The article presents methods for constructing an unmanned vehicle (UV) motion path in an environment with obstacles to solve the problem of finding a path for a non-holonomic vehicle in an environment with obstacles. A combined approach is described and individual components are considered that allow solving the problem of planning a path for a non-holonomic vehicle. The practical use example of the combined approach for solving the problem of planning the UV path is given.
The article presents current state of development of autonomous control system prototype for GAZ A65R32. The solutions for mapping and localization, planning, used sensors, as well as the applied methods of computer vision and deep learning and the methods used to transfer data in the system are described.
This article describes the algorithm development for constructing a local trajectory for an unmanned vehicle or for implementation in an ADAS system using the reinforcement learning method. A special part is dedicated to reinforcement learning. One of the methods that is best suitable for the task conditions will also be implemented. This method will allow bypassing obstacles and reaching the specified short target points.
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