Achieving Real-Time Path Planning in Unknown Environments Through Deep Neural Networks

Real-time path planning is crucial for intelligent vehicles to achieve autonomous navigation. In this paper, the authors propose a novel deep neural network (DNN) based method for real-time online path planning in unknown cluttered environments. Firstly, an end-to-end DNN architecture named online three-dimensional path planning network (OTDPP-Net) is designed to learn 3D local path planning policies. It determines actions in 3D space based on multiple value iteration computations approximated by recurrent 2D convolutional neural networks. Moreover, a path planning framework is also developed to realize near-optimal real-time online path planning. The effectiveness of the proposed planner is further improved by a switching scheme, and the path quality is optimized by line-of-sight checks. Both virtual and real-world experimental results demonstrate the remarkable performance of the proposed DNN-based path planner in terms of efficiency, success rate and path quality. Different from existing methods, the computational time and effectiveness of the developed DNN-based path planner are both independent of environmental conditions, which reveals its superiority in large-scale complex environments. A video of the experiments can be found at: https://www.youtube.com/watch?v=gb4nSG4hd6s.

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  • English

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  • Accession Number: 01847640
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 31 2022 9:16AM