Classifying Environmental Features From Local Observations of Emergent Swarm Behavior  

作　　者：Megan Emmons Anthony AMaciejewski Charles Anderson Edwin KPChong 

机构地区：[1]IEEE [2]Department of Electrical and Computer Engineering,Colorado State University,Fort Collins,CO 80523 USA [3]Department of Computer Science,Colorado State University,Fort Collins,CO 80523 USA

出　　处：《IEEE/CAA Journal of Automatica Sinica》2020年第3期674-682,共9页自动化学报（英文版）

摘　　要：Robots in a swarm are programmed with individual behaviors but then interactions with the environment and other robots produce more complex, emergent swarm behaviors. One discriminating feature of the emergent behavior is the local distribution of robots in any given region. In this work, we show how local observations of the robot distribution can be correlated to the environment being explored and hence the location of openings or obstructions can be inferred. The correlation is achieved here with a simple, single-layer neural network that generates physically intuitive weights and provides a degree of robustness by allowing for variation in the environment and number of robots in the swarm. The robots are simulated assuming random motion with no communication, a minimalist model in robot sophistication, to explore the viability of cooperative sensing. We culminate our work with a demonstration of how the local distribution of robots in an unknown, office-like environment can be used to locate unobstructed exits.

关 键 词：Biologically inspired ROBOTICS environment EXPLORATION MULTI-AGENT systems SWARM ROBOTICS 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]