Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA.
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Sensors (Basel). 2019 Mar 28;19(7):1524. doi: 10.3390/s19071524.
This paper develops a decentralized approach to gas distribution mapping (GDM) and information-driven path planning for large-scale distributed sensing systems. Gas mapping is performed using a probabilistic representation known as a Hilbert map, which formulates the mapping problem as a multi-class classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map information fusion method is presented for rapidly merging the information from individual robot maps using limited data communication. A communication strategy that implements data fusion among many robots is also presented for the decentralized computation of GDMs. New entropy-based information-driven path-planning methods are developed and compared to existing approaches, such as particle swarm optimization (PSO) and random walks (RW). Numerical experiments conducted in simulated indoor and outdoor environments show that the information-driven approaches proposed in this paper far outperform other approaches, and avoid mutual collisions in real time.
本文提出了一种用于大规模分布式传感系统的气体分布测绘（GDM）和信息驱动路径规划的分散式方法。气体测绘使用概率表示形式，即希尔伯特地图，将测绘问题表述为多类分类任务，并使用核逻辑回归在线训练判别分类器。提出了一种新颖的希尔伯特地图信息融合方法，用于使用有限的数据通信快速合并来自单个机器人地图的信息。还提出了一种通信策略，用于在许多机器人之间实现 GDM 的分散计算。开发了新的基于熵的信息驱动路径规划方法，并与现有的方法（如粒子群优化（PSO）和随机游走（RW））进行了比较。在模拟室内和室外环境中进行的数值实验表明，本文提出的信息驱动方法远远优于其他方法，并能实时避免相互碰撞。
