Peacock Exploration: A Lightweight Exploration for UAV using Control-Efficient Trajectory
This work addresses the challenge of computationally heavy probabilistic path planning and kinodynamic constraints for UAV exploration, which is a problem for researchers and practitioners deploying UAVs in unknown environments.
This paper introduces "Peacock Exploration," a lightweight method for UAVs to explore unknown 3D environments using precomputed minimum snap trajectories. The method achieves O(logN) computational complexity and is demonstrated in a challenging 3D maze environment.
Unmanned Aerial Vehicles have received much attention in recent years due to its wide range of applications, such as exploration of an unknown environment to acquire a 3D map without prior knowledge of it. Existing exploration methods have been largely challenged by computationally heavy probabilistic path planning. Similarly, kinodynamic constraints or proper sensors considering the payload for UAVs were not considered. In this paper, to solve those issues and to consider the limited payload and computational resource of UAVs, we propose "Peacock Exploration": A lightweight exploration method for UAVs using precomputed minimum snap trajectories which look like a peacock's tail. Using the widely known, control efficient minimum snap trajectories and OctoMap, the UAV equipped with a RGB-D camera can explore unknown 3D environments without any prior knowledge or human-guidance with only O(logN) computational complexity. It also adopts the receding horizon approach and simple, heuristic scoring criteria. The proposed algorithm's performance is demonstrated by exploring a challenging 3D maze environment and compared with a state-of-the-art algorithm.