Abstract
Sampling-based path planning algorithms suffer from heavy reliance on uniform sampling, which accounts for unreliable and time-consuming performance, especially in complex environments. Recently, neural-network-driven methods predict regions as sampling domains to realize a non-uniform sampling and reduce calculation time. However, the accuracy of region prediction hinders further improvement. We propose a sampling-based algorithm, abbreviated to Region Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain the optimal path based on a high-accuracy region prediction. First, we implement a region prediction neural network (RPNN), to predict accurate regions for the RPNN-RRT*. A full-layer channel-wise attention module is employed to enhance the feature fusion in the concatenation between the encoder and decoder. Moreover, a three-level hierarchy loss is designed to learn the pixel-wise, map-wise, and patch-wise features. A dataset, named Complex Environment Motion Planning, is established to test the performance in complex environments. Ablation studies and test results show that a high accuracy of 89.13% is achieved by the RPNN for region prediction, compared with other region prediction models. In addition, the RPNN-RRT* performs in different complex scenarios, demonstrating significant and reliable superiority in terms of the calculation time, sampling efficiency, and success rate for optimal path planning.
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This work was submitted and accepted for the Journal Track of the joint symposium of the 29th International Symposium on Artificial Life and Robotics, the 9th International Symposium on BioComplexity, and the 7th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Beppu, Oita and Online, January 24–26, 2024).
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Huang, Y., Tsao, CT., Shen, T. et al. Neural-network-driven method for optimal path planning via high-accuracy region prediction. Artif Life Robotics 29, 12–21 (2024). https://doi.org/10.1007/s10015-023-00915-6
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DOI: https://doi.org/10.1007/s10015-023-00915-6