未知環境におけるロボット群協調ナビゲーションのためのFFTフィルタリングに基づく高効率観測プロセス
Highly Efficient Observation Process based on FFT Filtering for Robot Swarm Collaborative Navigation in Unknown Environments
FFTフィルタリングを用いて、未知環境でのロボット群の協調経路計画において、安全方向の抽出と観測データの圧縮・融合を高効率に行う手法を提案した。
著者: Chenxi Li, Weining Lu, Zhihao Ma, Litong Meng, Bin Liang
分類: cs.RO
原文アブストラクト
Collaborative path planning for robot swarms in complex, unknown environments without external positioning is a challenging problem. This requires robots to find safe directions based on real-time environmental observations, and to efficiently transfer and fuse these observations within the swarm. This study presents a filtering method based on Fast Fourier Transform (FFT) to address these two issues. We treat sensors' environmental observations as a digital sampling process. Then, we design two different types of filters for safe direction extraction, as well as for the compression and reconstruction of environmental data. The reconstructed data is mapped to probabilistic domain, achieving efficient fusion of swarm observations and planning decision. The computation time is only on the order of microseconds, and the transmission data in communication systems is in bit-level. The performance of our algorithm in sensor data processing was validated in real world experiments, and the effectiveness in swarm path optimization was demonstrated through extensive simulations.
