Abstract
This work proposed an improved Gaussian pseudospectral optimization data-driven neural network trajectory planning algorithm to shorten the planning time and improve the speed of obstacle avoidance trajectory. Firstly, the obstacle avoidance trajectory planning optimal control problem is established by deriving the kinematics model of unmanned vehicle and obstacle model. Gaussian pseudospectral optimization is then derived in detail to tackle the infinite time optimal control problem (OCP) and obtain the obstacle avoidance optimization data. By using the neural network training data set, an intelligent planning method for on-line calculation of obstacle avoidance trajectory is designed. Simulation results show that the proposed method can efficiently decrease trajectory planning time.
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Acknowledgements
This work was supported by the National key R&D program (No. 2022YFE0101000).
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Chen, T., Tan, C., Liu, P., Bian, M. (2023). Gaussian Pseudospectral Optimization-Driven Neural Network Planning of Obstacle Avoidance Trajectory. In: Park, J.S., Yang, L.T., Pan, Y., Park, J.H. (eds) Advances in Computer Science and Ubiquitous Computing. CUTECSA 2022. Lecture Notes in Electrical Engineering, vol 1028. Springer, Singapore. https://doi.org/10.1007/978-981-99-1252-0_3
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DOI: https://doi.org/10.1007/978-981-99-1252-0_3
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