Vision-based flexible leader-follower formation tracking of multiple nonholonomic mobile robots in unknown obstacle environments

• Main Authors: Wang, Yuanzhe, Shan, Mao, Yue, Yufeng, Wang, Danwei
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2019
• Subjects: Flexible Formation; Formation Control; DRNTU::Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/105337; http://hdl.handle.net/10220/48012; http://dx.doi.org/10.1109/TCST.2019.2892031
• Institution: Nanyang Technological University

This brief investigates the flexible leader-follower formation tracking problem for a group of nonholonomic mobile robots, while most of the formation control related work in the literature focuses on the rigid formation. The flexible formation discussed in this brief is defined in curvilinear coordinates in terms of longitudinal separations between robots along the reference trajectory and lateral deviations with respect to this trajectory. Unlike the previous studies on flexible formation control, this brief is under a more challenging assumption that the global position and orientation measurements are not available. To obtain the relative pose relationships amongst robots, a stereo camera is mounted on each follower. In consideration of the fact that visual observations are noise-corrupted and intermittently available, a particle filter-based relative pose estimation approach is employed to estimate the position and orientation of the leader in the local reference frame of the follower using the polluted and discontinuous information. Also, to form a flexible formation, the leader historical trajectory is reconstructed with respect to the current local frame attached on the follower, based on which a reference point is generated. In addition, this brief considers the situation where robots operate in unknown obstacle environments. To ensure robot safety in such environments, a multiobjective control law is proposed to balance reference tracking and collision avoidance in different situations. Simulation and real-robot experiment have been performed to demonstrate the efficacy of the proposed method.