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Data-driven Predictive Control for Safe Motion Planning
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AI
DOI：10.1142/S230138502550092X.png)
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Abstract
En 中文
Controlling a constrained dynamic system in an environment with multiple obstacles is important yet challenging. Many existing methods are either heuristic (e.g. A* algorithm) or model-based (e.g. optimal control). In contrast to these methods, this paper addresses scenarios where the mathematical model of the dynamic system is unknown, relying solely on input-output data and environmental information. We propose a new data-driven framework to achieve safe path planning and efficient tracking control by integrating sample-based methods with more recent data-enabled predictive control. In the offline phase, we develop a safe path planning algorithm to generate a sequence of convex safe sets from the initial point to the target set. This is achieved by leveraging a sample-based planning algorithm and solving bi-linear optimization problems. The resulting adjacent safe sets have a nonempty intersection, and the distance between each safe set and any obstacle exceeds the required safe distance. In the online phase, we develop an efficient data-enabled predictive tracking control algorithm with the core of safe set contraction constraints to sequentially track the safe sets. The proposed algorithm transforms the nonconvex obstacle avoidance control problem into a convex optimization problem, which can be solved efficiently. We demonstrate that the proposed framework is safe, efficient, and scalable through quadcopter simulations, comparison simulations, and unmanned vehicle experiments.
Keywords:
Motion planning
data-driven control
model predictive control (MPC)
mobile robot
Journal
IF:
2.4
Papers:
302
Citations:
705