Sampling-based Kinodynamic Motion Planning Using a Neural Network Controller
Author:
Affiliation:
1. Georgia Institute of Technology
Publisher
American Institute of Aeronautics and Astronautics
Reference21 articles.
1. Sampling-based algorithms for optimal motion planning
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1. Practical Approach to Mission Autonomy of Multiple Unmanned Air Mobilities;International Journal of Control, Automation and Systems;2024-07-02
2. Real-Time On-the-Fly Motion Planning for Urban Air Mobility via Updating Tree Data of Sampling-Based Algorithms Using Neural Network Inference;Aerospace;2024-01-22
3. Control Inference Neural Network for Motion Planning With Dynamical Systems;IEEE Robotics and Automation Letters;2023-12
4. A Forward Propagation Motion Planning Algorithm Based on Generative Model;2023 IEEE International Conference on Unmanned Systems (ICUS);2023-10-13
5. Parallelized Control-Aware Motion Planning With Learned Controller Proxies;IEEE Robotics and Automation Letters;2023-04
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