An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like Vehicle
This work investigates the possibility to improve the computational efficiency of a set-based method for the trajectory planning of a car-like vehicle through artificial intelligence. Planning is performed on a graph that represents the operating scenario in which the vehicle moves, and the kinodyna...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/795 |
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| Summary: | This work investigates the possibility to improve the computational efficiency of a set-based method for the trajectory planning of a car-like vehicle through artificial intelligence. Planning is performed on a graph that represents the operating scenario in which the vehicle moves, and the kinodynamic feasibility of the trajectories is guaranteed through a series of set-based arguments, which involve the solution of semi-definite programming problems. Navigation in the graph is performed through a hybrid A* algorithm whose performance metrics are improved through a properly trained classificator, which can forecast whether a candidate trajectory segment is feasible or not. The proposed solution is validated through numerical simulations, with a focus on the effects of different classificators features and by using two different kinds of artificial intelligence: a support vector machine (SVM) and a long-short term memory (LSTM). Results show up to a 28% reduction in computational effort and the importance of lowering the false negative rate in classification for achieving good planning performance outcomes. |
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| ISSN: | 2076-3417 |