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Approximate Optimal Motion Planning to Avoid Unknown Moving Avoidance Regions
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DOI：10.1109/TRO.2019.2955321.png)
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摘要
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In this article, an infinite-horizon optimal regulation problem is considered for a control-affine nonlinear autonomous agent subject to input constraints in the presence of dynamic avoidance regions. A local model-based approximate dynamic programming method is implemented to approximate the value function in a local neighborhood of the agent. By performing local approximations, prior knowledge of the locations of avoidance regions is not required. To alleviate the a priori knowledge of the number of avoidance regions in the operating domain, an extension is provided that modifies the value function approximation. The developed feedback-based motion planning strategy guarantees uniformly ultimately bounded convergence of the approximated control policy to the optimal policy while also ensuring the agent remains outside avoidance regions. Simulations are included to demonstrate the preliminary development for a kinematic unicycle and generic nonlinear system. Results from three experiments are also presented to illustrate the performance of the developed method, where a quadcopter achieves approximate optimal regulation while avoiding three mobile obstacles. To demonstrate the developed method, known avoidance regions are used in the first experiment, unknown avoidance regions are used in the second experiment, and an unknown time-varying obstacle directed by a remote pilot is included in the third experiment.
Keyword:
Data-based control
learning and adaptive systems
motion and path planning
neural and fuzzy control
optimization and optimal control
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10.5
论文数:
3.3K
被引数:
2.8W
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