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- W4385220744 abstract "Abstract A data-driven predictive motion planner for mobile robots, referred to as LPM-Net, has been proposed in this paper. Conventional predictive motion planners are computationally expensive, often resulting in insufficient throughput on mobile robot hardware. LPM-Net is an imitation learning-assisted local predictive non-holonomic motion planner that is capable of learning from conventional motion planners regarded as paradigm models and replicating their behavior while satisfying the same kinodynamic constraints. In addition, LPM-Net is compatible with GPU and TPU hardware, allowing for faster and more efficient processing. LPM-Net uses convolutional and recurrent long short-term memory (LSTM) deep neural networks to predict steering commands. This has improved computational efficiency which allows autonomous vehicles to be equipped with more cost-effective computers. In the present study, LPM-Net was tuned to mimic the behavior of a model predictive controller (MPC) paradigm model. Measurements in this study demonstrate that the proposed mimic planner achieves acceptable performance while consuming approximately half the processing power of the predictive planner." @default.
- W4385220744 created "2023-07-25" @default.
- W4385220744 creator A5033100639 @default.
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- W4385220744 date "2023-07-24" @default.
- W4385220744 modified "2023-09-27" @default.
- W4385220744 title "LPM-Net: A Data-Driven Resource-Efficient Predictive Motion Planner for Mobile Robots" @default.
- W4385220744 doi "https://doi.org/10.21203/rs.3.rs-3184503/v1" @default.
- W4385220744 hasPublicationYear "2023" @default.
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