SMMR-Explore: SubMap-based Multi-Robot Exploration System with Multi-robot Multi-target Potential Field Exploration Method

Abstract

Collaborative exploration in an unknown environment without external positioning under limited communication is an essential task for multi-robot applications. For inter-robot positioning, various Distributed Simultaneous Localization and Mapping (DSLAM) systems share the Place Recognition (PR) descriptors and sensor data to estimate the relative pose between robots and merge robots’ maps. As maps are constantly shared among robots in exploration, we design a map-based DSLAM framework, which only shares the submaps, eliminating the transfer of PR descriptors and sensor data. Our framework saves 30% of total communication traffic. For exploration, each robot is assigned to get much unknown information about environments with paying little travel cost. As the number of sampled points increases, the goal would change back and forth among sampled frontiers, leading to the downgrade in exploration efficiency and the overlap of trajectories. We propose an exploration strategy based on Multi-robot Multi-target Potential Field (MMPF), which can eliminate goal’s back-and-forth changes, boosting the exploration efficiency by 1.03 ×∼1.62 × with 3 % ∼ 40 % travel cost saved. Our SubMap-based Multi-robot Exploration method (SMMR-Explore) is evaluated on both Gazebo simulator and real robots. The simulator and the exploration framework are published as an open-source ROS project at https://github.com/efc-robot/SMMR-Explore.