A Solution to the Problem of Self-Interference Under Multi-Robot Cooperative Exploration with 2D Lidar

Multi-robot cooperative exploration is more efficient in constructing unknown environment maps, compared with single-robot autonomous exploration. However, it is inevitable to identify each other as obstacles in exploration, resulting in self-interference. We propose an anti-self-interference problem framework, aiming at the self-interference problem in multi-robot collaborative exploration. First, the initial position of the dynamic obstacle is judged by the coordinate relationship between the robots; second, considering that there is a certain error in the coordinate system conversion relationship, the DBSCAN algorithm is used to cluster the lidar data, and the ICP algorithm is used to realize the dynamic obstacle object tracking. The anti-self-interference framework proposed in this paper improves the success rate of exploration tasks and the quality of the map by continuously eliminating the lidar point cloud data generated by dynamic obstacles. At the same time, the robot relies on the original lidar data to perform real-time dynamic path planning tasks and achieves accurate construction of static maps under the premise of ensuring the safety of multiple robots. Finally, a large number of simulation results in the gazebo simulation scene confirm that the proposed method is effective.