Multi-robot Cooperative Formation Based on Distributed Model Predictive Control

doi:10.12382/bgxb.2023.0851

Abstract

Abstract:

Multi-robot cooperative system has strong robustness and fault tolerance, which can greatly improve the overall efficiency and complete the complex tasks. At present, the multi-robot formation often adopts a centralized architecture, which relies on the central decision-making module. In particular, there are problems of insufficient scalability and low solvability when dealing with the collaborative tasks of a large number of robots. A distributed model predictive controller (DMPC) based on leader-follower method is proposed to deal with multi-robot cooperative formation tasks. The robot motion and system communication are modeled based on kinematics and graph network. The trajectory tracking and formation keeping tasks in the formation problem are decomposed, and the model predictive controllers are designed for the leader and followers, respectively. A formation matrix is designed and combined with the communication graph network to achieve consensus or formation control. Independent decision-making and parallel computing of each robot show better accuracy and scalability for the collaborative formation of a large number of robots. At the same time, the design of the controller also takes into account the change of control input, which helps to reduce energy consumption. Numerical simulation and scheme comparison are designed, and the effectiveness of the designed control strategy is verified by physical simulation experiment.

Key words: multi-robot system, model predictive control, formation control, trajectory tracking

CLC Number:

TP242.6

LI Caoyan, GUO Zhenchuan, ZHENG Dongdong, WEI Yanling. Multi-robot Cooperative Formation Based on Distributed Model Predictive Control[J]. Acta Armamentarii, 2023, 44(S2): 178-190.

Figures/Tables 18

Fig.1 Kinematics model of robot chassis

Fig.2 Undirected graph and directed graph

Fig.3 Directed communication network of four mobile robots

Fig.4 Consensus trajectory tracking result of multi-robot (linear trajectory)

Fig.5 Trajectory tracking error of leader (linear trajectory)

Fig.6 Consensus tracking errors of the followers (linear trajectory)

Fig.7 Trajectory tracking result of multi-robot formation control (MPC circular trajectory)

Fig.8 Trajectory tracking error of leader (MPC circular trajectory)

Fig.9 Formation errors of the followers (MPC circular trajectory)

Fig.10 MPC formation control input

Fig.11 MPC integrated formation error

Fig.12 Trajectory tracking result of multi-robot formation control (state feedback circular trajectory)

Fig.13 Trajectory tracking error of leader (state feedback circular trajectory)

Fig.14 Formation errors of the followers (state feedback circular trajectory)

Fig.15 Formation control input of state feedback

Fig.16 State feedback integrated formation error

Fig.17 Physical simulation of curve trajectory tracking formation control

Fig.18 Actual trajectories and reference trajectory of the formation control systems

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