Quad-Rotor Path Planning for Cluttered Environments Using Obstacle Clustering
Yoshihide Arai, Masanori Harada, Behcet A. Acikmese · AIAA SCITECH 2023 Forum · 2023
View Video Presentation: https://doi.org/10.2514/6.2023-1634.vid This paper investigates the validity of the path planning algorithm that was developed in a previous study for cluttered environments with obstacle clustering. Using Successive Convexification (SCvx) and compound State-Triggered Constraints (STCs), the path planning of a small unmanned aerial system flying through obstacles is assessed. The obstacles with various radii are placed with uniform distribution along with the flight course to make cluttered environments. Hierarchical clustering is used for obstacle clustering, and minimum bounding circles for each cluster are obtained by convex optimization. Various configurations of the obstacles are used in the path planning computation, and the statistics of computation time and collisions with obstacles are discussed to assess the path planning. The results show that the path planning method with obstacle clustering is effective and reliable for cluttered environments since obstacle clustering can reduce collisions with obstacles and computation loads in dense obstacle regions. For future work, a method that can improve obstacle clustering is proposed.