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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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/795 |
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| author | Vito Antonio Nardi Marianna Lanza Filippo Ruffa Valerio Scordamaglia |
| author_facet | Vito Antonio Nardi Marianna Lanza Filippo Ruffa Valerio Scordamaglia |
| author_sort | Vito Antonio Nardi |
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| description | 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. |
| format | Article |
| id | doaj-art-6faa3695607b4b6294aba186d32f5f04 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-6faa3695607b4b6294aba186d32f5f042025-01-24T13:20:51ZengMDPI AGApplied Sciences2076-34172025-01-0115279510.3390/app15020795An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like VehicleVito Antonio Nardi0Marianna Lanza1Filippo Ruffa2Valerio Scordamaglia3DIIES Department, Università degli Studi “Mediterranea” di Reggio Calabria, Via Rodolfo Zehender, 89124 Reggio Calabria, ItalyNeXT: Neurophysiology and Neuro-Engineering of Human-Technology Interaction Research Unit, Campus Bio-Medico University, Via Alvaro del Portillo, 200, 00128 Rome, ItalyDIIES Department, Università degli Studi “Mediterranea” di Reggio Calabria, Via Rodolfo Zehender, 89124 Reggio Calabria, ItalyDIIES Department, Università degli Studi “Mediterranea” di Reggio Calabria, Via Rodolfo Zehender, 89124 Reggio Calabria, ItalyThis 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.https://www.mdpi.com/2076-3417/15/2/795trajectory planningmotion planningcar planninglane changevehicle motionhybrid a-star |
| spellingShingle | Vito Antonio Nardi Marianna Lanza Filippo Ruffa Valerio Scordamaglia An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like Vehicle Applied Sciences trajectory planning motion planning car planning lane change vehicle motion hybrid a-star |
| title | An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like Vehicle |
| title_full | An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like Vehicle |
| title_fullStr | An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like Vehicle |
| title_full_unstemmed | An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like Vehicle |
| title_short | An Artificial Intelligence Approach for the Kinodynamically Feasible Trajectory Planning of a Car-like Vehicle |
| title_sort | artificial intelligence approach for the kinodynamically feasible trajectory planning of a car like vehicle |
| topic | trajectory planning motion planning car planning lane change vehicle motion hybrid a-star |
| url | https://www.mdpi.com/2076-3417/15/2/795 |
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