A Reinforcement Learning-Based Visual Topology Navigation Method
A technology of reinforcement learning and topology, applied in navigation calculation tools, instruments, calculations, etc., can solve problems such as the sparseness and effectiveness of topological maps, and achieve the effect of improving the navigation range
Active Publication Date: 2022-08-09
XI AN JIAOTONG UNIV
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By integrating the topological map, the navigation range of the mobile robot based on reinforcement learning is effectively improved, the navigation problem in a large-scale environment is solved, and the validity problem after the topological map is sparse is solved, and the use of time series based In this way, the positioning of the mobile robot is corrected, and the accuracy of the positioning of the mobile robot is improved.
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[0030] The following will refer to the appendix Figure 1 to Figure 3 Specific embodiments of the invention are described in more detail. While specific embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present invention will be more thoroughly understood, and will fully convey the scope of the present invention to those skilled in the art.
[0031] It should be noted that certain terms are used in the description and claims to refer to specific components. It should be understood by those skilled in the art that the same component may be referred to by different nouns. The description and the claims do not use the difference in terms as a way to distinguish components, but use the difference in function of the components as a criterion for distinguishing. As referr...
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The present invention discloses a visual topical navigation method based on strengthening learning. In the method, based on the enhanced learning model and sequence images to establish a topology map, given the current position and target position of the mobile robot, based on the topology map search based on the topology map searchMatch the current position and target location of the current position; after positioning the current position and target position, the path planning is performed, and the shortest path of the shortest circuit diameter algorithm is used to reach the shortest path to the target position from the current position;After the node, select a node as a sub -target node to guide the mobile robot to reach the next sub -target point; use the current status image characteristics and the status image characteristics of the child target node as the enhanced learning network input, strengthen the learning network for navigation control, output guidanceThe movement signal of the mobile robot reaches the child target node until the target location is completed.
Description
technical field [0001] The invention belongs to the field of mobile robot visual navigation, in particular to a visual topology navigation method based on reinforcement learning. Background technique [0002] Autonomous navigation is the basic premise for mobile robots to realize environmental exploration, and it is also a hotspot of current research. Reinforcement learning is considered a promising technique for autonomous exploration due to its outstanding action planning ability. Reinforcement learning is a self-evolving type of machine learning. It learns by interacting with the environment and by continuous trial and error, which is closer to the realization of true artificial intelligence. However, since reinforcement learning is limited by its own planning ability, it is powerless for long-distance navigation, so planning algorithms are needed to guide and decompose large-scale navigation tasks into sub-tasks that reinforcement learning can complete. [0003] When c...
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IPC IPC(8): G01C21/20G06V20/62
CPCG01C21/20G06V20/62
Inventor任鹏举张均旺丁焱景鑫赵文哲夏天郑南宁
OwnerXI AN JIAOTONG UNIV