Data-driven robust distributed MPC for collision avoidance formation navigation of constrained nonholonomic multi-robot systems

In this work, we consider the robust collision avoidance formation navigation problem for multiple constrained nonholonomic robots with uncertain dynamics. Distributed model predictive control (MPC) based method is proposed in view of its ability to handle the input and state constraints of the robo...

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Vydáno v:2022 4th International Conference on Data-driven Optimization of Complex Systems (DOCS) s. 1 - 5
Hlavní autoři: Fu, Junjie, Wen, Guanghui
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 28.10.2022
Témata:
Adaptation models
Collision avoidance
data-driven learning
formation navigation
input and state constraints
Learning systems
Multi-robot systems
Multiple nonholonomic robots
Navigation
Predictive models
Trajectory
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Shrnutí:In this work, we consider the robust collision avoidance formation navigation problem for multiple constrained nonholonomic robots with uncertain dynamics. Distributed model predictive control (MPC) based method is proposed in view of its ability to handle the input and state constraints of the robots explicitly. A synchronous non-iterative distributed algorithm is employed which reduces the communication requirement of the system. Furthermore, to enable the state trajectory prediction under uncertain robot dynamics, a data-driven online learning method is proposed to generate an accurate model of the nonholonomic robots adaptively. Based on the proposed control strategy, it is shown that robust collision avoidance formation navigation is successfully achieved while the input and state constraints of the robots are satisfied. Simulation examples are given to demonstrate the performance of the data-driven learning method and the distributed MPC based formation navigation controller.
DOI:10.1109/DOCS55193.2022.9967769