Integrated Mapping and Path Planning for Very Large-Scale Robotic (VLSR) Systems

Abstract

This paper develops a decentralized approach for mapping and information-driven path planning for Very Large Scale Robotic (VLSR) systems. In this approach, obstacle mapping is performed using a continuous probabilistic representation known as a Hilbert map, which formulates the mapping problem as a binary classification task and uses kernel logistic regression to train a discriminative classifier online. A novel Hilbert map fusion method is presented that quickly and efficiently combines the information from individual robot maps. An integrated mapping and path planning algorithm is presented to determine paths of maximum information value, while simultaneously performing obstacle avoidance. Furthermore, the effect of how percentage communication failure effects the overall performance of the system is investigated. The approach is demonstrated on a VLSR system with hundreds of robots that must map obstacles collaboratively over a large region of interest using onboard range sensors and no prior obstacle information. The results show that, through fusion and decentralized processing, the entropy of the map decreases over time and robot paths remain collision-free.