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

arXiv: https://arxiv.org/abs/2305.10442 · HTML: https://mlsanity.com/paper/2305.10442/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 57 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Selective Kernel Networks](https://mlsanity.com/paper/1903.06586/) (2019)
- [Contextual Transformer Networks for Visual Recognition](https://mlsanity.com/paper/2107.12292/) (2021)
- [Rotate to Attend: Convolutional Triplet Attention Module](https://mlsanity.com/paper/2010.03045/) (2020)
- [SA-Net: Shuffle Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/2102.00240/) (2021)
- [Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic](https://mlsanity.com/paper/1404.2334/) (2014)
- [Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks](https://mlsanity.com/paper/1905.09646/) (2019)
- [Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks](https://mlsanity.com/paper/2210.08864/) (2022)
- [Asymptotically near-optimal RRT for fast, high-quality, motion planning](https://mlsanity.com/paper/1308.0189/) (2013)
- [Generative Adversarial Network based Heuristics for Sampling-based Path Planning](https://mlsanity.com/paper/2012.03490/) (2020)
- [Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered Environments](https://mlsanity.com/paper/1807.08325/) (2018)
- [A Survey on the Integration of Machine Learning with Sampling-based Motion Planning](https://mlsanity.com/paper/2211.08368/) (2022)
- [Conditional Generative Adversarial Networks for Optimal Path Planning](https://mlsanity.com/paper/2012.03166/) (2020)
- [AIT* and EIT*: Asymmetric bidirectional sampling-based path planning](https://mlsanity.com/paper/2111.01877/) (2021)
- [Enhance Connectivity of Promising Regions for Sampling-based Path Planning](https://mlsanity.com/paper/2112.08106/) (2021)

## Abstract (arXiv)

Sampling-based path planning algorithms play an important role in autonomous robotics. However, a common problem among these algorithms is that the initial path generated is not optimal, and the convergence is too slow for real-world applications. In this paper, we propose a novel image-based learning algorithm using a Convolutional Block Attention Generative Adversarial Network (CBAGAN-RRT) 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 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. Ablation studies show the effectiveness of various components in our network architecture. The advantage of our approach is that we can avoid the complicated preprocessing in the state space, our model can be generalized to complex 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.