CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning
Sampling-based path planning algorithms play an important role in autonomous robotics. However, a common problem among the RRT-based algorithms is that the initial path generated is not optimal and the convergence is too slow to be used in real-world applications. In this paper, we propose a novel i...
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| Main Authors: | , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
13-05-2023
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Sampling-based path planning algorithms play an important role in autonomous
robotics. However, a common problem among the RRT-based algorithms is that the
initial path generated is not optimal and the convergence is too slow to be
used in real-world applications. In this paper, we propose a novel image-based
learning algorithm (CBAGAN-RRT) using a Convolutional Block Attention
Generative Adversarial Network with a combination of spatial and channel
attention and a novel loss function to design the heuristics, find a better
optimal path, and improve the convergence of the algorithm both concerning time
and speed. The probability distribution of the paths generated from our GAN
model is used to guide the sampling process for the RRT algorithm. We train and
test our network on the dataset generated by \cite{zhang2021generative} and
demonstrate that our algorithm outperforms the previous state-of-the-art
algorithms using both the image quality generation metrics like IOU Score, Dice
Score, FID score, and path planning metrics like time cost and the number of
nodes. We conduct detailed experiments and ablation studies to illustrate the
feasibility of our study and show that our model performs well not only on the
training dataset but also on the unseen test dataset. The advantage of our
approach is that we can avoid the complicated preprocessing in the state space,
our model can be generalized to complicated environments like those containing
turns and narrow passages without loss of accuracy, and our model can be easily
integrated with other sampling-based path planning algorithms. |
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| DOI: | 10.48550/arxiv.2305.10442 |