Abstract
In this paper, a new method based on deep convolutional neural network (CNN) for path planning of robot is proposed, the aim of which is to transform the mission of path planning into a task of environment classification. Firstly, the images of road are collected from cameras installed as required, and then the comprehensive features are abstracted directly from original images through the CNN. Finally, according to the results of classification, the moving direction of robots is exported. In this way, we build an end-to-end recognition system which maps from raw data to motion behavior of robot. Furthermore, experiment has been provided to demonstrate the performance of the proposed method on different roads.
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Acknowledgements
This work was supported by the Nature Science Foundation of China (Grant Nos. U1608253 and 61473282) and by the Liaoning Provincial Social Planning Found (L15BGL017). The authors would like to thank the reviewers for their insightful comments and suggestions.
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Wu, P., Cao, Y., He, Y., Li, D. (2017). Vision-Based Robot Path Planning with Deep Learning. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_9
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DOI: https://doi.org/10.1007/978-3-319-68345-4_9
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