CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning

• URL: http://arxiv.org/abs/2305.10442v1
• Date: Sat, 13 May 2023 20:06:53 GMT
• Title: CBAGAN-RRT: Convolutional Block Attention Generative Adversarial Network for Sampling-Based Path Planning
• Authors: Abhinav Sagar, Sai Teja Gilukara
• Abstract summary: We propose a novel image-based learning algorithm (CBAGAN-RRT) using a Convolutional Block Attention Generative Adversarial Network. 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 citezhang 2021 and demonstrate that our algorithm outperforms the previous state-of-the-art algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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