategically aligned with project goals and environmental constraints. Overall, this study offers empirical evidence to guide the selection of collision detection methods in autonomous crane path planning systems, contributing both academically and practically to the field of construction automation. |