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Sampling-based path planning algorithms suffer from heavy reliance on uniformsampling, which accounts for unreliable and time-consuming performance,especially in complex environments. Recently, neural-network-driven methodspredict regions as sampling domains to realize a non-uniform sampling andreduce calculation time. However, the accuracy of region prediction hindersfurther improvement. We propose a sampling-based algorithm, abbreviated toRegion Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain theoptimal path based on a high-accuracy region prediction. First, we implement aregion prediction neural network (RPNN), to predict accurate regions for theRPNN-RRT*. A full-layer channel-wise attention module is employed to enhancethe 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 MotionPlanning, is established to test the performance in complex environments.Ablation studies and test results show that a high accuracy of 89.13% isachieved by the RPNN for region prediction, compared with other regionprediction models. In addition, the RPNN-RRT* performs in different complexscenarios, demonstrating significant and reliable superiority in terms of thecalculation time, sampling efficiency, and success rate for optimal pathplanning.

Neural-Network-Driven Method for Optimal Path Planning via High-Accuracy Region Prediction