Neural-network-driven method for optimal path planning via high-accuracy region prediction

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...

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Vydáno v:Artificial life and robotics Ročník 29; číslo 1; s. 12 - 21
Hlavní autoři: Huang, Yuan, Tsao, Cheng-Tien, Shen, Tianyu, Lee, Hee-Hyol
Médium: Journal Article
Jazyk:angličtina
Vydáno: Tokyo Springer Japan 01.02.2024
Springer Nature B.V
Témata:
Ablation
Accuracy
Algorithms
Artificial Intelligence
Computation by Abstract Devices
Computer Science
Control
Efficiency
Heuristic
Mechatronics
Motion planning
Neural networks
Original Article
Path planning
Prediction models
Robotics
Sampling
Semantics
Optimal path planning
Region prediction
Neural networks
Sampling-based algorithm
ISSN:1433-5298, 1614-7456
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Shrnutí: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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ISSN:1433-5298
1614-7456
DOI:10.1007/s10015-023-00915-6