In this article, leader-following formation control is investigated for nonholonomic mobile robots with inaccurate measurements of global positions and velocities. In many existing results, the leader robot's velocities are assumed to be precisely measured and transmitted to follower robots, and unmodeled parts of the kinematic model caused by inaccurate global position are often ignored. First, an error-based extended state observer (EBESO) for each robot is designed to counteract influence of the inaccurate global positions and velocities in real time. Then, an EBESO-based formation controller without the leader robot's velocities is proposed to guarantee the accurate formation tracking performances. Furthermore, both convergence of the EBESO and stability analysis of the closed-loop error system are provided, respectively. The superiority of the proposed method is verified and illustrated by experiments.
K E Y W O R D Serror-based extended state observer, leader-following formation, multirobot systems, nonholonomic mobile robots
INTRODUCTIONRecently, cooperative control of nonholonomic mobile robots has been an active research field of multiple unmanned vehicle systems. [1][2][3] Compared with a single robot, the cooperation of multiple robots shows some excellent performances, such as high efficiency, adaptability, and robustness. One of the key issues in multirobot systems is the formation control that aims at cooperating a group of robots to form up and maintain a desired geometric structure. Formation control of mobile robots has extensive applications in various fields, such as surveillance, 4 search and rescue operations, 5,6 cooperative transportation, 7,8 and military applications. 9 Many methods have been applied to the formation control of multiple robots, such as the behavior-based method, 10,11 the virtual structure approach, 12,13 the leader-following scheme, [14][15][16] the potential field method, 17 and consensus-based approach. 18,19 No matter which formation control method is adopted, the position information of robots comes from the local positioning (such as onboard cameras [20][21][22] ) or the global positioning (such as ultrawideband (UWB) sensors 23,24 ). It is pointed out that the local position information is precise but not comprehensive whereas the global position information is comprehensive but not accurate. In outdoor or complex situations, the local position information is usually limited, so the global position information is indispensable for formation control, path planning, and obstacle avoidance of robots. Unfortunately, the global positioning deviation is inevitable because robot's barycenter is difficult to be ascertained precisely in reality. Thus, how to control the formation of multiple robots in a high precision is a challenging work because of the global positioning deviation.