An online local boundary detection and classification algorithm for networked multi-robot systems

Abstract

We present an online boundary classification error detection algorithm to improve accuracy of the original distributed boundary detection algorithm for networked multirobot systems. It is a fully decentralized method based on the geometric approach allowing to suppress boundary errors without recursive process and global synchronization. The accuracy of the ration of correctly identified robots over the total number of robots reaches 100%. We have demonstrated the effectiveness of this boundary detection algorithm in both simulation and real-world environment.