Reinforcement learning and slam based approach for mobile robot navigation in unknown environments
ISPRS Workshop Indoor 3D 2019(2019)
摘要
Autonomous navigation of robots in unknown environments from their current position to a desired target without colliding with obstacles represents an important aspect in the field of mobile robots. In literature, traditional methods do exist in case a complete knowledge of the environment is available. However, this is not the case in real-life applications where a complete knowledge about stochastic environments can hardly be obtained where the positions of the obstacles are unknown. Our main goal is to navigate a skid-steering mobile robot (SSMR) with non-holonomic constraints in an unknown environment to its desired target in minimum time while avoiding colliding with the obstacles. In the context of autonomous cart navigation, autonomous navigation of mobile robots in unstructured environments can be formulated as a Reinforcement learning (RL) problem. Reinforcement learning is a paradigm in which …
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