This research employs sequential quadratic programming to optimize formation control and trajectory tracking in mobile robot groups.
This paper proposes the application of the Sequential Quadratic Programming (SQP) algorithm to control a group of omnidirectional mobile robots using the Leader-Follower model. The method includes predicting the group’s kinematics, formulating a distributed model predictive control (DMPC) problem with a cost function comprising components for formation maintenance, trajectory tracking, and collision avoidance. The cost function is then optimized in real time using the SQP algorithm. In simulations, Gaussian noise is introduced into the states and control signals to evaluate the method’s effectiveness under real-world deployment conditions.
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Duong et al. (2025) studied this question.
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