An improved active SLAM algorithm for multi-robot exploration

For robot exploration that is subject to pose uncertainty, combining multi-robot cooperative exploration strategy and active simultaneous localization and mapping (SLAM) algorithm can efficiently explore the environment while building the map from the observed data. In the paper, the exploration is stated as a constrained optimization problem and a two-phase approach is proposed. In the first phase, robots with low pose uncertainties coordinate with one another to minimize the exploration time while taking into account the uncertainties of the robot poses. Whenever the pose uncertainty of a robot exceeds a pre-defined threshold, the robot switches to the second phase by revisiting previously seen landmarks or meeting other robots. During the exploration/SLAM process, robots switch between the two phases to minimize the exploration time while maintaining the accuracy of robot poses and map. An adaptive strategy is employed to automatically adjust the threshold of the robot pose uncertainty constraints in order to prevent the robots from oscillating between the two phases. To deal with the limited communication problem, rendezvous technique is utilized by allowing robots to temporarily move out of the communication range and rejoin the group later. Simulation results are provided to verify the proposed approach.